Cognitive Non-orthogonal Multiple Access Research Articles

Improving the security of NOMA cognitive networks

Aiming at the scenario where the eavesdropper try to eavesdrop on the confidential messages of secondary trusted users, two physical-layer network coding (PNC) strategies, namely dual power superposition (PS-PS) encoding strategy and bit-level exclusive-or power superposition (XOR-PS) encoding strategy, are proposed to improve the security performance of cognitive non-orthogonal multiple access (NOMA) networks with the help of the data transmitted by the primary user. Considering the overlay spectrum sharing mechanism and the imperfect successive interference cancellation (SIC) technology, we first derive the closed-form accurate expressions of the outage probability and intercept probability of the secondary network with these two schemes over Rayleigh fading, and also give the asymptotic outage probability in the high signal-to-noise ratio (SNR) regime. Then, the correctness of the theoretical results is verified by simulation. For comparison, we also use the scheme without the assistance of the primary user as the benchmark scheme, which is represented by NPS. Finally, the simulation results show that the security of the proposed PS-PS and XOR-PS schemes is much better than that of the NPS scheme in the case of without reducing the outage performance.

Performance analysis of CDRT based cognitive NOMA vehicular networks with ambient backscatter relays

In order to improve the increasing spectrum requirements and the unguaranteed quality of service (QoS) for edge users in the internet of vehicles (IoV), many techniques such as cognitive radio (CR), non-orthogonal multiple access (NOMA) and coordinated direct and relay transmission (CDRT) have been proposed. To further enhance the spectrum utilization, this paper combines the above techniques with ambient backscatter communications (AmBC), which replaces the traditional relay in the network with a backscatter device (BD). When BD meets certain energy constraints (EC), an optimal dynamic reflect coefficient (RC) is enabled to promote the reflection effect. In this work, firstly, establish a CDRT-based CR-NOMA vehicular network with an ambient backscatter relay. Secondly, the closed expression for the outage probability of each secondary user is obtained with the help of Gauss Laguerre quadrature and Gauss Chebyshev quadrature. Then the system throughput analysis and the asymptotic analysis of each user when the maximum transmitting power as well as the interference temperature constrains (ITC) tends to infinity are performed. Finally, we performed simulations to reveal the impact of AmBC and ITC on edge users. Comparisons are also made with the system using fixed RC, the conventional system and its orthogonal multiple access (OMA) counterpart, demonstrating the superiority of our proposed system in improving the performance of edge users.

Outage and throughput performance analysis of incremental relaying‐based underlay cognitive nonorthogonal multiple access networks with maximal ratio combining

SummaryIn this paper, we investigate the effectiveness of incremental relaying (IR)‐based underlay cognitive nonorthogonal multiple access (CNOMA) system with maximal ratio combining (MRC). Considering imperfect successive interference cancelation (i‐SIC), we derive analytical expressions for the outage probabilities experienced by the secondary users (SUs) and the system outage probability of the IR‐CNOMA system. We also derive the analytical expression for the outage probability of far SUs by considering IR‐CNOMA system with MRC. The maximum transmit power constraint of the secondary nodes and the interference threshold constraint of the primary receiver are considered in the analysis. We also evaluate the system throughput of IR‐CNOMA network. We also derive outage and throughput expression for the traditional cooperative relaying‐based CNOMA system (CR‐CNOMA) for the purpose of comparison. We derive analytical expression for the asymptotic outage probability and the asymptotic system outage probabilities of the SUs of IR‐CNOMA system. Further, we derive expressions for the optimal power allocation (OPA) factor at the secondary source that minimizes the asymptotic system outage probability of the IR‐CNOMA system. Through numerical and simulation investigations, we demonstrate that the system outage probability significantly lowers when the OPA factor is selected based on the criteria outlined in this work. It can be observed from the results that the IR‐CNOMA system outperforms the conventional CR‐CNOMA system in terms of outage probabilities and throughput. The outage probability is further reduced when IR‐CNOMA system with MRC is considered for the far SUs.

On the performance of outage probability in cognitive NOMA random networks with hardware impairments

ABSTRACT The performance of the cognitive non-orthogonal multiple access (NOMA) systems with random locations is examined in the present paper. More precisely, the positions of the secondary receivers (SRs) are randomly distributed inside a disk with the centre at the secondary transmitter (ST). The primary networks (PNs), on the other hand, are not necessarily inside the transmission range of the ST. Two user selection schemes namely, path-loss-based and channel-gain-based approaches, are adopted to serve two SRs according to the power domain NOMA technique. Additionally, the impact of the imperfect hardware impairments (HIs) on both transmitters and receivers is also taken into consideration. Furthermore, a recently proposed transmit power allocation at the ST is employed to concurrently maximize the system performance of the secondary networks (SNs) and to strictly protect the quality-of-service (QoS) of the PNs. The correctness of the developed mathematical frameworks is corroborated by simulation results based on the Monte Carlo method. Finally, we unveil that there is no scheme that always outperforms another. Thus, an adaptive scheme can be proposed to optimize the networks' performance. Our findings also reveal that the outage probability (OP) under the harmful effect of HI can be effectively mitigated by increasing the transmit power of the ST. Additionally, increasing the transmit power of the PT is also beneficial for the SNs with the adopted transmit power allocation at the ST.

On the outage probability of energy harvested cooperative multiuser cognitive NOMA network

AbstractThis paper investigates a multiple users cooperative cognitive non‐orthogonal multiple access (NOMA) network with energy harvesting. Primary transmitter and secondary sources serving as energy‐constrained nodes harvest energy from the power beacon PBS. In primary network, primary transmitter sends signals towards primary receiver. In order to make full use of spectrum resources, the secondary network access the licensed spectrum of primary network with overlay mode, where the best secondary source with maximum transmission signal‐to‐noise ratio between primary transmitter and secondary sources act as relays to forward signal via NOMA principle. In this context, two cooperative transmission protocols, namely, traditional NOMA cooperative transmission and adaptive NOMA cooperative transmission are proposed. For NOMA cooperative transmission, relays use NOMA to forward the superimposed signal of primary signal and secondary signal without any constrain. However, for adaptive NOMA cooperative transmission, relays use NOMA by judging whether the transmission between primary transmitter and primary receiver is successful or not. Primary receiver merges signals received from two transmission slots by selection combining. Finally, exact and asymptotic outage probability of primary network and secondary network with two cooperative transmissions are derived and simulated. It is shown that outage performance is improved for the proposed adaptive NOMA cooperative transmission.

A Robust Adaptive Objective Power Allocation in Cognitive NOMA Networks.

Cognitive radio (CR) is a candidate for opportunistic spectrum implementation in wireless communications, allowing secondary users (SUs) to share the spectrum with primary users (PUs). In this paper, a robust adaptive target power allocation strategy for cognitive nonorthogonal multiple access (NOMA) networks is proposed, which involves the maximum transmission power of each SU and interference power threshold under PU constraints. By introducing the signal-to-interference-plus-noise ratio (SINR) adjustment factor, the strategy enables single-station communication to achieve energy efficiency (EE) or high throughput (HT), thus making the target function more flexible. In the same communication scenario, different cognitive users can choose different communication targets that meet their needs. Different QoS can be selected by the same cognitive user at different times. In the case of imperfect channel state information (CSI), semi-infinite (SI) constraints with bounded uncertainty sets are transformed into an optimization problem under the worst case, which is solved by the dual decomposition method. Simulation results show that this strategy has good adaptive selectivity and robustness.

Sum-rate maximization for cognitive relay NOMA Systems with channel uncertainty

In this paper, we study the sum-rate maximization problem for relay cognitive network based on decoding and forwarding (DF), where the primary network is implemented with the orthogonal frequency division multiple access (OFDMA) technology and the secondary users are in the underlying accessing mode. In the system, multiple primary users are included and one of them plays a role of relay. The relay forwards information to secondary users using the non-orthogonal multiple access (NOMA) technology. We attempt to improve the transmission rate of both primary and secondary users. The block power control strategy is developed by addressing interference of primary users caused by the channel sharing with secondary users. To tackle the tricky issue caused by the channel uncertainty, the worst-case method is adopted when solving the proposed sum-rate maximization problem. Considering the nonlinear objective function and constraints, the successive convex approximation (SCA) method is introduced to determine a suboptimal solution. Also, the Lagrangian dual algorithm is utilized to obtain the closed-form solutions of the transmission power and subchannel allocation. Numerical results verify that the proposed scheme significantly improves the sum-rate under the uncertainty channel gains compared to benchmarks.

Cognitive radio-inspired NOMA in short-packet communications

Short-packet communication is an important feature of the internet of things (IoT). For allowing massive IoT devices to connect to a network through short packets, this paper designs a cognitive radio-inspired non-orthogonal multiple access (NOMA) scheme, where the IoT users will not necessarily occupy an exclusive spectrum. To further improve the connections in an IoT network, we propose a general NOMA scheme that serves multi- ple IoT users. Since the IoT devices are generally homogeneous, a user fairness-oriented power allocation scheme is designed for the proposed NOMA scheme. Besides, for comparison purposes, the benchmark orthogonal multiple access (OMA) scheme and its corresponding user fairness transmission scheme are also proposed. The closed-form expressions of average block error rates for secondary users are derived over Nakagami-m channels in both NOMA and benchmark OMA scheme. Simulation and analysis results show that a faraway primary network will benefit the spectrum sharing. In addition, shorter packets and more different channel conditions improve the NOMA gains over OMA. Besides, power allocation factors are found to be capable of achieving user fairness and the fairness transmission is achieved by the proposed scheme. Interestingly, when the channel differences are bigger enough, paring an extra NOMA user nearly does not affect the previous system performance.

Secondary Network Throughput Optimization of NOMA Cognitive Radio Networks Under Power and Secure Constraints

Recently, the combination of cognitive radio networks with the nonorthogonal multiple access (NOMA) approach has emerged as a viable option for not only improving spectrum usage but also supporting large numbers of wireless communication connections. However, cognitive NOMA networks are unstable and vulnerable because multiple devices operate on the same frequency band. To overcome this drawback, many techniques have been proposed, such as optimal power allocation and interference cancellation. In this paper, we consider an approach by which the secondary transmitter (STx) is able to find the best licensed channel to send its confidential message to the secondary receivers (SRxs) by using the NOMA technique. To combat eavesdroppers and achieve reasonable performance, a power allocation policy that satisfies both the outage probability (OP) constraint of primary users and the security constraint of secondary users is optimized. The closed-form formulas for the OP at the primary base station and the leakage probability for the eavesdropper are obtained with imperfect channel state information. Furthermore, the throughput of the secondary network is analyzed to evaluate the system performance. Based on that, two algorithms (i.e., the continuous genetic algorithm (CGA) for CR NOMA (CGA-CRN) and particle swarm optimization (PSO) for CR NOMA (PSO-CRN)), are applied to optimize the throughput of the secondary network. These optimization algorithms guarantee not only the performance of the primary users but also the security constraints of the secondary users. Finally, simulations are presented to validate our research results and provide insights into how various factors affect system performance.

Reconfigurable Intelligent Surface-Aided Cognitive NOMA Networks: Performance Analysis and Deep Learning Evaluation

This paper investigates reconfigurable intelligent surface (RIS)-aided cognitive non-orthogonal multiple access (NOMA) systems, where an RIS is deployed to serve two users under multi-primary users’ constraints. Our analysis assumes imperfect channel state information and successive interference cancellation under scenarios with and without line-of-sight (LoS) link between source and users. We derive exact closed-form expressions for the outage probability, throughput, and an upper bound for the ergodic capacity (EC). To provide further insights, an asymptotic analysis is carried out by considering two power settings at the source. It is also determined the optimal data rate factors of all users that maximize the system throughput. In addition, a deep learning framework (DLF) for EC prediction is designed. Numerical results show that: i) compared to the system without LoS link, the performance of the proposed system with LoS link can significantly improve when the number of reflecting elements at the RIS increases, and ii) the proposed system has superior performance compared to its orthogonal multiple access counterpart. Furthermore, our proposed DLF exhibits the lowest root-mean-square error and low execution-time among other approaches, verifying the effectiveness of this method for future analysis.

On the performance of STBC-NOMA assisted overlay cognitive system under CEEs and imperfect SIC

In future wireless networks, the synergy of non-orthogonal multiple access (NOMA) and cognitive radio (CR) can provide higher spectral efficiency and increased data rate. In this context, this article studies the performance of a space-time block code-aided overlay cognitive NOMA system based on Alamouti's (2 x 1, multiple-input single-output) code under the practical consideration of channel estimation errors (CEEs) and imperfect implementation of successive interference cancellation (SIC) at the receiver. We evaluate the closed-form expressions, after approximation, for the system throughput (ST) and outage probability (OP) over Rayleigh fading channels for both the secondary and primary networks. Asymptotic expressions of the OP and ST at high signal-to-noise ratio region are also evaluated to obtain further insights into the proposed scheme. Further, the article investigates the impact of various parameters on the OP and ST of the proposed scheme. Moreover, the OP of STBC orthogonal multiple access (OMA) based overlay cognitive network is also determined and compared with the presented network. Finally, simulations are used to validate the performance analysis of the proposed network and the accuracy of the derived analytical results. The simulation result shows that the presented system achieves significant gain in terms of the OP and ST compared to the STBC-OMA based overlay cognitive system and a recently proposed scheme under imperfect SIC and CEE conditions.

Exploiting SWIPT-Enabled IoT-Based Cognitive Nonorthogonal Multiple Access With Coordinated Direct and Relay Transmission

This article proposes a simultaneous wireless information and power transfer (SWIPT)-enabled Internet of Things (IoT)-based cognitive nonorthogonal multiple access (NOMA) with coordinated direct and relay transmission (CDRT) system. It incorporates overlay cognitive radio (CR) and time switching (TS)-based SWIPT technology to enhance spectrum utilization and energy efficiency (EE). The proposed system comprises a primary network having a primary transmitter and its intended NOMA receivers (UE1 and UE2), accompanied by an energy-constrained secondary transmitter (ST) and its designated receiver (IoT-U). The primary transmitter communicates directly with its strong user UE1 and exploits the ST as an IoT relay to communicate with a weak user UE2. The IoT-relay node employs a TS-based receiver architecture and a decode-and-forward (DF) protocol to convey the weak user’s information along with its own information by following the NOMA principle. We evaluate the performance of the proposed system by considering both the perfect and imperfect successive interference cancellation (SIC) at the legitimate users over Nakagami- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula> fading in terms of outage probability (OP), system throughput, and EE. Moreover, we propose an iterative algorithm to minimize the OP by optimizing the TS factor. Furthermore, the impact of key parameters is also highlighted, which lays the guidelines for the practical design of energy-efficient and spectrum-efficient futuristic wireless communication networks.

Performance Analysis and Deep Learning Design of Wireless Powered Cognitive NOMA IoT Short-Packet Communications With Imperfect CSI and SIC

In this article, we study wireless-powered cognitive nonorthogonal multiple access (NOMA) Internet of Things (IoT) networks with short-packet communications to improve spectrum utilization and sustainability, as well as reduce the latency under imperfect channel state information (CSI) and successive interference cancelation (SIC). For performance evaluation, closed-form expressions for the block error rate (BLER) of the NOMA users, goodput, energy efficiency, latency, and reliability are derived. To gain some further insights into the system design, two scenarios can be taken into account for the positions of the primary receivers: 1) they are located near the secondary network and 2) they are located far away from the secondary network. Moreover, we propose an effective algorithm to minimize the BLERs of the NOMA users by optimizing power allocation coefficients. In addition, a novel multi-output deep-learning (DL) framework is designed to simultaneously predict the BLERs and goodputs of users towards real-time configurations for IoT systems. Numerical results show the outstanding performance of the proposed system over the orthogonal multiple access (OMA) one in terms of the BLER and goodput. Moreover, the proposed system achieves a lower latency and higher reliability compared to the long packet communications under the same channel settings. Furthermore, our designed multioutput DL also exhibits the lowest error performance and a short run-time prediction compared to the other multioutput regression models, while the predicted results using the DL model are almost matched with the simulation ones.

Energy Harvesting in Overlay Cognitive NOMA Systems With Hardware Impairments

Combining energy harvesting with cognitive radio and nonorthogonal multiple access (NOMA) techniques will be a notable candidate for improving both energy and spectral efficiency of wireless systems. In this article, we appraise the performance of an overlay cognitive NOMA (OCNOMA) system by employing an energy-harvesting-based spectrum sharing cooperation (SSC) scheme in the presence of hardware impairments (HIs) at the transceiver nodes. Herein, an energy-constrained secondary transmitter node harvests energy from primary transmitter’s radio-frequency signal and utilizes this energy to relay primary information signal as well as to transmit its own information signal using NOMA technique. For this, we investigate two SSC schemes using amplify-and-forward (AF) and decode-and-forward (DF) relaying strategies, named as SSC-AF and SSC-DF schemes. Importantly, we consider the impact of the imperfect successive interference cancellation in NOMA and distortion noises in signal processing due to HIs, which are inevitable in practical systems. Adopting Nakagami- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> fading environments, we derive the expressions of outage probability for primary and secondary networks under both SSC-AF and SSC-DF schemes and, thereby, disclose some pertinent ceiling effects in the system. Furthermore, we evaluate the system throughput and energy efficiency for the considered OCNOMA system. Our results manifest the advantages of the proposed SSC schemes over the benchmark direct primary transmission and orthogonal multiple access schemes.

Overlay Cognitive IoT-Based Full-Duplex Relaying NOMA Systems With Hardware Imperfections

Energy harvesting with cognitive radio and nonorthogonal multiple access (NOMA) techniques offer a promising solution to enhance the spectral and energy efficiency in Internet of Things (IoT) networks. This article investigates the performance of an overlay cognitive NOMA (OCNOMA) system tailored for IoT applications. Herein, the primary network includes a primary transmitter-receiver pair, whereas the secondary network comprises an energy-constrained full-duplex (FD) relaying-based secondary transmitter (ST) with its intended multiple receivers. Accordingly, ST employs a time-switching (TS)/power-splitting (PS)-based receiver architecture to harvest the energy from the radio-frequency signal of primary transmission and, thereby, uses this energy to relay the primary signal and to transmit its own signals simultaneously using the OCNOMA principle. For this, we propose a cooperative spectrum-sharing transmission (CSST) scheme using the decode-and-forward relaying strategy, while considering the realistic assumptions of FD-based loop self-interference, NOMA-based imperfect successive interference cancelation, and the transceiver hardware impairments in IoT devices. Adopting Nakagami- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${m}$ </tex-math></inline-formula> fading environments, we comprehensively analyze the performance by deriving the expressions of the outage probability for the primary and secondary networks for the FD-based CSST scheme under both TS and PS protocols. Thereby, we disclose some apposite ceiling effects and provide an insight on deciding the value of the OCNOMA power allocation factor for efficacious spectrum-sharing cooperation. We further quantify throughput and energy efficiency for the overall system. Our results demonstrate the performance advantages of the proposed FD CSST scheme over the benchmark schemes and provide useful guidelines for the practical design of cognitive IoT networks.

UAV-Assisted Cooperative &amp; Cognitive NOMA: Deployment, Clustering, and Resource Allocation

Cooperative and cognitive non-orthogonal multiple access (CCR-NOMA) has been recognized as a promising technique to overcome issues of spectrum scarcity and support massive connectivity envisioned in next-generation wireless networks. In this paper, we investigate the deployment of an unmanned aerial vehicle (UAV) as a relay that fairly serves a large number of secondary users in a hot-spot region. The UAV deployment algorithm must jointly account for user clustering, channel assignment, and resource allocation sub-problems. We propose a solution methodology that obtains user clustering and channel assignment based on the optimal resource allocations for a given UAV location. To this end, we derive closed-form optimal power and time allocations and show it delivers optimal max-min fair throughput by consuming less energy and time than geometric programming. Based on optimal resource allocation, the optimal coverage probability is also provided in closed-form, which takes channel estimation errors, hardware impairments, and primary network interference into account. The optimal coverage probabilities are used by the proposed max-min fair user clustering and channel assignment approaches. The results show that the proposed method achieves 100% accuracy in more than five orders of magnitude less time than the optimal benchmark.

Wireless Powered Cognitive NOMA-Based IoT Relay Networks: Performance Analysis and Deep Learning Evaluation

In this article, we study novel wireless powered cognitive nonorthogonal multiple access (NOMA)-based Internet-of-Things (IoT) relay networks to improve the performance of a cell-edge user under perfect and imperfect successive interference cancelation (SIC). In the secondary networks, a source node communicates with a cell-center user via direct link and with a cell-edge user through the assistance of a master IoT node under cognitive radio constraint. Exact closed-form analytical expressions for the outage probability (OP) of NOMA users and the overall system throughput are derived. To provide further insights, a performance floor analysis is also carried out considering two power-setting scenarios: 1) the transmit powers at the power beacon goes to infinity and 2) the maximum allowable power constraint goes to infinity. Moreover, we develop two iterative algorithms for minimizing OP users and maximizing system throughput subject to time-switching and power-allocation factors in two-hop transmission. Direct derivation of the closed-form expression for the ergodic capacity (EC) becomes unfeasible due to the high complexity of the proposed system model. To overcome this issue, we design a deep neural network (DNN) framework for the EC prediction toward real-time configurations. Our results show that the predicted results based on this DNN framework perfectly align with the simulations, validating our design framework. In addition, the DNN approach exhibits the lowest root-mean-square error and low run-time predictions among other regression models.

Physical layer security in cognitive NOMA sensor networks with full-duplex technique

This article studies the physical layer security in a downlink full-duplex cognitive non-orthogonal multiple access sensor networks (FD-C-NOMA). Compared with the existing works, this article proposes a FD-C-NOMA transmission scheme with a primary user (PU) and secondary user (SU) sensor nodes in the presence of an eavesdropper. The zero-forcing beamforming design problems of FD operation are investigated subject to the practical secrecy rate and the quality of services of PU. To characterize the security reliability trade-off of the FD-C-NOMA scheme, we first derive the closed-form expressions of connection outage probability (COP), the secrecy outage probability (SOP), and effective secrecy throughput (EST) of each SU in the NOMA networks. Then the impacts of the system parameters on the COP, SOP, and EST are investigated to evaluate the security and reliability in the FD-C-NOMA networks. Furthermore, in order to further verify the security and reliability of our considered network, an OMA scheme of FD operation is provided in the simulation for the purpose of comparison. Results demonstrate that the NOMA-based cognitive sensor networks of FD operation outperforms the OMA system in terms of EST. Finally, simulations are performed to validate the accuracy of our analysis results of the proposed scheme.

Performance Analysis and Deep Learning Design of Underlay Cognitive NOMA-Based CDRT Networks With Imperfect SIC and Co-Channel Interference

In this paper, we investigate an underlay cognitive non-orthogonal multiple access (NOMA)-based coordinated direct and relay transmission network with imperfect successive interference cancellation, imperfect channel state information, and co-channel interference caused by a multi-antenna primary transmitter. In the secondary network, a source communicates with a near user via direct link and with a far user through the assistance of multiple relays subject to transmit power constraints. Four relay selection schemes are proposed to enhance the performance of NOMA users and the overall system throughput. In our analysis, exact closed-form expressions for the outage probability (OP) of NOMA users and for the overall system throughput are derived. To provide further insights, a performance floor analysis is carried out considering two power-setting scenarios: (i) the transmit powers at the secondary source and relays go to infinity and (ii) the peak interference constraint goes to infinity. Towards real-time configurations, we also design a deep learning (DL) framework for the OP and system throughput prediction. Our results show that the deep neural network exhibits the lowest run-time prediction and root-mean-square error among the proposed DL models. Furthermore, the predicted results based on DL framework match with those of the analysis and simulation.

Performance Study of Cognitive Relay NOMA Networks With Dynamic Power Transmission

The cooperative non-orthogonal multiple access (NOMA) networks with one pair primary user and one pair cognitive user share the same spectrum resource via a common relay is considered in this paper. We propose a dynamic power transmission scheme for both uplink and downlink NOMA transmission in cognitive relay networks, which preserves the quality of service for the primary user. The closed-form expressions of overall outage probability and average sum rate for the proposed dynamic power transmission scheme of cognitive relay NOMA networks are derived. Both developed analytical results and Monte Carlo simulations show that the proposed dynamic power control scheme can dramatically enhance performance gain for the proposed networks, compared to other existing NOMA power allocation schemes.