NOMA-Based Backscatter Communications: Fundamentals, Applications, and Advancements

Developing wireless communication technologies is an ongoing process to satisfy the requirements of new applications and the increasing proliferation of interconnected devices. Using nonorthogonal multiple access (NOMA) and backscatter communication (BC) has surfaced as an advantageous approach for enhancing energy efficiency (EE), maximizing sum rates, ensuring security, and optimizing resource allocation. NOMA permits multiple users to share time and frequency resources even without the requirement of antenna arrays, whereas BC employs ambient RF signals for low-power communication. By integrating the advantages of NOMA and BC, NOMA-based BC provides a solution for future energy-efficient and low-power networks. Despite its potential, there is a lack of a comprehensive overview of NOMA-BC, necessitating a systematic survey that covers its principles, applications, challenges, and future directions. This survey aims to bridge the gap by exploring NOMA-BC within beyond fifth generation (B5G) and 6G networks. We delve into its technical aspects, performance optimization techniques, and real-world applications to enhance understanding and knowledge. First, we cover topics, such as enhancing EE, maximizing the sum rates, ensuring security, and analyzing performance. Our primary goal is to provide researchers and practitioners with valuable insights that enable them to grasp the capabilities and benefits of NOMA-BC. To achieve this, we comprehensively analyze the performance of various schemes by presenting detailed summary tables. These analyses cover a range of scenarios, methods, and objectives, focusing on emerging B5G technologies, such as reconfigurable intelligent surfaces (RIS), visible light communication (VLC), and unmanned aerial vehicle (UAV) communication. By examining NOMA-BC’s effectiveness within these contexts, we aim to provide a holistic view of its potential and applicability in diverse technological domains. Moreover, our survey identifies and discusses open research challenges and proposes future directions to guide researchers toward unexplored areas and facilitate advancements in NOMA-BC.

R Ecent years have seen tremendous developments in wireless communication networks to meet the needs of cutting-edge and immersive applications and the expanding number of interconnected devices.[1].As we move towards the era of beyond fifth generation (B5G) and anticipate the development of sixth generation (6G) networks, there is a pressing need to address the challenges posed by ultra-high data rates, ultra-low latency, massive connectivity, energy efficiency (EE), and ubiquitous communication [2].Innovative approaches that push the boundaries of existing wireless technologies are essential to overcome these challenges.
In this context, integrating non-orthogonal multiple access (NOMA) and backscattering communication (BC) has emerged as a promising paradigm for B5G and 6G networks.NOMA enables multiple users to share time and frequency resources by leveraging the power domain to overlay signals with varying power levels [3].The base station (BS) in a conventional orthogonal multiple access (OMA) system must independently encode and decode the signals from various backscatter devices (BDs).In a NOMA system, the BS just needs to send out one signal, which the returned signal devices will then decode.As a result, the BS's signal processing is considerably improved.This approach enhances spectral efficiency and facilitates extensive connectivity.On the other hand, BC leverages wireless signal reflection and modulation to enable low-power and cost-effective communication between devices.Integrating NOMA and BC opens up new possibilities for pervasive and power-saving wireless communication, laying the foundation for future wireless networks.
NOMA is pivotal in enabling concurrent service for multiple users by efficiently utilizing shared resources.It encompasses power-domain (PD-NOMA) and code-domain (CD-NOMA).PD-NOMA achieves low latency and high spectral efficiency by leveraging channel gain differences for multiplexing.It uses power domain differentiation to allow transmission at the same time using superposition coding (SC) and successive interference cancellation (SIC) methods [4].PD-NOMA has been thought of in conjunction with orthogonal frequency-division multiple access (OFDMA) in Long-Term Evolution Advanced (LTE-A) networks, which allows for multi-user superposition transmission (MUST) [5].It has also found applications in layered division multiplexing (LDM) for digital TV broadcasting, such as the Advanced Television Systems Committee (ATSC) 3.0 protocol [6].
Similarly, BC offers a highly efficient solution for lowpower communication, allowing devices to exchange data without needing a dedicated power source.By modulating ambient radio frequency (RF) signals, BC achieves remarkable EE and enables innovative solutions [7], [8].Initially used for aircraft identification during World War II [9].BC has evolved to find applications in various domains, including radio frequency identification (RFID) systems for commodity supply chains and electronic toll collection [10], [11].However, BC technology faces challenges such as the complexity of RF signals, limited data rates, transmission range, interference, and security concerns.Despite these challenges, the demand for BC technology keeps rising, necessitating the development of more efficient and scalable solutions.
Combining NOMA principles with backscatter technology, NOMA-based BC is a cutting-edge communication paradigm that offers significant technical advances and opens up various application areas with various technologies.In addition to B5G and 6G IoT networks, NOMA-based BC has applications in the intelligent transportation systems (ITS), health and agricultural sectors, cognitive radio networks (CRNs), industrial automation, cooperative UAV networks, and wireless sensor networks [12].Nevertheless, this innovative method presents obstacles such as interference from multiple users, signal processing complexity, synchronization, security, and resource allocation.Modern interference management techniques, synchronization algorithms, resource allocation strategies, and secure encryption mechanisms are needed to make NOMA-based BC systems more scalable, reliable, and efficient.This is because more and more people need to be able to communicate easily and reliably with each other [13].Provided an overview of the state-of-the-art research and development in AmBC, including various system architectures, modulation schemes, and applications.

IEEE China Communications
Examined a detailed analysis of the AmBC technology, including its working principle, performance characteristics, and potential applications.It also discussed challenges and opportunities and highlighted its potential to enable a new generation of green IoT.[16] 2019 Journal of Communications and Information Networks Contributed a comprehensive survey of the existing literature on BC and highlighted the key research contributions and open research challenges.They also presented a comparison of different BC techniques.[17] 2019 IEEE Network Explored the potential of BC technology, key research challenges, potential solutions to these challenges, and guidelines for designing and optimizing BC systems in healthcare networks.[18] 2019 MDPI Prepared a comprehensive survey of BC technology by including its fundamental principles, historical development, key challenges and opportunities, architectures, modulation and encoding techniques, various applications, current state-of-the-art research and future research directions for new researchers. [19]

EURASIP Journal on Wireless Communications and Networking
Granted an overview of the recent advances in BC systems and their potential applications, discussed other key technical challenges, such as signal processing, channel modeling, energy harvesting, and proposed solutions of these.Also determined application models in biomedical fields and other industrial scenarios. [20]

IETE Technical Review
Introduced AmBC by discussing different ambient RF signals and the energy requirements of passive IoT devices.Moreover, it covers techniques such as frequency shifting, channel estimation, power and information management, scheduling, resource allocation, and performance analysis of AmBC systems.[21] 2020 ITU Journal on Future and Evolving Technologies Covered backscattering fundamentals and its potential IoT applications.Also discussed different types of backscattering systems based on excitation and passive receivers, which form the backscattering tag-to-tag networks.Moreover, it focused on advancements in hardware design, modulation, and passive demodulation.[22] 2020

Intelligent and Converged Networks
Provided a history of IoT and BC along with types of BC, their applications, and the challenges, such as interference, range limitations, and security issues.Furthermore, suggested solutions to backscatter's use in future IoT, such as improvements in EE, data rates, and security.[23] 2020 International Conference on Robots & Intelligent System (ICRIS) Discussed the limitations of traditional communication technologies and proposed cooperative AmBC as an alternative for IoT devices.Highlighted the advantages of AmBC, recent advancements of cooperative AmBC, and compared the performance of both strategies.Research challenges and the future demand for cooperative networks for the future IoT are discussed. [24]

IEEE Open Journal of the Communications Society
Presented a comprehensive survey in the area of wireless-powered networks with BC.Also, the authors covered system architecture, circuit design, modulation techniques, and protocols for wireless-powered backscatter networks.[25] 2021 IEEE Access Focused on the principles and characteristics of non-coherent and BC and discussed their potential advantages regarding EE, scalability, and complexity.It also presented various use cases for ultra-massive connectivity in smart cities, IoT, and smart industries and explored how non-coherent and BC can be applied. [12]

IEEE Wireless Communications
An overview of the RIS fundamentals with BC to enable 6G IoT networks.Presented a detailed analysis, including the design of the RIS, the BC protocol, and the signal processing algorithms.Also provided a case study of RIS-assisted NOMA-based BC. [26] 2020 IEEE IoT Magazine Highlighted the potential of combining NOMA and BC for the development of battery-free IoT networks.Provides use cases of NOMA-BC and case studies for smart farming and healthcare.

NOMA-BC Our Survey
Provided a thorough examination of NOMA and BC, encompassing their fundamental concepts, practical uses, and challenges.This paper presents a comprehensive analysis of performance enhancement techniques for NOMA-BC networks.The evaluation of these techniques is conducted in the context of B5G and 6G technologies.In addition, the open challenges and potential research directions for enhancing BC networks based on NOMA are deliberated.

A. Related Surveys
There has been an increasing focus on BC and its possible uses in the IoT in recent years.Several articles have conducted detailed reviews on BC, its various types, and emerging hybrid techniques, as shown in Table II.However, our comprehensive NOMA-BC survey differs from the listed surveys in Table II.Our goal is to encourage more study of NOMA-BC, which will speed up the advancement of cutting-edge wireless communication systems and unlock NOMA's full potential in BC.For instance, in [14], the authors surveyed ambient BC (AmBC), focusing on its basic concepts, research progress, proposed design techniques, challenges, and open issues.However, it does not focus on combining NOMA with BC.Similarly, in [15], the authors considered AmBC for green IoT communication, highlighting the EE challenges in IoT and discussing the possible solutions for future IoT applications.However, NOMA-based BC has not been focused.Furthermore, [16] investigated the history and overview of BC, differentiating its types and applications and discussing their challenges and future research directions.Besides this, it does not cover the NOMA-enabled BC aspects and their applications.On the other hand, [17] discussed the applications of BC in healthcare and highlighted the open challenges and future advancements in this direction.However, it does not examine the performance of NOMA-BC or its integration with B5G and 6G technologies nor thoroughly compare cutting-edge schemes.Moreover, authors of [18] presented a detailed overview of BC regarding battery-free communication.Their main objective is to explain the advantages of BC to overcome the energy crisis in a limited battery environment of communication.They focus on hardware aspects, such as hardware designs of BC and its types, software-side aspects, such as signal processing, modulation, and demodulation schemes, and multiple access schemes of BC to overcome the practical problems of next-generation wireless communication and engage emerging technologies for future IoT.The effectiveness of NOMA-BC or its integration with B5G and 6G technologies is not evaluated.In another survey, the authors in [19] investigated BC's advancements, technical advantages, and applications regarding future-generation communication.Specifically, they cover next-generation BC's basic principles, designs, and techniques under IoT applications.Nevertheless, its main focus was not NOMA-BC's performance or integration with B5G and 6G technologies.Authors in [20] express a detailed overview of AmBC and different ambient RF signals that provide energy to IoT devices.Their main objective is to elaborate on the RF signal processing, optimization models, power transfer methods, resource management approaches, and open challenges of AmBC.
BC systems have active and passive receivers to receive the surrounding signals.The authors in [21] determined the four different types of receivers regarding the BC system and propose a detailed overview of backscattering systems with passive receivers by considering backscattering tag-totag networks (BTTN).Their main focus is advancements in hardware design, modulation, and passive demodulation for passive tag-to-tag communication.This survey also fails to consider NOMA-BC.The authors of [22] reviewed the innovation of BC and IoT systems with their different types, applications, research, and practical challenges.AmBC is the main power supply technique for IoT devices to reduce power issues during transmission, but the authors did not include the NOMA-BC discussion.In another survey [23], the authors highlighted the advantages of AmBC with a detailed overview and recent advancements of cooperative AmBC.Furthermore, research challenges and future demand for cooperative networks for future IoT are discussed.However, they did not consider the NOMA merger with BC.Authors in [24] provided an overview of state-ofthe-art wireless-powered networks with BCs.The paper surveys the recent research on various aspects of largescale wireless-powered networks with BCs, including energy harvesting, BC protocols, system architectures, channel modeling, and performance analysis.It also highlights the key research challenges and some practical applications.But, it does seek the advancements of NOMA-BC with B5G and 6G.A research work [25] discussed the potential of non-coherent BCs to enable ultra-massive connectivity in 6G wireless networks.The authors argue that traditional coherent communication techniques may not support the massive number of devices expected to be connected in 6G networks.Non-coherent and BC techniques can offer a more energy-efficient and cost-effective approach to supporting ultra-massive connectivity.The paper provides an overview of the principles and potential applications of non-coherent BCs.It highlights key challenges and research directions for realizing these techniques in 6G networks.However, NOMA-BC has not been discussed.
Article [12] provided an overview of a new approach for enabling 6G IoT networks by using reconfigurable intelligent surface (RIS) to enhance BC.The article talks about how RIS, which are flat structures that can reflect and change electromagnetic waves, can improve the performance of BC by increasing the signal-to-noise ratio (SNR) of backscattered signals.They described several potential applications of RIS-assisted BC in smart homes, smart cities, and healthcare networks.The article also talks about the difficulties of using RISs in 6G IoT networks, like creating effective RIS control algorithms, figuring out how to use power most efficiently, and dealing with security issues.However, the paper didn't focus on NOMA-BC's principles, performance, or integration with B5G and 6G technologies.
In 2020, the authors of [26] looked into how NOMA techniques could be combined with BC for IoT.They showed how NOMA-enabled backscatter could be used to connect battery-free IoT devices efficiently.The study classifies and researches problems in building big IoT networks with NOMA-enabled BC.However, it does not delve into the performance of NOMA-BC or its merger with B5G and 6G technologies, nor does it comprehensively compare innovative schemes in this context.

B. Motivation and Contribution
This study seeks to fill the knowledge gap and contribute substantially to the field by addressing the need for thorough research on NOMA-based BC.In contrast to earlier research, our study looks closely at NOMA-based BC, as shown in taxonomy Fig. 1.Our study bridges this information gap, which helps enhance wireless communication systems, especially in B5G and 6G networks.The main goal of our survey is to motivate researchers and practitioners to learn more about NOMA-BC and realize its full potential for advancing wireless communication.We are convinced that researchers and practitioners can find novel solutions and accelerate the development of wireless communication systems in the B5G and 6G eras by fully grasping the fundamental principles, investigating applications, identifying challenges, and imagining future directions for NOMA-based BC.
Our survey offers several key contributions: ‚ We provide an in-depth study of NOMA-based BC, offering a deep understanding of its fundamental principles, applications, challenges, and future directions within B5G and 6G networks.By exploring the synergistic potential of NOMA and BC, we address these advanced wireless networks' unique requirements and demands.‚ Our survey delves into the technical aspects of NOMA-BC, examining advanced techniques to optimize per- The subsequent sections of this survey are organized as follows: Section II provides an introduction to NOMA, including an overview of its principles, different types, and standardization efforts.It also examines the applications and challenges associated with NOMA.Section III focuses on BC fundamentals.It presents an overview of BC, its various types, applications, and challenges.In Section IV, we delve into advanced techniques for optimizing the performance and efficiency of NOMA-BC.This section explores strategies for enhancing EE, maximizing sum rates, and ensuring security.Section V evaluates and analyzes performance in the context of NOMA-BC systems.This section offers detailed performance analysis and investigates how cutting-edge B5G technologies like RIS, MTC, VLC, CRN, and UAV networks interact with NOMA-BC.Section VI highlights open research issues and future research opportunities in NOMA-BC, identifying areas that require further exploration and providing a roadmap for future investigations.Finally, Section VII concludes the NOMA-BC survey.

II. NOMA PRELIMINARIES
A. Overview and Types of NOMA 1) Overview: As a multiple access technique in wireless communication networks, NOMA enables multiple users to share the same time and frequency resources to transmit their data simultaneously.Compared to OMA, NOMA is more energy-efficient and can handle more users in the same time and frequency resources.It also has superior spectral efficiency [27].To accommodate many users, NOMA divides the available frequency spectrum into sub-bands or subcarriers and allocates various power levels or code sequences to each sub-band.Due to the cancellation of the interference from the stronger users, users with good channel conditions can decode their signals first, while users with poor channel conditions can decode their messages later.
NOMA offers significant advantages over traditional multiple access techniques like time division multiple access (TDMA), frequency division multiple access (FDMA), and code division multiple access (CDMA) [28].TDMA and FDMA allocate separate time or frequency slots to each user, while CDMA assigns unique codes.Still, all three techniques have network capacity and efficiency limitations.In contrast, NOMA allows numerous users to share the same resources simultaneously, improving network efficiency and capacity.It is flexible and can adapt to the changing requirements of the network by adjusting the power levels or code sequences assigned to each user [29].NOMA had a high impact in the context of B5G and 6G technologies to enhance the spectrum efficiency and capacity of users [30].Handling the massive number of devices increases the spectrum efficiency, throughput, and user fairness and reduces interference by applying SIC [31].NOMA supports various applications, including high-speed data transmission, machine-to-machine (M2M) communication, and IoT devices.
2) NOMA Types: An overview of two techniques in NOMA is presented, i.e., PD-NOMA and CD-NOMA.
PD-NOMA: In this type, users are separated based on their assigned power levels.This type separates users based on their allocated levels of power [5].The transmitter superimposes the signals of various power levels, and the receiver uses the SIC method to separate them.The first signal that SIC decodes is one with the highest power level.The decoded signal is subtracted from the received signal after decoding, leaving only the signals of the remaining users.[32].The receiver then decodes the user whose signal has the second-highest power level before repeating the procedure until all user signals have been decoded.For example, if three users have power levels P1, P2, and P3, and P 1 ą P 2 ą P 3, the receiver will first decode the signal from the user with power level P1 before subtracting it from the received signal.The signal received by the receiver is decoded using power level P2, which is obtained by subtracting P1 from the received signal and then subtracting it from the remaining signal.After subtracting P1 and P2, the recipient uses power level P3 to decode the signal the user transmitted.
CD-NOMA: In this type, users are separated based on unique code sequences assigned to them.Each user is assigned a unique code sequence orthogonal to other users' code sequences [33].The signals from different users are superimposed at the transmitter, and the receiver separates them using the multi-user detection (MUD) technique.In MUD, the receiver picks up signals from all users at once and uses the idea of interference rejection combining (IRC) to decode the signals from various users by removing the interference that other users have caused, [34].For example, if there are three users with code sequences C1, C2, and C3, the receiver detects the superimposed signal of all three users.The receiver then uses IRC to cut through the interference from other users' signals and decode each user's signal individually.A comparison of PD-NOMA and CD-NOMA is shown in Table III.

B. NOMA Applications
NOMA has emerged as a prominent technique in conjunction with 5G and 6G technologies, aiming to significantly enhance spectrum efficiency [35].Currently, hybrid approaches within communication systems have gained considerable traction in various industries, enabling reliable and energy-efficient communication.NOMA's integration with established techniques has paved the way for flexible communication, resulting in improved performance.As shown in Fig. 2, NOMA applications with different technologies are given as: 1) Future Wireless Networks: NOMA is a key technology for next-generation wireless communication systems, such as 5G and beyond [36].It improves spectral efficiency by enabling several users to share the same time-frequency resources.Contrary to conventional orthogonal multiple access protocols like OFDMA, NOMA enables non-orthogonal user occupancy of the same resource block.As a result, there are more supported users due to the more effective utilization of the available spectrum.By offering a dependable and minimal-latency communication channel for vital applications like self-driving vehicles and robotic surgery, NOMA may be utilized to enhance URLLC.
2) Massive Machine-Type Communication (mMTC): mMTC refers to the communication between a massive number of low-power devices in cellular networks, often in the order of millions or even billions under next-generation services [37].These devices, known as IoT devices, require connectivity for smart homes, smart cities, automotive industry 4.0 and 5.0 applications, industrial automation, and environmental monitoring.By deploying the NOMA technique, resources may be efficiently distributed to many devices, regardless of their varying power levels.
3) NOMA-Enhanced Unmanned Aerial Vehicle (UAV): NOMA technology adopted to improve the communication performance of UAVs to enhance the connectivity, localization, efficiency, capacity, and reliability of UAVs communication links in wireless networks [38].NOMA allows multiple UAVs to share the same time-frequency resources by allocating different power levels or codebooks to individual UAVs.NOMA-enhanced UAV communication can exploit multi-connectivity capabilities, where UAVs can establish simultaneous connections with multiple BSs, access points, or sensors.This improves spectral efficiency and enables more UAVs to operate within the same frequency band, increasing the overall system capacity.
4) NOMA-based Robotics Communication: Robotics and machine learning are developing quickly, and they are becoming increasingly important in various industries, such as smart homes and industrial automation.Integrating robots into wireless networks as connected users is a promising direction, allowing them to play a crucial role in human society [39].Many robotic devices, including robots and drones, are included in the NOMA-based robotics communication system and a centralized communication infrastructure.The central infrastructure can be a BS or an access point that coordinates the communication among the robotic devices.The central infrastructure allocates the available resources to the robotic devices using NOMA principles [40].This involves assigning power levels or codebooks to individual robots based on channel conditions.

5) NOMA-based Healthcare Systems Communication:
There is an increasing need for highly effective communication networks to create seamless interactions between patients and hospitals due to the rising healthcare needs of individuals.This is particularly vital in scenarios such as real-time telemedicine and telesurgery, which are crucial for emergencies and remote rural areas where immediate medical assistance may be limited [41].NOMA can enable efficient and reliable communication between wearable health monitoring devices and healthcare providers.It allows multiple devices to share the same communication resources, facilitating real-time transmission of patient data such as vital signs, activity levels, and medication adherence [42].NOMA's increased capacity and spectral efficiency support continuous remote monitoring of patients, leading to timely interventions and improved healthcare outcomes.6) Mixed-Mode Networks: NOMA can be used in heterogeneous networks (HetNets) where different devices and technologies may require different resources.HetNets often involve the coexistence of communication technologies, such as cellular networks, Wi-Fi, and IoT devices [43].NOMA allows these technologies to operate in the same frequency band by employing non-orthogonal resource allocation techniques.This enables seamless integration and coexistence of multiple technologies.It enables efficient resource allocation and interference management in these networks, improving overall system performance and user experience.

III. BC FUNDAMENTALS
A. Overview and Types of BC 1) Overview: Billions of wireless sensors connect through IoT systems to enhance the smartness of the network.In different smart IoT applications, such as smart homes, smart cities, smart industries, and smart hospitals, many devices are connected within different environments [44].It is a big challenge to keep all the sensors alive at all times for smart communication.Some energy solutions were proposed, i.e., energy harvesting from ambient sources [45] and wireless power transfer (WPT) [46].Moving towards battery-free IoT systems, a key energy solution is BC, in which the device sends the information by reflecting and modulating the radio frequency wave, which is already available in the air [47].The Backscatter transmitter, also called a tag or backscatter tag, reflects the incoming RF signal without generating RF signals by itself, and then the receiver receives that signal and extracts the information [48].A tag is a passive node that harvests the energy from the previously generated RF signals [24].Currently, several improvements to BC methods have been proposed.
Owing to the BC system's high level of EE and durability.The majority of wireless communication networks use the backscatter method.
2) BC Types: The main types of BC are briefed as follows: ‚ Monostatic BC: A monostatic BC system employs a single antenna for transmitting and receiving signals, facilitating communication between the reader and the backscatter tag.The system emits a continuous wave (CW) signal from the reader, which the tag modulates through alternating reflection and absorption.This modulation technique is used to transfer data.The reader receives the modulated signal from the tag using the same antenna [49].Monostatic backscatter transmission is commonly used in systems such as RFID due to its simple hardware construction.‚ Bistatic BC: It uses separate antennas for sending and receiving signals.These antennas are placed between the reader and the backscatter tag [16].The current configuration involves using a tag antenna responsible for the signal's modulation or reflection to facilitate data transmission.On the other hand, the reader's antenna generates a persistent wave signal.Subsequently, the reader antenna receives the signal reflected for further processing.Bistatic BC offers advantages such as extended coverage distance, enhanced data transfer rates, and reduced signal disruption.This technology is commonly utilized in various systems, such as radar, communications, and long-range wireless.‚ AmBC: It utilizes ambient RF for power supply and communication transmission.According to [22], the backscatter tag operates by utilizing the ambient radio frequencies present in its environment to reflect and .This setup allows efficient sharing of resources, energy, and spectrum, significantly reducing power usage [51].Moreover, SR's collaborative operation between the primary and secondary systems on both transmitting and receiving ends helps mitigate interference and ensure reliable BC through joint decoding at the SRr, enhancing the overall communication efficiency.Table IV compares the communication configuration of these types.

B. BC Applications
In wireless networks, BC is a new technique attracting interest from both industry and academia.The new aspect of this method is exploiting RF already in the area for communication with ultra-low-power devices.Comparing these devices to conventional communication systems, they have a basic design, are more affordable, and use less energy.BC has numerous applications in various fields, as shown in Fig. 3.The principal uses of BC are as follows: 1) IoT Applications: In IoT applications, BC is frequently utilized.BC allows IoT devices with little computation and power to send data by modifying and reflecting existing RF waves.This makes it possible for various IoT devices, including sensors, tags, and wearables, to connect in an economical and energy-efficient manner [52].
2) RFID Systems: BC is used in RFID systems.RFID tags and RFID readers use backscatter methods to communicate [14].Such tags enable operations like inventory management, asset monitoring, and access control by reflecting the RF signals sent by the reader to send their identifying information.
3) Wireless Sensors and Environmental Monitoring: Wireless applications for monitoring and sensing can use BC.Backscatter methods can be used by sensors or monitoring devices to wirelessly communicate data without the need for a separate power supply or complicated communication hardware [53].As a result, BC is appropriate for use in smart homes, agriculture, environmental monitoring, and structural health monitoring systems.
4) Green Transportation: With many manufacturers implementing sensors to guarantee the optimum performance of vehicles, BC is quickly becoming an important technique in the automotive industry.For instance, cars have employed inertia sensors for safety, air pressure sensors for tires, and proximity sensors to prevent collisions [54].These sensors gather data and send it to the main system for processing and the system continuously sends data to the roadside units for smart transportation to control the traffic and accidents.
5) Logistics and Industries: Industry-specific backscatterenabled tags track the movements and conditions of valuable assets, improving utilization and lowering losses.These sensors, which are integrated into packaging, identify risks associated with transit.They can be installed on essential equipment parts, allowing for real-time monitoring, avoiding malfunctions, and increasing productivity [54].Datadriven scheduling empowers predictive maintenance while minimizing disruption and maximizing productivity.6) Healthcare Applications: Wearable sensors with backscatter capabilities may gather information on a patient's vital signs, activity level, and other health parameters [17].Patients who are not hospitalized can also be monitored using BC.For instance, backscatter-enabled wearable sensors can be used by patients with persistent illnesses like diabetes or heart disease to gather information on their health parameters.A doctor or a different medical professional can receive this data for follow-up and evaluation.The authors present the X-Tandem, which is a BC system.They list some potential uses for X-Tandem's indoor monitoring and onbody sensor tags [55].

IV. ADVANCED TECHNIQUES FOR OPTIMIZING
NOMA-BC NETWORKS EFFICIENCY This section offers a comprehensive analysis of advanced techniques that optimize the performance and efficiency of NOMA-BC systems.These subsections delve into various NOMA-BC technology aspects, such as energy-efficient schemes, secure solutions, and sum rate maximization schemes.In addition, the section explores sum rate maximization in the context of uplink and downlink scenarios by highlighting the challenges, opportunities, and benefits of NOMA-BC integration with cutting-edge technologies.

A. Enhancing EE in NOMA-BC Systems
This subsection delves into different methodologies for improving EE in NOMA-based BC systems.This subsection presents various studies that suggest diverse techniques and methods to minimize power consumption and improve EE.Iterative algorithms, quadratic transformation techniques, and joint optimization of parameters like transmit power and reflection coefficients (RCs) are some of the methods used.The studies aim to tackle various challenges in the field, such as mitigating multi-cell interference, addressing imperfect decoding, and managing channel state information (CSI) by maintaining QoS.A detailed summary is provided in Table V to provide readers with a deeper understanding of various schemes used to achieve EE and system performance.
Energy consumption is a challenging issue in the communication field, and Xu et al. [56] considered the energyefficient resource allocation problem in NOMA to increase the efficiency of the system.By considering QoS, the authors proposed a method to enhance the efficiency of the NOMAbased BC system.The study focuses on optimizing the RC of BNs and transmit power of the BS using Dinkelbach's method and the quadratic transformation technique to enhance the EE.According to an investigation into the results, the proposed scheme's performance is far better than the baseline approaches in terms of EE.Similarly, in another work, El Hassani et al. [57] assessed the EE of the NOMA-enhanced BC system by considering the data rate and the overall energy usage.By considering the ideal RC, they constructed the EE maximization problem as a nonconvex problem and obtained a closed-form solution.The authors increase efficiency by reducing Dinkelbach's algorithm's complexity by finding a solution to the optimization problem.Evaluation results showed that the BNs enhance the EE of the NOMA systems and get 45 percent relative gains compared to the OMA system.
Unlike the works in [56] and [57], which have considered a single cell with two users model under perfect SIC decoding at the receiver side, Ahmed et al. [58] introduced a multi- Enhance the EE by optimizing the source's transmit power, the PAC, and the relay node power.
DL-Downlink, 1A-Single Antenna, 2U-Two Users, Co-cooperative, QC-LDPC-Quasi-cyclic Low-density parity-check, SPA-Sum-product algorithm cell model with various sizes and transmitting power of the cells under imperfect SIC decoding.In this model, the system's performance faces degradation due to cell interference with each other and decoding errors due to imperfect SIC decoding.The authors proposed an optimization technique to address this issue to enhance EE and decrease inter-cell interference.The authors first adopted the Dinkelback method to maximize the network's EE and convert the fractional form into a subtractive one.Then, the problem is divided into sub-problems, and closed-form solutions are extracted using Karush-Kuhn-Tucker (KKT) conditions and dual methods.This approach allowed the authors to optimize the system's EE while minimizing inter-cell interference, thus improving the overall performance of the multi-cell NOMA-based BC system under imperfect SIC decoding.Similarly, another work [59] considered imperfect SIC decoding and proposed a hybrid resource allocation technique to maximize the total EE in IoVs by enabling the NOMA-based BC system.The IoVs connect low-power devices with 6G technologies, enabling EE and fast communication to create an intelligent transportation system.The authors deployed multiple RSUs and backscatter tags transmitting signals toward IoVs.They created a joint optimization problem that is split into two sub-problems to find the best solution.The sub-problems look at the total power of RSU, the reflection power of tags, and the power allocation coefficient of IoVs.Dual theory, KKT conditions and Lagrangian dual variables were calculated to solve the problems efficiently.
AmBC, NOMA, and UAV are the high-potential techniques in future IoT networks to connect billions of devices with low power consumption.Hybrid techniques like NOMA-based BC, NOMA-aided UAVs, and NOMA-aided AmBC were also investigated.Most of the works included NOMA-based AmBC with the single-cell model, and these are convex/concave optimization problems due to the absence of cell interference with each other.Different from the aforementioned works [56], [57], and [58], Aljubayrin et al. [60] investigated a new resource allocation model by merging the techniques of NOMA-based AmBC for UAV networks with a multi-cell scenario to achieve EE under imperfect CSI.Due to multi-cell interference, this problem is non-convex, and its complexity is high.To reduce the complexity, the authors convert this problem into a convex problem and then find the optimal solution by applying the sub-gradient method.The proposed technique was compared with a conventional NOMA-based UAV network, which does not include AmBC.
Automotive industries are adopting the emerging 6G technologies for smart, intelligent, energy-efficient, and fast data communication between roadside sensors, different vehicles, and traffic control systems in IoV networks.To deal with the networks' energy and spectrum efficiency, the NOMAbased technique is the potential emerging paradigm for 6G communications.In contrast to the studies presented in [56], [57], and [60], Khan et al. [61] took into account imperfect SIC in multicell IoV networks for automotive industry 5.0 to achieve green transportation by considering NOMA-enabled BC.To maximize the EE of the vehicular networks, the authors came up with a single-carrier and multi-cell-based optimization model where the RC of the BD and transmit power of the roadside unit were jointly optimized and then derived the non-convex problem.The formulated non-convex problem is divided into two subproblems, and the optimal solution is found using KKT conditions and the dual theory method.
This paper [62] suggested an optimization approach for improving cooperative NOMA systems' EE and performance with backscatter technology.It tackles the dearth of thorough optimization frameworks considering imperfect SIC decoding, QoS, power budget, and cooperative constraints.The framework uses QC-LDPC codes to introduce a joint channel coding strategy while optimizing transmit power, PAC, and relay power.Decoupling the problem into subproblems to find the optimal solution by deploying an alternating optimization algorithm to tackle it effectively.Simulation results demonstrate the suggested system's higher EE and error-correction capabilities.

B. Maximizing Sum Rates in NOMA-BC Networks
This section investigates methods designed to optimize the sum rates in NOMA-BC networks.The presented studies suggest different techniques for improving resource alloca-tion and network performance in various scenarios, such as wireless-powered IoT networks, upcoming 6G technologies, cooperative systems, UAV networks, and symbiotic radio (SR) AmBC systems.The optimization techniques simultaneously optimize critical parameters, including power allocation and RCs, to enhance reliability and system capacity.These techniques aim to improve the performance of NOMA-BC networks across various communication environments by increasing throughput and maximizing sum rates.Summary Tables VI and VII provide sum rates for uplink and downlink scenarios, respectively.These tables aid readers in comprehending the NOMA-based BC approaches effectively.
1) Maximizing Sum Rate and Throughput for Uplink Scenarios: The major flaw of the monostatic backscatter configuration is the near-far issue causing round-trip path loss.This weakness is then turned into an advantage for the BC system by employing NOMA.Guo et al. [63] proposed NOMA-based BC in which one reader communicates to numerous BNs.First, the reader divided multiple BNs into two groups using the feedback signal during the training phase and then implemented NOMA by pairing the BNs from these two groups.In particular, different RCs are suggested to tell the difference between the reflected signal powers of the paired BNs and come up with a mathematical expression for figuring out the success rate.The simulation results validated that the reader transmits power and has little impact on the system's performance when the channel condition is less severe.Instead, choosing the RCs for the BNs' groups can significantly enhance the BC system.
BC with NOMA is a promising technique to overcome energy-related challenges in future IoT systems.For example, in future IoTs, 5G and 6G systems are considered lowcost and low-power consumption devices.To enhance the abilities of the BC, Guo et al. [64] leveraged NOMA with hybrid PD-NOMA and TDMA.The authors made different groups of BNs based on their backscatter power level and proposed a novel criterion for selecting the RC between these groups.In the practical example, they analyzed and investigated the impact of the BC model in two different scenarios, i.e., with fading and fading-free, with two BN pairings.Their numerical results showed that NOMA in BC enhanced the performance gain compared to the other conventional models.
Conventional BC systems have limited coverage due to co-located receivers and carrier transmitters.Yang et al.
[65] considered a NOMA-enhanced bistatic BC system with a separate carrier transmitter and backscatter receiver to increase the system's coverage.The authors maximize the minimum throughput by using optimization techniques and considering different constraints, such as backscatter time, energy constraints, RCs, etc.An iterative algorithm was proposed by manipulating optimization techniques and BCD to calculate the suboptimal solutions against the composed non-convex problem.After examining the numerical results, the authors revealed that the suggested model had the best throughput gain compared to other benchmark models.
NOMA and BC are the two leading energy-efficient technologies that empower low-power transmission for green IoT.Ardakani et al. [66] considered multicarrier NOMAassisted BC for IoT devices and framed a non-convex problem.The authors proposed a paradigmatic strategy using an optimal technique with a monotonic structure to select the optimal RC.An algorithm was designed based on outer polyblock approximation for better performance.Using a successive convex approximation algorithm, the authors divided an optimal solution into suboptimal solutions in polynomial time.Analytical results showed that the suboptimal solution is much closer to the best solution than the optimal solution, and multi-carrier NOMA has a much better data rate than the OMA technique.
Zhou et al. [67] considered the wirelessly powered WP-NOMA-assisted BC for an IoT network in which one power beacon (PB), many IoT devices, and one information receiver (IR) are included.IoT devices harvest the energy from the PB signal and backscatter that signal to transfer the information from devices to IR.An optimization problem was calculated in terms of max-min throughput to get better throughput performance and consider the time resource of BC for uplinking NOMA.The optimization problem covered PB's RC, transmit power, and uplink NOMA.Finally, the author formulated an iterative algorithm to find the optimal solution in terms of resource allocation.With the rapid development of next-generation communication, researchers face issues regarding data rate, throughput, EE, etc.
The interconnection of numerous wireless devices should enable future spectral and energy-efficient wireless communications.For better spectral efficiency, Khan et al. [68] looked at multi-cell NOMA-BC and suggested a way to make it work better.They considered BS's transmission power and BD's RC to optimize these under imperfect SIC.The authors used the decomposition approach and KKT criteria to create an optimal solution that maximized spectral efficiency.According to the evaluation of the performance, the proposed NOMA-BC technique outperforms as compared to NOMA without BC.
Future wireless networks will use cutting-edge technologies to link millions of IoT networks' sensors and devices.The connection of numerous devices causes a network's massive data transfer.With the nature of wireless communication, it is not possible to provide power backup to many devices.Energy harvesting is the key term in a wireless network, where different devices harvest energy from many sources to remain alive in the network.NOMA and BC are the key technologies for EE and network spectrum efficiency.As was previously said, BNs have limited power and energy.Due to problems with resource allocation in NOMA, BNs may face a variety of problems, including poor SINR, complexity, and limited energy supply.Li et al. [69] looked into the SINR and energy properties of NOMA-BC by suggesting a new way to allocate the resources that involves several groups of BNs with different RCs and frame  [70] considered the full-duplex UAV leveraging NOMA-BC to optimize the network's sum rate.By deploying a full-duplex UAV, BS is unnecessary because it can transmit and receive BC information.An iterative algorithm based on the quadratic transform algorithm and the block coordinated descent (BCD) scheme is suggested to solve the sum rate maximization optimization problem.This algorithm takes into account the UAV's residual self-interference (RSI) and energy harvesting.
There has been a lot of focus on integrating BC and NOMA to promote cooperative energy usage across IoT devices.This work [71] aims to maximize the uplink data rate while meeting the QoS criteria of the downlink users by designing a beamforming scheme for NOMA-BC networks.The non-concave optimization problem is transformed into a convex one using semidefinite relaxation (SDR).Simulation results showed that the suggested technique performed better than the reference implementation.In the future, 6G-enabled umMTC networks will use PD-NOMA and BC to connect large-scale IoT devices.However, power allocation becomes critical due to NOMA co-channel interference.Existing convex optimization approaches struggle in dynamic environments.In order to resolve this issue and boost the uplink backscatter devices' sum rate, authors in [72] proposed a novel model-free NOMA-BC method to help base stations handle the challenging resource scheduling assignment in the dynamic nature of networks.Under the QoS constraints of downlink IoT users, the goal is to maximize the sum rate of backscatter users by jointly maximizing the transmit power for downlink users and the RC for BDs.The optimization problem of maximizing sum rate is a complex Markov decision process (MDP) that cannot be solved using conven-tional methods.Hence, the RL-based model-free soft-actor critic (SAC) algorithm is employed.Based on numerical results, the suggested methodology performs better in terms of the attainable sum rate of uplink BDs than the benchmark optimization method.
2) Maximizing Sum Rate and Throughput for Downlink Scenarios: Because of the ambient nature of the devices drawing power from ambient sources, AmBC is an energy-efficient variant of BC.Chen et al. [73] studied the backscatter-assisted NOMA for downlink communication.The authors considered two cellular users based on the NOMA downlink, in which BN divides the received signal from BS to harvest the energy and send the information through the backscattered signal.Particularly, energy consumption constraints are studied in terms of optimal total energy transmitted, RC, and optimal power allocation for each transmission block to enhance the EC of the network.Furthermore, a scenario was considered where some restrictions were applied to the BS and BN, such as average transmit power and fixed RC, to reduce the complexity of the network.The authors proposed a less complex algorithm to minimize the computational and signaling overhead.The proposed scheme's performance gain outperformed the compared approaches.
Liao et al. [74] studied NOMA-enhanced SRN in which full-duplex access point (FAP), a legacy user, and BNs considered to improve the spectrum efficiency.The authors proposed a NOMA-based DTDMA technique to utilize the dynamic nature of the channel.To boost the minimum throughput, the authors jointly optimized RC to find the best power and time allocation of BNs, taking into account the needs of the legacy user, the power allocation of FAP for subcarriers, and the constraints of harvested energy.They proposed an efficient iterative algorithm to solve the optimization problem by applying optimization techniques and investigating the proposed algorithm's intricacy.The performance of NOMA-enhanced DTDMA is extensively better than benchmark techniques in terms of throughput and fairness of BNs.Furthermore, it has been demonstrated that energy harvesting extends the duration of the device's operation.Zhang et al. [75] studied an SRN where multiple BDs share the same receiver as the primary transmitter.A hybrid peerassisted NOMA plus TDMA technique is proposed.The BDs are categorized into different NOMA clusters based on their channel strengths.Only one cluster transmits per time slot using NOMA, while the other clusters can harvest energy from previous transmissions.An efficient technique was proposed to get the lowest possible throughput for all BDs by finding the best ways to divide up BS's power, BD's RCs, and time, all while keeping things like energy causality in mind.Numerical results showed the proposed scheme achieves higher throughput than conventional NOMA and TDMA schemes.Throughput is also improved with peer assistance, where weaker clusters benefit from harvesting energy from stronger clusters.The impact of imperfect SIC is also evaluated.The primary 6G mobile network scheme is known as umMTC.In another work [76], Ding et al. explored the NOMA-assisted BC application for umMTC under the 6G mobile network and observed simultaneous energy and spectrum cooperation between uplink and downlink.The suggested optimal resource allocation scheme is better suited to boost uplink throughput while reducing interference between uplink and downlink.Combining BC with nearly every aspect of communication can result in a dependable and battery-free transmission.Khan et al. [77] looked at a vehicle-to-everything (V2X) network and did some novel research by combining BC and NOMA in their analysis.According to the proposed scheme, all the considered vehicles are connected to BS using roadside units (RSUs) and the backscatter tags deployed on the roads' sides.To improve the performance of the network, a convex problem of power allocation at RSUs and BSs is combined as a joint optimization problem.The problem is formulated and solved using sub-gradient and KKT conditions techniques.Simulation results of the proposed scheme showed that the proposed scheme outperforms in terms of performance compared to simple NOMA schemes.
The performance of AmBC systems in terms of throughput and enormous connectivity improved with the introduction of NOMA.Ding et al. [78] studied the usage and advantages of the NOMA technique in BC systems to enhance the reliability of the system.The authors designed two resource allocation approaches to evaluate the trade-off regarding system performance and complexity.Finally, they consider a legacy system with an enhanced NOMA AmBC system using OFDMA and space division multiple access (SDMA).
Departing from the earlier research conducted for uplinkbased works in [67] and [70], Khan et al. [79] considered downlink PD-NOMA enabled AmBC with imperfect SIC decoding for future 6G technologies.The main objective of the proposed scheme is to enhance the sum rate of the system.To achieve the optimal sum rates, authors jointly optimize the RC of the BN and transmission power of the source.A closed-form solution is formulated using an iterative algorithm, validating the results by comparing them with other power domain NOMA schemes.In contradistinction to the preceding works for uplink and downlink NOMA considering perfect CSI in [67], [70], and [79], Khan et al. [80] considered statistical CSI for NOMA-enabled BC for IoT networks and proposed the optimum solution to enhance the system's sum capacity.The authors jointly optimized the IoT device's transmission power and the BD's RC and derived it as a convex problem.KKT conditions are applied, and results are evaluated by comparing them with simple NOMA and OMA benchmark schemes to find the optimal solution.
In order to increase system efficiency, research has concentrated on hybrid approaches such as backscattering in the NOMA B5G and 6G eras.However, current literature mainly considers optimizing power allocation under perfect SIC, which is impractical.Additionally, some researchers have considered imperfect SIC but failed to consider cooperation between communicating users, while others included power allocation optimization for the same time allotment in hops.Considering all network attributes, resource management has not been investigated under imperfect SIC.To address these issues, Ahmed et al. [81] proposed a new resource management technique to enhance the sum rate of the NOMAbased backscatter cooperative system under imperfect SIC.Furthermore, they optimized the framework and derived a possible solution using KKT conditions and dual theory techniques.
In recent years, AmBC has gained high potential in academia and industries due to its battery-free feature for future IoT devices.SR technology is an advancement in the AmBC technique to enhance the spectrum and power efficiency by sharing the power and spectrum of both the source and receiver of the communication.The combination of NOMA and SR AmBC resulted in a promising technique for large-scale next-generation wireless devices to increase the network's EE and massive data communication.Elsayed et al. [82] investigated and enhanced the spectrum efficiency of the network by considering the NOMA-based SR AmBC under the Nakagami-m fading scheme.The authors formulate the closed-form expression of the EC of BD and cellular transmission by considering Nakagami-m fading and additive white Gaussian noise (AWGN) in a wireless channel.Simulation results were conducted based on Monte-Carlo simulations by studying the influence of system parameters on EC.
NOMA enables massive connectivity in B5G and 6G networks.AmBC and NOMA together create new communication enhancements and deal with difficulties in dense networks.Khan et al. [83] studied multi-cell NOMA-based AmBC B5G networks and came up with a way to divide up resources using a dual theory approach to get the system's total rate to be as high as possible.The authors derived a joint optimization problem to maximize the sum rate and formulated two subproblems: BS's transmit power and BN's RC.The proposed method outperforms conventional NOMA and OMA schemes in simulation results for sum rate.Recent research by the authors in [84] focuses on a resource allocation algorithm for a BC system with UAV assistance that leverages NOMA.The goal is to maximize the system's overall rate while considering constraints on energy collection time, UAV position, and gateway amplifi-cation factor.Using a BCD approach, the algorithm divides the problem into smaller ones.It employs a remote power supply gateway as a relay and a UAV as a mobile BS.The algorithm performs better than benchmark methodologies and effectively enhances system throughput.
Xie et al. [85] discussed a new approach to improving the capacity of existing NOMA systems by adding an uplink BD.The proposed approach formulates an uplink data rate maximization problem and uses an alternating algorithm to solve it iteratively by ensuring QoS requirements for two users.The study provides a closed-form solution and simulation results to validate the proposed approach.The research has practical significance for IoT networks as it allows adding more IoT devices to an existing NOMA network, significantly improving its sum rates.The study contributes to resource-limited networks and energy cooperation techniques, particularly BDs.A recent study in [86] has examined a multi-user transmission scheme that utilizes BC and FD-NOMA.The system considers a downlink-NOMA network with an FD source, multi-users, with BDs transmitting information simultaneously.The main goal is to optimize the downlink sum rate while considering SIC restrictions and the desired target uplink sum rate.The resulting optimization problem is proposed to be solved using an iterative method.This algorithm maximizes the downlink sum rate by optimizing the RC and power allocation coefficients simultaneously.The simulation results demonstrate that the proposed algorithm is effective compared to baseline schemes.
The new beamforming design for an SR system is looked at in [87].In this system, a BS handles a backscatter tag and many NOMA users at the same time.The objective is to tag under minimal rate and energy harvesting limits while maximizing the WSR of the NOMA users.The tag can gather power and deliver its data by sending the signal to the NOMA users.The closest user decodes the data from the tag in addition to its own data by using SIC decoding.Assuming that each node needs a certain minimum rate and the tag can only harvest a certain amount of energy, an optimization problem is made to design the BS beamformer and NOMA power distribution in a way that maximizes the WSR.Since the issue is non-convex, it is solved continually with fractional programming and alternative optimization approaches.

C. Ensuring Security in NOMA-BC Communications
This subsection pertains to the critical element of security in NOMA-BC systems, as shown in Fig. 4. The subsection presents studies that suggest different techniques for improving security and privacy in wireless communications within NOMA-BC systems.Looking at techniques used in this study to look into authenticity and security in NOMA-BC systems, security in multi-cell NOMA networks, physical layer security (PLS) in NOMA-based AmBC systems, and making PD-NOMA-enabled BC systems safer are all part of this study.The studies aim to tackle various challenges, in-  The results indicate that it is crucial to maintain a balance between security and performance trade-offs in NOMA-BC systems.The suggested methods provide solutions to improve reliability, confidentiality rates, and overall security in different communication situations.A summary Table VIII has been included to aid readers in understanding these novel methodologies.The table is useful for readers to gain insight into the most common techniques and innovations in secure NOMA-BC systems.
The influence of NOMA-enhanced AmBC systems on industries and the academic community is significant.By deploying energy-efficient and reliable wireless communication, privacy and security are the main issues in such transmission types.Everyone wants to protect their data while it is being transmitted.In [88], Li et al. studied the authenticity and security of the NOMA-added AmBC system in which two users scenario and existence of an eavesdropper is considered.The authors formulate the analytical expressions of the outage and intercept probability for all BNs and users with in-phase and quadrature-phase imbalance (IQI).The behavior and diversity order is derived for outage probability (OP) in high SNR and analyzed.The results showed that IQI enhances security but reduces performance, and NOMA-enhanced AmBC systems perform better in terms of reliability than OMA systems with low SNR.When reliability increases, then security will affect the trade-off that exists.
The proliferation of IoT devices has raised security concerns and exposed data risks.Unlike the single-cell study outlined in [88], Khan et al. [89] studied the PD-NOMAbased multicell BC network security issue, in which the BS of each cell transmits the information to the cellular user.Due to the wireless nature of the system, eavesdroppers can hear the information.Therefore, the authors considered a multi-cell NOMA network and formulated an optimization problem to improve security.The RC of the BNs is considered for optimization, and the optimization problem is considered convex.The authors used KKT conditions to maximize the secrecy rate of multi-cell NOMA under TDMA for the optimal solution.Monte Carlo simulationbased results showed that the proposed scheme's performance is better than the TDMA scheme's.In another work [90], Li et al. studied a NOMA-based AmBC system with an eavesdropper and imperfect SIC.The authors looked into how PLS could be used to make the system they were looking at more reliable and safe by assuming realtime metrics of hardware flaws, imperfect SIC, and channel estimation errors.By considering the mathematical noise relation determined between the OP and the intercept probability (IP), an artificial noise technique was presented to increase the system's security.The asymptotic inspection and diversity orders for the OP with a high SNR are assessed for a real-time system.Also explored are the IP's asymptotic actions when there is a high main-to-eavesdropper ratio (MER).
Diverging from prior studies assuming statistical CSI in their scenarios exemplified by [88], [89], and imperfect SIC in [90], Khan et al. [91] assumed perfect SIC and CSI and studied the PD-NOMA enabled BC for 6G technology under multiple non-colluding eavesdroppers to enhance the spectrum efficiency and link security.The authors proposed an optimization structure to improve the network's security by targeting the tag signal in the presence of multiple non-colluding eavesdroppers.Furthermore, they enhance the link security by deriving the optimal RC and including the BS's transmit power.By evaluating the performance of the proposed system, we conclude that the proposed power domain NOMA-based BC system increased the secrecy rate compared to OMA.In another work, Khan et al. [92] studied NOMA-enabled AmBC for IoT networks and investigated the PLS to improve the security level.Security levels are decreasing on massive devices due to the wireless nature of communication.The authors proposed an optimization scheme by deploying different operations of NOMA-based BC for IoT networks with multiple eavesdroppers to improve the system's security.The primary goal of improving security is met through the joint optimization of BS transmission power and BN RC.The suggested NOMA-enabled AmBC technique outperforms in terms of secrecy rate.
Recently, the authors in [93] investigated how to improve authentication performance and developed three physicallayer authentication (PLA) methods, each of which uses various multiplexing techniques for authentication tags, which include PLA with shared authentication tags (PLA-SAT), PLA with space division multiplexing authentication tags (PLA-SDMAT), and PLA with time division multiplexing authentication tags (PLA-TDMAT).These techniques improve the system's authentication's robustness, covertness, and efficiency.To determine how reliable the suggested PLA schemes are, the authors develop analytical formulas for the probability of a false alarm (PFA) and the probability of detection (PD), considering errors in channel estimation.These metrics comprehensively evaluate the PLA schemes' capacity to precisely identify users, prevent unauthorized access, and mitigate malicious actions.With analytical formulations for PFA, PD, and OP, the proposed schemes are carefully created and examined for their robustness.The study focused on how the PLA-SAT scheme is better, how authentication affects how well the system works when it goes down, and how optimizing power allocation could make the system more reliable.

V. PERFORMANCE ANALYSIS OF NOMA-BC SYSTEMS
This section pertains to evaluating and investigating the effective functioning of NOMA-BC in different systems and scenarios.This subsection presents a series of studies that examine the merits and demerits of NOMA-BC in various scenarios.These include cellular and IoT systems, green IoT systems, uplink and downlink transmissions, maritime transportation systems, and cognitive IoT networks.The proposed research presents unique methodologies and approaches for assessing and estimating system performance indicators, such as dependability, bit error rate, ergodic rate, and OP among others.The authors investigate the effects of various factors, including power allocation, energy profiles, QoS requirements, interference, and security, on how well NOMA-BC systems operate.In order to provide insight into the advantages and problems of implementing NOMA-BC technology in various communication scenarios, the studies make use of numerical analysis and simulation results.Finally, Table IX is enclosed, which provides a summary of the NOMA-BC merger with IoT and MTC that assists the reader in comprehending these innovative methods.
1) Performance Analysis of NOMA-BC Integrated with IoT and Machine Type Communication: In order to handle the many forms of traffic in 5G and 6G networks, NOMA is a viable solution.AmBC is also an encouraging solution for IoT systems due to its EE.Zhang et al. [94] considered a symbiotic model of cellular and IoT systems based on backscatter-NOMA.The authors include AmBC with NOMA as a downlink system where BS transmits messages to the two cellular users.One user has a direct link and is close to the BS.The second user will be far from the BS and communicate through a BD using passive radio technology.Specifically, the NOMA-BC system becomes a symbiotic radio system when BS only deals with cellular users far from BS.The authors calculated each system's ergodic rates and OPs and examined the resulting numerical details.
Considering hybrid technologies or systems is the best way to enhance the system throughput and efficacy.Zeb et al. [95] proposed NOMA-based BC for green IoT systems.The authors included a wireless-powered BC system in which IoT devices use a hybrid scheme combining TDMA with PD-NOMA to enhance the narration of the system.By analyzing the numerical results, the authors claimed that the proposed hybrid technique is much better than the standalone TDMA or PD-NOMA regarding system throughput.The proposed method also enhances the capacity to serve many devices in the network.
Nazar et al. [96] studied PD-NOMA-assisted BC and analyzed the BER for uplink transmission.The authors consider the BER for the NOMA-assisted BC system and include two BNs and one reader device as a cluster.Monte Carlo simulations prove the BER expressions for different RCs.The authors compared the successfully transmitted bits in NOMA-BC with an OMA strategy regarding the optimal and non-optimal nature of the RC while settling down the suitable RC.The authors derived the analytical expressions and simulated the results to find the best nature of BER for BNs in terms of various coefficients of the reflection.
NOMA has a crucial feature for multiple users that can access a single channel for communication to achieve high spectrum efficacy.Chen et al. [97] proposed a NOMAenhanced backscatter cooperation solution for downlink transmission that allows excess downlink signal power at one user to be backscattered to the other user who cannot receive the signal.The outage performance of the proposed scheme is compared to the non-cooperation (NC)-NOMA scheme, the conventional relaying (CR)-NOMA scheme, and the incremental relaying (IR)-NOMA method.Analytical results showed that the backscatter cooperation NOMA scheme is better than NC-NOMA in reliability.The authors derived the asymptotic expressions of the outage performance in a high SNR scenario.They analyzed the expected rates of all the schemes mentioned and concluded that the proposed scheme is better regarding transmission effectiveness.
Future IoT shows the new capabilities allowing IoT devices to operate with various energy profiles and QoS specifications.In [98], the authors considered two energy and spectral-efficient techniques: WPT-NOMA and BC-based NOMA.Using NOMA, different devices can use the same spectrum WPT and BC, different energy profile holders can communicate with each other, and a dedicated power beacon is not required.The author considered hybrid SIC decoding to propose the WPT-NOMA scheme that avoids the OP error.BC-based NOMA calculates the OP error and evaluates it using extreme value theory in the proposed scheme.It was assessed how BC-based NOMA's characteristics affected diversity gain.The simulation's outcomes are contrasted and validated.
Elsayed et al. [99] looked into a NOMA-based SR system where a source node uses NOMA to send two messages to a destination and lets a BD send its own message by reflecting the source signal.They analyzed the OP of the system, assuming Nakagami-m fading channels with additive white Gaussian noise.New closed-form and asymptotic expressions for the OP are derived.An outage optimal power allocation algorithm is proposed to minimize the BD message OP by optimally setting the NOMA power allocation factor.Numerical and simulation results validated the analysis and revealed how various parameters impact outage performance.The proposed NOMA system outperforms an OMA benchmark at low-to-medium SNR levels.
To investigate the OP for the SINR and the ergodic rate of the symbiotic radio devices and cellular users, downlink transmission of a NOMA-enhanced cellular symbiotic radio system based on mMTC was taken into consideration by Raza et al. [100].Furthermore, the authors exhaustively examine the OP and ergodic rate for cellular symbiotic radio systems and NOMA-enhanced BC systems to evaluate the performance of future cellular mMTC systems.Recently, in [101], It was described how well a hybrid TDMA/PD-NOMA monostatic BC system performs in low-power IoT applications.This study offers a design framework for dynamic systems in contrast to traditional techniques concentrating on static NOMA-aided monostatic BC systems.Furthermore, novel methodologies are suggested to improve the efficiency of both stationary and evolving systems.A two-node pairing (TNP) strategy is suggested to improve the successful decoding of NOMA groups in static systems.Under the current channel circumstances, the BN power RC is dynamically adjusted via an adaptive power reflection coefficient (APRC) scheme.The hybrid APRC/DSP and dynamic-sized pairing (DSP) schemes are recommended for dynamic systems, allowing for flexibility in the number of BNs in a NOMA group.Performance analysis was done based on how many successful BNs and how many bits the reader or controller could decode.Results show that the proposed systems are superior to established methods and cutting-edge techniques.
Recently, an article [102] looked into an SR system with a primary user with a single antenna and several backscatter tags with single antennas that use the primary BS signal to send data.NOMA is used for the tags to share the channel.We set up an optimization problem to find the best way to minimize the transmit power at the multi-antenna BS while still meeting the rate needs of the primary user, the energy harvesting needs of the tags, and the beamforming vector design.Since the problem is non-convex, semidefinite relaxation techniques are used to obtain a suboptimal solution.After the simulation results, they found that the suggested optimization framework and NOMA-enabled SR system can handle more tags than benchmark schemes while still meeting QoS requirements and using less power for transmission.Specifically, the beamforming optimization enables green operation.
Different from [99]- [102] works, which considered perfect CSI, [103] considered imperfect CSI and suggested SR communication for uplink AmBC with NOMA in a mutually beneficial way to improve spectrum efficiency and connectivity for IoT applications.This allows a cognitive radio transmitter (CT) to transmit information to multi-ple BDs simultaneously through power domain multiple access.A unique word (UW) set is generated, and each BD uses a different UW for frame synchronization and user identification at the receiver.M-sequences are used as UWs due to their good autocorrelation and cross-correlation properties.A multi-user detection algorithm called evolved correlation successive interference cancellation (ECSIC) is proposed for the receiver.It uses two detection thresholds to improve performance at low SNRs compared to a single threshold algorithm while achieving similar performance at high SNRs.Through simulations, it verifies that the proposed techniques can effectively detect users and recover information in scenarios with different numbers of devices, channels, Rc, etc.
2) Performance Analysis of NOMA-BC with RIS: This subsection assesses and examines the operational efficiency of NOMA-BC systems integrated with RIS.The introduction of RIS technologies aims to improve the effectiveness of future IoT networks.First, we will explore the shared reflective paradigm underlying BC and RIS while highlighting their primary technical divergences.The comparative examination encompasses several key aspects: operational modes, transmission speeds, coverage range, energy consumption, technical complexity, and application fields.
BC and RIS represent two different approaches in wireless technology.BC relies on the reflection and modulation of existing radio signals.This approach is notable for its minimal power requirement, as it does not generate RF signals [104].This makes BC particularly suitable for applications where low power consumption is crucial, such as in RFID systems and various IoT devices, and it is predominantly used in scenarios demanding lower data transfer rates over short distances.The simplicity of BC's equipment, focusing on signal reflection and modulation, is a crucial characteristic.
In contrast, RIS uses specially designed surfaces with numerous tiny, controllable elements.These elements can manipulate electromagnetic waves, adjusting their phase, amplitude, and polarization [105].This capability allows RIS to significantly enhance wireless communication by optimizing the path of radio waves, potentially improving communication systems' range and data rates.RIS finds its utility in various advanced wireless applications, from boosting signal quality in cellular networks to enabling efficient energy transfer and reducing network interference [106].The operational complexity of RIS is higher than that of BC due to the need for intricate control over its surface elements.
The studies in this part evaluate the advantages of combining RIS with NOMA-BC systems by looking at essential performance indicators, including OP, EC, throughput, and EE.The evaluation considers several variables, including the number of reflecting elements in the RIS, power distribution, phase shift, and the quality of user and BS communication.The study's numerical analysis and simulation results show that RIS-based NOMA-BC systems work better than regular NOMA-BC or OMA-based systems.The proposed Backscatter Links.
Fig. 5: Illustration of RIS-assisted NOMA-BC Scenario Communications techniques and algorithms aim to enhance the deployment of RIS in NOMA-BC systems, improving the overall performance and efficiency of the system.Before concluding this subsection, Table X summarizes the main features of each innovative method.Intelligent communication technologies are required for future IoT networks to improve network dependability.Le et al. [107] considered the RIS to improve the network's effectiveness and intelligence.The authors examined how RIS is used in BC systems that have been upgraded by NOMA and then developed a two-user-based paradigm.By deploying direct and backscatter links, they evaluate the OP, EC, and throughput of the RIS-based NOMA scheme in the presence of far and near users.Two methods are proposed to calculate the crucial elements of the RIS system because the number of reflecting elements is significant.The proposed approach performs significantly better in OP and EC than the OMA-based RIS system.In another work, Zuo et al. [108] considered RIS-based NOMA added BC and formulated a joint optimization problem regarding power, RC for BNs, and phase shift for RIS.The authors proposed a low-complex algorithm by considering different optimization problems to resolve the non-convex problem.The proposed scheme, which elaborates the staging of the system, outperforms the NOMA-based BC without RIS, according to the results.
Unlike the studies outlined in [107] and [108], Zhuang et al. [109] considered imperfect SIC and blind channel estimation (CSI) and framed IRS-NOMA-based AmBC model.The authors deployed two cell users with different geographical locations; one is a center-cell user, and the other is an edgecell user, a BS, BD, and IRS.BS and BD can directly communicate with center-cell users but not with edge-cell users due to the high buildings between them.IRS was installed on the tall building by the authors to improve communication.They derived an optimization problem to To improve the performance of diverse group of users, resource efficient problem formulated by jointly optimizing resource block and power allocation and phase shifts of RIS.DL-Downlink, UL-Uplink, 1A-Single antenna, 2U-Two Users, MCS-Monte Carlo simulations, xA-Multi antenna, SDR-Semidefinite relaxation maximize EE by considering the power allocation and phase shift.A closed-form expression is used to solve the phaseshift subproblem, and the Lagrange dual theory and subgradient approach are used to solve the power allocation subproblem for NOMA users.An iterative technique based on the Dinkelbach method is considered to discover the network's optimal EE.
In a recent article [110], the authors present a framework incorporating downlink active, uplink BC, and STAR-RIS.The system uses NOMA to improve spectrum efficiency.The author's aim is to raise the system's weighted sum rate by improving the FD BS, STAR-RIS, and downlink NOMA decoding orders at both active and passive beamforming.The suggested framework performs better than baseline systems and provides a flexible performance trade-off between downlink and uplink transmissions.The findings support the usefulness of the suggested framework and offer prospective directions for additional investigation.
In contrast to [109], the authors of [111] looked at how well a PD-NOMA-enabled BS worked as a source in an IRS-assisted multiple-user AmBC system.The primary goal of this research is to achieve good spectral efficiency and coverage, both of which are essential for future wireless connectivity.A novel system is proposed consisting of a BS, an IRS with multiple elements, and two destination devices.Destination devices decode the respective message using the band pass filter (BPF) and SIC decoding.The IRS acts as a relay to enable communication between the BS and the destination devices.This paper addressed Rayleigh fading channel characterization and imperfect SIC operation.Closed-form expressions for CDF, SNR, and OP are obtained to analyze the performance of the considered system.
Van et al. [112] proposed a system model for enhancing backscatter IoT communications using NOMA and RIS.The model considers two BS with multiple antennas serving two NOMA devices: a cell-center user (D1) and a cell-edge user (D2), as well as a BD.The cell-edge user benefits from the assistance of an RIS with multiple reflecting elements.The system also experiences interference from a nearby BS.In this system, the transmit antenna selection (TAS) scheme is used at the BS to improve energy and spectrum efficiency and serve more users effectively.A two-user grouping model was adopted within the coverage of the main BS associated with a particular RIS.Interference from neighboring BSs is also considered.It was possible to come up with analytical expressions for two important performance metrics: the BD and the two NOMA devices' EC and their OP.Asymptotic analyses for OP in the high SNR regime are also provided.By using RIS and TAS techniques, the signal phases can be changed in a smart way to get the best SNR and outage performance compared to benchmark schemes that do not use these technologies.
The channel gain and the sequence of SIC decoding can be adjusted by installing RIS.In other words, RIS encourages a more adaptable NOMA design.Wu et al. [113] studied RIS-assisted SR communication in the NOMA system, which demonstrates the RIS's amazing potential for IoT data transmission while also improving NOMA's performance.In order to accommodate the various transmission needs in future wireless networks, the suggested system took into account two categories of NOMA users: EE-and spectrum efficiency-oriented users.The main objective of this paper is to optimize resource efficiency and improve the spectrum and energy efficacy by jointly optimizing power allocation, two-dimensional time-frequency resource block, and phase shift of RIS while ensuring the QoS.Proposed SRbased technique was compared with one-dimensional frequency resource block and proposed technique outperform in terms of resource efficiency.Due to complex model of proposed method, authors divided this problem into three sub-problems then AO optimization technique is adopted to to solve them.
3) Performance Analysis of NOMA-BC with Cognitive Radio Network, UAV, IoV, and VLC: Researchers are looking into numerous methods to enhance wireless network performance in response to the rising need for fast and viable communication.NOMA has lately been combined with other cutting-edge innovations such as CRN, UAVs, IoV, and VLC to improve the network's performance further.Below this section, we will summarize the most recent research that has looked into the effectiveness of NOMA-BC with cutting-edge technologies.An overview of recent Farajzadeh et al. [114] suggest well UAV with NOMAbased BC in which UAV acts as a dual data collector and an energy broadcaster.The authors asserted that this method would increase the network's data-collecting efficiency.By changing the UAV's coordinates, the authors' main goal is to reduce flying duration and accelerate the quantity of decoded bits.If a UAV communicates near the ground, it will cover less area and take much flight time to cover the entire network.The authors calculate the optimal altitude for UAVs to maximize interference, flight time, and coverage area.Instead of TDMA, the authors use power-domain NOMA up-link to communicate with many BNs.By analyzing numerical results, the optimal behavior of UAVs is noted in terms of different network parameters in different scenarios.
Promising techniques like NOMA and AmBC jointly enable future IoT to make energy and spectrum-efficient transmission by connecting many IoT devices.Le et al. [115] studied cognitive radio (CR)-based AmBC by enabling the NOMA technique.A BS can talk with two destination devices, and the relay node acts to collect, decode, and backscatter the source signal in the considered scenario.The authors derived the expression by considering the node's EC and OP to calculate the system's performance.Deploying NOMA-based AmBC with CR enhances the network's overall OP and throughput efficiency.The proposed technique achieves efficient spectrum efficiency by comparing numerical results and enhancing system reliability in different SNRs.
VLC is a promising technique to deal with many devices and a massive amount of data transfer over a high-speed network using B5G and 6G technology, and it provides many unlicensed optical spectrum accesses.Shi et al. [116] combined NOMA-enhanced BC with VLC for umMTC systems B5G/6G.The authors coupled NOMA with a composite RF-VLC channel for multi-users to obtain data from administrators and sensor information simultaneously to improve efficiency and boost the frequency of resource allocation across all users in the IoT ecosystem.The suggested plan would use more available channel capacity and produce a lower BER.
It has been suggested that CR offers a promising means of taking advantage of the growing need for spectrum resources in 5G and 6G communication networks.To improve system throughput and allow more users to connect, Zeng et al. [117] suggested a hierarchical symbiotic transmission strategy for multi-carrier CRN.It combines mutualistic and commensalistic transmission modes in the primary and secondary networks, utilizing cooperative NOMA.By developing subcarrier allocation and power distribution techniques, the method seeks to optimize system throughput while ensuring key users receive high-quality service.A convex (DC) programming method difference solves the non-convex power allocation problem.Comparing the suggested technique with previous strategies, simulation results showed that it is more effective in achieving greater performance while facilitating more user connectivity.
IoV-based maritime transportation systems (MTS) are thought to be extremely dependable and latency-free and address massive connectivity.Li et al. [118] said that the IoV-MTS network should use a cognitive AmBC-based NOMA to make it more reliable and connect over a large area.Considering this network, security and performance are evaluated in the presence of IQI and deploying the eavesdropper.High SNR and MER are used to compute the OP and IP analytical expressions.The outcomes of the Monte Carlo simulation showed that OPs are a non-negative fixed value when SNR proceeds toward infinity.The IPs of users continue to decline while the IP of BN rises as the MER approaches infinite.The performance comparison showed that, in ideal conditions, the system's reliability decreases with IQI, but security increases.So, reliability will improve by increasing the security of the systems.
Different from [115], Asiedu et al. [119] considered multiple RIS, with both imperfect SIC and CSI and proposed a novel optimization model for symbiotic CRN by investigating backscatter-aided SN and NOMA-aided PN and sharing the same spectrum for both.The authors also include imperfect SIC, CSI, and hardware impairments in their proposed resource allocation model.To solve the complex problem and enhance the network's sum rate and resource allocation, convex weighted minimal mean square error was applied to the actual problem.The best solution was determined using an iterative approach.1) RIS-based SR in NOMA Networks : The current state of research in SR networks, particularly those integrating RIS and NOMA, shows specific gaps.Existing studies have been primarily theoretical, lacking practical considerations for managing communication resources and decoding backscatter signals.Moreover, these studies have generally used oversimplified system models, which do not explore the complexities and potential benefits of systems with multiple cooperative RIS devices.They also tend to ignore the challenges of resource allocation in numerous device scenarios and the impact of real-world imperfections in communication devices and channel estimation.This indicates a need for more comprehensive research that addresses these practical aspects and explores more intricate system models for a thorough understanding of SR with RIS and NOMA [119], [120].
2) Synchronization and Timing: Achieving accurate synchronization and timing in NOMA-based BC is another critical challenge.BDs must accurately detect and respond to the incident signals, requiring precise synchronization with the transmitter.Designing synchronization techniques resilient to multipath fading, noise, and varying signal conditions is essential for reliable communication.
3) Channel Estimation and Interference Mitigation: In next-generation wireless networks, channel modeling, estimation, and acquisition are crucial to establish successful communication [121].One of the primary challenges in NOMA-based BC is accurate channel estimation and interference mitigation.Backscattering relies on reflecting and modulating incident signals, which introduces interference and requires precise channel information for successful decoding.Specifically, the backscattering channel has a multiplication nature, so there are more channel characteristics to consider, including rank-deficient, channel correlation, etc.It is also important to look at the imperfect CSI in V2X because moving vehicles make the channel estimate less clear [122].Unfortunately, most channel estimation algorithms have a significant level of complexity.Creating a low-complexity technique or performing combined channel estimation and signal identification is greatly intended.Developing robust channel estimation techniques and interference mitigation strategies is crucial for improving NOMAbased BC systems' performance.
4) Developing Robust Modulation and Detection Mechanisms: Traditionally, NOMA research has been based on idealized scenarios with Gaussian codebook signals [123].However, actual implementations often involve superimposed signals using various modulation techniques like Quadrature Amplitude Modulation (QAM) and Binary Phase Shift Keying (BPSK) [124].Addressing the gap between theoretical models and practical applications is essential, requiring the development of advanced modulation and detection methods that can realistically achieve theoretical rates.A critical area for future investigation is the demodulation process in NOMA, which differs significantly from orthogonal systems.It involves users decoding their symbols from complex, superimposed signals, necessitating innovative approaches in modulation planning and signal power distribution.Traditional studies have assumed known modulation types and orders at the receivers, an assumption that often falls short in practical scenarios due to excessive signaling overhead.Overcoming this challenge calls for novel techniques, such as ML-based blind modulation detection, which can effectively demodulate superimposed NOMA signals without prior knowledge of modulation schemes.Additionally, integrating BC into this research framework is vital.BC involves signals being reflected or modulated by objects in the environment, complicating the NOMA signal environment.Research should aim to leverage ML algorithms to streamline NOMA signal demodulation and optimize BC processes in dynamically changing signal environments.Therefore, the future research direction lies in developing methodologies that balance technical complexity with performance efficiency.This approach should focus on enhancing detection and modulation capabilities in NOMA, particularly in BC scenarios.Such advancements are crucial for NOMA's practical deployment and optimization in nextgeneration networks' increasingly complex and dynamic landscape.
5) Multi-User Access and Scalability: NOMA is particularly attractive for scenarios with many users.However, in NOMA-based BC, supporting multiple users simultaneously and ensuring scalability are still open challenges.Developing efficient multiple access schemes, user grouping algorithms, and interference management techniques to accommodate growing users is an ongoing research area.
6) Beamforming and Precoding Techniques: Beamforming and precoding can significantly improve the performance of multi-antenna backscatter systems.However, designing effective techniques for NOMA-aided BC requires addressing challenges such as interference management, power allocation, and user fairness.Developing robust and efficient beamforming and precoding algorithms tailored to the specific requirements of multi-antenna backscatter tags in NOMA systems is a research challenge.The backscattering technique integrates with CRNs, full-duplex, and MIMO.Due to integrating these hybrid techniques, different coding schemes are required to minimize compatibility issues [125].An error and coding scheme should be compatible with massive IoT devices and emerging technologies for highdata-rate techniques.We have seen that only two users have considered NOMA-based BC, and more sub-regions for more than two users to achieve multiplexing are needed, with each region having a different RC [126].Analyzing the NOMA-based BC system's performance is challenging for more than two users, sub-regions, and optimal RC. 7) Integrating AI in NOMA-based BC Networks: AI is set to play a pivotal role in developing 6G wireless networks, focusing on enhancing signal waveform control through advanced beamforming.In RIS-assisted BC and NOMA scenarios, AI is key in fine-tuning broadcast signal propagation to maximize SNR.Machine learning, a core component of AI, is essential for improving SNR and signal quality while reducing path loss by intelligently determining the most effective transmission paths and reflector cells.Deep reinforcement learning, an advanced AI technique, brings adaptive, real-time decision-making to communication systems, vital for the dynamic nature of modern wireless networks.AI's influence extends to various 6G network aspects like adaptive coding, modulation, and more efficient spectrum use.Future AI applications involve creating innovative network architectures, optimizing link scheduling, and managing interference.A significant challenge is developing a new protocol that handles both RF energy and BC, crucial for integrating AI into 6G networks and realizing their full capabilities [127].
8) Security and Privacy: In the realm of BC systems enhanced with NOMA technology, security and privacy issues represent a complex research area.A significant concern is the vulnerability of backscattered signals to interception and disruption, such as eavesdropping and jamming attacks.New practical challenges arise as wireless networks evolve, including high-speed mobile networks, D2D communications, CR networks, and the IoT with NOMAenabled BC.One critical requirement is the development of rapid CSI assessment methods and flexible authentication frameworks.These innovations must keep pace with the changing nature of wireless channels and terminal mobility.Another significant challenge is designing a secure network architecture.This task involves creating a robust framework, developing efficient coding schemes, formulating secure protocols, and integrating hybrid encryption algorithms.Future network architectures are anticipated to feature heterogeneous communication nodes with distinct characteristics.A comprehensive security solution demands a cross-layer approach, merging physical layer security with traditional cryptographic security measures.However, designing security solutions adaptable to diverse radio access technologies, network infrastructures, and node types is daunting.The efficacy of such integrated security strategies in adapting to these varied elements is crucial and necessitates focused research efforts.

VII. CONCLUSION
Our survey provides a comprehensive overview of NOMA and BC, covering their fundamental principles, applications, and challenges.We conduct a detailed literature review, exploring various approaches and techniques used in NOMAbased BC networks to enhance performance, including EE, sum rate optimization, security, and resource allocation.We also evaluate the performance of NOMA-BC with B5G and 6G technologies, such as RIS, VLC, and UAV.By consolidating extensive and updated research into one article, our survey is a valuable resource for guiding future research directions.We address open challenges in the field and propose opportunities for improvement.Additionally, we highlight applications and future research directions to enhance the performance of NOMA-based BC networks.

Fig. 4 :
Fig. 4: Illustration of secure communication in NOMA-BC Scenario VI. OPEN ISSUES AND FUTURE RESEARCH CHALLENGES NOMA-based BC is an emerging field that combines the principles of NOMA with backscattering techniques to enable efficient communication.While this field is relatively new, there are indeed several open research challenges that researchers are currently investigating.Here are some critical open research challenges in NOMA-based BC:

TABLE I :
List of Significant Acronyms (Continued)

TABLE II :
Comparison of NOMA-BC related survey papers

TABLE III :
Comparison of PD-NOMA and CD-NOMA

TABLE IV :
Comparison of Backscattering Communication Configurations [50]late signals, thereby enabling the transmission of information without generating its own signal.This technique enables the tag to operate autonomously without needing an external power source by harnessing the harvested energy from ambient signals.Amplitude modulation-based BC is useful in low-power and energy-constrained applications, particularly IoT devices.This is because battery life and power availability are common concerns in such applications.‚SymbioticRadio (SR): It is a novel approach to enhance spectrum sharing and improve the reliability of BC, overcoming the drawbacks of AmBC and Cognitive Radio (CR).SR differentiates itself by employing a dual-spectrum sharing system that includes primary and secondary entities[50].It uses backscattering radio technology to facilitate secondary transmissions from the Secondary Transmitter (STr) to the Secondary Receiver (SRr), leveraging the RF signals from the Primary Transmitter (PTr)

TABLE V :
Summary of NOMA-BC Schemes for Enhancing EE

TABLE VI :
Summary of NOMA-BC Schemes for Maximizing Sum Rates of the Networks for Uplink Scenarios BD sum rate by optimizing the transmit power of IoT users and RC of BDs.DL-Downlink, UL-Uplink, 1A-Single Antenna, 2U-Two Users, xU-Multi Users, MCS-Monte Carlo simulations expansion.The authors also proposed the time allocation technique to reduce the interruption in communication due to different energy harvesting of BNs by frame expansion.BC and NOMA technologies are cost-effective, longlasting wireless technologies dealing with large-scale, lowpowered wireless devices.Many emerging technologies adopt NOMA-based BC, such as UAVs and IoVs.Fan et al.

TABLE VII :
Summary of NOMA-BC Schemes for Maximizing Sum Rates of the Networks for Downlink Scenarios

TABLE VIII :
Summary of NOMA-BC Approaches for Improving Security

TABLE IX :
Summary of Performance Analysis of NOMA-BC Integrated with IoT and MTC detection algorithm is proposed based on SIC that can detect the number of active BDs and recover their transmitted signals without knowing the exact number of users.DL-Downlink, UL-Uplink, 2U-Two Users, SDR-Semidefinite Relaxation, MCS-Monte Carlo simulations

TABLE X :
Summary of Performance Analysis of NOMA-BC with RIS

TABLE XI :
Summary of Performance Analysis of NOMA-BC with CRN, UAV, IoV, and VLC