Traditional Non-orthogonal Multiple Access Research Articles

On the Performance of Noma Systems with Different User Grouping Strategies

This paper investigates several critical issues on the ergodic sum capacity and system outage performance of downlink non-orthogonal multiple access (NOMA) systems with different numbers of users per cluster sharing the same resource. The outcomes from this work suggest that NOMA systems with more users per cluster perform better under a low channel gain, while those with fewer users per cluster perform better under a high channel gain. It discloses a fact that a traditional NOMA system with a fixed user grouping scheme may not be a good choice to deal with complex channel conditions with diverse applications. To address the issue, this paper introduces a concept of mixed user grouping NOMA, which outperforms OMA and traditional NOMA significantly. The work in this paper lays a foundation for more further investigations on mixed user grouping NOMA systems.

Air Reconfigurable Intelligent Surface Enhanced Multiuser NOMA System

This paper proposes a new framework of Aerial Reconfigurable Intelligent Surface (ARIS) enhancing the Non-Orthogonal Multiple Access (NOMA) system. The Base Station (BS) transmits superimposed signals to multiple users with different channel gains through ARIS which can flexibly change channel conditions and perform intelligent NOMA operations. It ensures that our system can perform well in providing services to multiple users simultaneously. In this system, the placement of the Unmanned Aerial Vehicle (UAV) is jointly optimized along with the AIRS passive beam and the multi-user power allocation in order to maximize the communication sum rate. Since the joint optimization problem is non-convex and coupled, it is hence disintegrated into three subproblems and it is solved alternately through the Successive Convex Approximation (SCA). Moreover, Semi Definite Programming (SDP) is used to deal with the rank one constraint of RIS reflection matrix and comparisons are made using particle swarm optimization (PSO). The numerical results show that the proposed ARIS-NOMA framework can achieve better sum rate performance than traditional NOMA with fixed RIS and OMA-ARIS.

Grant-Free NOMA: A Low-Complexity Power Control through User Clustering.

Non-orthogonal multiple access (NOMA) has emerged as a promising solution to support multiple devices on the same network resources, improving spectral efficiency and enabling massive connectivity required by ever-increasing Internet of Things devices. However, traditional NOMA schemes operate in a grant-based fashion and require channel-state information and power control, which hinders its implementation for massive machine-type communications. Accordingly, this paper proposes synchronous grant-free NOMA (GF-NOMA) frameworks that effectively integrate user equipment (UE) clustering and low-complexity power control to facilitate the power-reception disparity required by the power-domain NOMA. Although single-level GF-NOMA (SGF-NOMA) designates an identical transmit power for all UEs, multi-level GF-NOMA (MGF-NOMA) groups UEs into partitions based on the sounding reference signals strength and assigns partitions with different identical power levels. Based on the objective of interest (e.g., max-sum or max-min rate), the proposed UE clustering scheme iteratively admits UEs to form clusters whose size is dynamically determined based on the number of UEs and available resource blocks (RBs). Once the UEs are acknowledged with power levels and allocated RBs through random-access response (RAR) messages, UEs can transmit anytime without grant acquisition. Numerical results show that the proposed GF-NOMA frameworks can compute clusters in the order of milliseconds for hundreds of UEs. The MGF-NOMA can reach up to 96-99% of the optimal benchmark max-sum rate, and the SGF-NOMA reaches 87% of the optimal benchmark max-sum rate at the same power consumption. Since the MGF-NOMA and optimal benchmark enforce the strongest and weakest channel UEs to transmit at maximum and minimum transmit powers, respectively, the SGF-NOMA also offers a significantly higher energy consumption fairness and network lifetime as all UEs consume equal transmit powers. Although the MGF-NOMA delivers an inferior max-min rate performance, the SGF-NOMA is shown to reach 3e6 MbpJ energy efficiency compared to the 1e7 MbpJ benchmark.

Analyzing Coverage Probability of Reconfigurable Intelligence Surface-aided NOMA

Along with the explosive growth of wireless communication network users who require large frequency bands and low latency, it is a challenge to create a new wireless communication network beyond 5G. This is because installing a massive 5G network requires a large investment by network providers. For this reason, the authors propose an alternative beyond 5G that has better quality than 5G and a relatively lower investment value than 5G networks. This study aims to analyze the downlink of the cooperative non-orthogonal multiple access (NOMA) network, which is usually used in 5G, combined with the use of a reconfigurable intelligence surface (RIS) antenna with decode and forward relay mechanisms. RIS is processed with a limited number of objects utilizing Rayleigh fading channels. The scenario is created by a user who relays without a direct link for users near the base station and with a direct link for users far from the base station. Under the Nakagami-m fading channel, the authors carefully evaluated the probability of loss for various users as a function of perfect channel statistical information (p-CSI) utilizing simply a single input-output (SISO) system with a finite number of RIS elements. As a key success metric, the efficiency of the proposed RIS-assisted NOMA transmission mechanism is evaluated through numerical data on the outage probability for each user. The modeling outcomes demonstrate that the RIS-aided NOMA network outperforms the traditional NOMA network

Blind reconfigurable intelligent surface-aided fixed non-orthogonal multiple access for intelligent vehicular networks

In intelligent vehicular networks, vehicles should be able to communicate with their surroundings while traveling. This results in more efficient, safer, and comfortable driving experiences, as well as new commercial prospects in a variety of industries. Connected vehicles and autonomous vehicles expect 100% reliable connectivity without any compromise in quality. However, due to challenges such as difficult channel terrains in urban scenarios and dead zones, the reliability of current vehicle-to-infrastructure (V2I) and vehicle-to-vehicle communication systems cannot be guaranteed. The performance of vehicular networks can be considerably enhanced with reconfigurable intelligent surfaces (RIS). Non-orthogonal multiple access (NOMA) allows for massive connectivity with the surroundings. In vehicular networks, the RIS-assisted NOMA can ensure regulated channel gains, better coverage, throughput, and energy efficiency. In this work, a blind RIS-assisted fixed NOMA (FNOMA) system is proposed for a downlink V2I scenario. The closed-form analytical outage probability and throughput expressions are derived by considering RIS as an intelligent reflector and as a roadside unit. It is observed that the analytical and Monte Carlo simulation results are closely related. In simulations, it has been discovered that RIS-assisted FNOMA outperforms the traditional NOMA variants in terms of outage and throughput. Even without precise channel knowledge, blind RIS transmission outperforms traditional NOMA variants due to huge array gain. The increase in the number of reflective elements also results in a significant improvement in signal-to-noise ratio gains.

Robust Beamforming Design for RIS-Aided NOMA Secure Networks With Transceiver Hardware Impairments

Integrating reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) provides a promising solution to support large-scale secure communications. However, reducing the impact of transceiver hardware impairments (HWI) on the performance of RIS-aided NOMA secure networks remains a challenge. In this paper, we propose a robust transmission scheme for RIS-aided NOMA secure networks with transceiver HWI under two proposed eavesdropping scenarios and two artificial noise methods. A closed-form expression for the distortion noise power caused by transceiver HWI in NOMA networks is derived to quantify transceiver HWI. The sum secrecy rate of NOMA users with imperfect successive interference cancellation (SIC) is maximized under distortion noise for more robust security. Further, to tackle the resulting non-convex problem, we decompose it into two sub-problems. Both of them are converted into convex problems using the multi-dimensional quadratic transform (MDQT) method, semi-definite relaxation (SDR) technique, and the successive convex approximation (SCA) algorithm, and then employing the alternate optimization (AO) algorithm solve them. Numerical results demonstrate that the proposed scheme has superior security performance over orthogonal multiple access (OMA) networks, time division multiple access (TDMA) networks, and traditional NOMA networks, achieving more robust performance than the conventional scheme that ignores HWI and imperfect SIC.

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.

Adaptive User Association Scheme for Indoor Multi-User NOMA-VLC Systems

In this letter, we propose a novel adaptive multi-user association strategy for non-orthogonal multiple access (NOMA) in visible light communication (VLC) systems, by which a number of user association (UA) schemes are obtained, in order to improve the multi-user system performance. Moreover, two new concepts called the redundant user and the multi-user association matrix are proposed to indicate UA status and data rates. The system throughput of the proposed strategy is evaluated and compared with the traditional NOMA without UA and NOMA-based pairwise coupling schemes, while the effect of the residual interference is also investigated.

A Cross-Layer Optimization Framework for Index-Coded NOMA in Cache-Aided F-RANs

This paper studies cached-aided multicast transmissions in fronthaul fog radio access networks (F-RANs). While index coding and cached-aided non-orthogonal multiple access (NOMA) are techniques commonly used for utilizing cache contents to save transmit energy, there is a lack of general framework to integrate them. This work proposes index-coded NOMA and dynamic coded-NOMA to investigate energy performance of the integration of index coding and NOMA under whole-file and subfile caching, respectively. Besides, dynamic cache space allocation is applied to both caching schemes, which allocates cache sizes to the fog access points (F-APs) according to their large-scale channel conditions. For index-coded NOMA, the general grouping problem is proved to be NP-hard and optimal solutions for some special cases are given. Furthermore, efficient heuristic grouping algorithms are proposed. For dynamic coded-NOMA, we obtain the closed-form minimum transmit energy. The numerical results validate the good performance of our proposed algorithms. Index-coded NOMA and dynamic coded-NOMA have comparable performance and both of them save much energy than the existing schemes. When there are 12 F-APs under small-cache scenarios, index-coded NOMA saves energy by 70.3% compared to traditional NOMA.

Combining multi-RIS and relay for performance improvement of multi-user NOMA systems

In this paper, we propose a theoretical framework to examine the performance of a non-orthogonal multiple access (NOMA) system with multiple users under the support of multiple advanced reconfigurable intelligent surfaces (RISs) and a traditional relay. We derive the analytical expressions of main performance metrics of the proposed multi-RIS-and-relay aided multiple-user NOMA system (so called the RIS-R-NOMA system), including the outage probability (OP) and the achievable data rate (ADR) over Nakagami-m fading channels. We conduct the Monte-Carlo simulations to confirm the exactness of our mathematical expressions. Numerical results reveal that the OP of the proposed RIS-R-NOMA system is significantly lower than the OPs of the traditional NOMA systems without either RIS or relay (the R-NOMA and RIS-NOMA systems), and the ADR of the proposed RIS-R-NOMA system is much higher than the ADR of the R-NOMA system. Importantly, at high transmit power, the ADRs of both RIS-R-NOMA and R-NOMA systems are similar due to the property of NOMA. Additionally, the effects of various parameters such as the number of reflecting elements (REs), the locations of the RISs, the power allocation coefficients, and the data transmission rates on the performance of the RIS-R-NOMA system are demonstrated.

Iterative NOMA Detection for Multiple Access in Satellite High-Mobility Communications

Non-orthogonal multiple access (NOMA) is a promising technology for next generation multiple access (NGMA). However, traditional NOMA detection methods cannot cope with challenges of NGMA in satellite high-mobility communications. On the one hand, due to the high mobility and heterogeneity of terminals in terms of the velocity and acceleration, time-varying Doppler shifts caused by high-mobility terminals are relatively higher and different for each user, which degrades the demodulation performance seriously and makes multi-user interference cancellation become the bottleneck. On the other hand, owning to wide distribution of high-mobility terminals, the arriving time of users’ signals is different at the receiver, which further incurs more difficulties for NOMA detection. To solve such a problem in satellite high-mobility communications, we propose a novel multi-user detection method based on the new three-dimensional (3D) factor graph for high-mobility environments, named the turbo iterative detection (TID) algorithm. Specially, the proposed algorithm consists of the interference cancellation loop, the Doppler elimination loop and the decoding loop. By means of message passing along edges in the proposed 3D factor graph, these three iterative loops can interact with each other to effectively eliminate time-varying Doppler shifts of heterogeneous high-mobility terminals and interference among multiple users. Simulation results show that the proposed algorithm improves the bit error ratio (BER) performance more than 0.9 dB with less computational complexity compared with traditional algorithms, which demonstrates the superiority of this algorithm in terms of the BER performance and computational complexity.

D2D Assisted Q-Learning Random Access for NOMA-Based MTC Networks

Machine-type communications (MTC) should account for half the connections to the internet by 2030. The use case massive MTC (mMTC) allows for applications to connect a massive number of low-power and low-complexity devices, leading to challenges in resource allocation. Not only that, mMTC networks suffer under rigid random access schemes due to mMTC ultra-dense nature resulting in poor performance. In this sense, this paper proposes a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -Learning-based random access method for massive machine-type communications, with device clustering and non-orthogonal multiple access (NOMA). The traditional NOMA implementation increases spectral efficiency, but at the same time, demands a larger <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -Table, thus slowing down convergence, which is known to be a highly detrimental effect on massive networks. We use pre-clustering through short-range device-to-device technology to mitigate this drawback, allowing devices to operate with a smaller <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -Table. Furthermore, the previous selection of partner devices allows us to implement a full-feedback-based reward mechanism so that clusters avoid time slots already successfully allocated. Additionally, to cope with the negative impact of system overload, we propose an adaptive frame size algorithm to run in the base station (BS). It allows adjusting the frame size to the network load, preventing idle slots in an underloaded scenario, and providing extra slots when the network is overloaded. The results show the great benefits in terms of throughput of the proposed method. In addition, the impact of the use of clustering and the size of the clusters, as well as the frame size adaptation, are analyzed.

Examination of the Non-Orthogonal Multiple Access System Using Long Short Memory Based Deep Neural Network

This paper investigates deep learning (DL) non-orthogonal multiple access (NOMA) receivers based on long short-term memory (LSTM) under Rayleigh fading channel circumstances. The performance comparison between the DL NOMA detector and the traditional NOMA method is established, and results have shown that the DL-based NOMA detector performance is far better in comparison with conventional NOMA detectors. Simulation curves are compared with the performance of the DL detector in terms of minimum mean square estimate (MMSE) and least square error (LSE) estimate, taking all realistic circumstances, except the cyclic prefix (CP), and clipping distortion into account. The simulation curves demonstrate that the performance of the DL-based detector is exceptionally good when it equals 1 when the noise signal ratio (SNR) is more than 15 dB, assuming that the DL method is more resilient to clipping distortion.

Research on Energy Efficiency of NOMA-SWIPT Cooperative Relay Network Using GS-DinkelBach Algorithm.

In order to improve the energy efficiency (EE) performance of cooperative networks, this study combines non-orthogonal multiple access (NOMA) with simultaneous wireless information and power transfer (SWIPT) technologies to construct a cooperative relay network composed of one base station (BS), multiple near users, and one far user. Based on the network characteristics, a time-division resource allocation rule is proposed, and EE formulas regarding direct-link mode and cooperative mode are derived. Considering user selection and decoding performance, to obtain the optimal EE, this study utilizes a DinkelBach iterative algorithm based on the golden section (GS-DinkelBach) to solve the EE optimization problem, which is affected by power transmitted from the BS, achievable rates under three communication links, and quality of service (QoS) constraints of users. The simulation results show that the GS-DinkelBach algorithm can obtain precise EE gains with low computational complexity. Compared with the traditional NOMA–SWIPT direct-link network model and the relay network model, the optimal EE of the established network model could be increased by 0.54 dB and 1.66 dB, respectively.

Investigation of the fifth generation non-orthogonal multiple access technique for defense applications using deep learning

In modern wireless communication scenarios, non-orthogonal multiple access (NOMA) provides high throughput and spectral efficiency for fifth generation (5G) and beyond 5G systems. Traditional NOMA detectors are based on successive interference cancellation (SIC) techniques at both uplink and downlink NOMA transmissions. However, due to imperfect SIC, these detectors are not suitable for defense applications. In this paper, we investigate the 5G multiple-input multiple-output NOMA deep learning technique for defense applications and proposed a learning approach that investigates the communication system’s channel state information automatically and identifies the initial transmission sequences. With the use of the proposed deep neural network, the optimal solution is provided, and performance is much better than the traditional SIC-based NOMA detectors. Through simulations, the analytical outcomes are verified.

Channel Assignment for Hybrid NOMA Systems With Deep Reinforcement Learning

The Hybrid Non-Orthogonal Multiple Access (NOMA) is a promising candidate for multiple access techniques of future wireless communication, which integrates orthogonal multiple access and traditional NOMA. The performance of hybrid NOMA systems depends on resource allocation including power and channel. In this letter, we focus on the channel assignment. Since channel assignment needs to be adapted to a real-time changing environment and accomplished in a restricted time slot, we treat the optimization of the dynamic channel assignment problem as a deep reinforcement learning task, to achieve better environmental adaptability with low time complexity. Simulation results show that the proposed method achieves better performance in terms of sum rate and spectral efficiency, compared to conventional methods.

Reconfigurable Intelligent Surface-Assisted Non-Orthogonal Multiple Access

Reconfigurable intelligent surface (RIS) is a revolutionary technology to achieve spectrum-, energy-, and cost-efficient wireless networks. This paper considers an RIS-assisted downlink non-orthogonal-multiple-access (NOMA) system. To optimize the rate performance and ensure user fairness, we maximize the minimum decoding signal-to-interference-plus-noise-ratio (equivalently the rate) of all users, by jointly optimizing the (active) transmit beamforming at the base station (BS) and the phase shifts (i.e., passive beamforming) at the RIS. A combined-channel-strength based user-ordering scheme for NOMA decoding is first proposed to decouple the user-ordering design and the joint beamforming design. Efficient algorithms are further proposed to solve the non-convex problem, by leveraging the block coordinated descent and semidefinite relaxation (SDR) techniques. For the single-antenna BS setup, the optimal power allocation at the BS and the asymptotically optimal phase shifts at the RIS are obtained in closed forms. For the multiple-antenna BS setup, it is shown that the rank of the SDR solution of the transmit beamforming design is upper bounded by two. Also, the proposed algorithms are analyzed in terms of convergence and complexity. Simulation results show that the RIS-assisted NOMA system can enhance the rate performance significantly, compared to traditional NOMA without RIS and traditional orthogonal multiple access with/without RIS.

Generalized User Grouping in NOMA: An Overlapping Perspective

Non-orthogonal multiple access (NOMA) is regarded as a promising technology to provide high spectral efficiency and support massive connectivity in 5G systems. Traditionally, NOMA user grouping is non-overlapping, leading to a waste of power resources within each NOMA group. Motivated by this, in this paper we propose a novel generalized user grouping (GuG) concept for NOMA from an overlapping perspective, which allows each user to participate in multiple user groups but subject to individual maximum power constraint. In order to achieve effective GuG and maximize the system sum rate, we formulate a joint power control and GuG optimization problem. Then we further provide a machine learning-based GuG scheme to obtain the optimized feasible GuG and the optimal power control solutions efficiently, in which the established machine learning-based model is exploited to explore the relative relationships of channel gains of users and obtain several fixed grouping patterns via Merge operation. Simulation results verify the efficiency of GuG in NOMA systems and indicate that compared with traditional NOMA user grouping schemes, our proposed GuG scheme achieves significant performance gains in terms of system sum rate.

Secrecy outage probability analysis for RIS‐assisted NOMA systems

In this Letter, the physical layer security for a novel reconfigurable intelligent surface (RIS)-assisted non-orthogonal multiple access (NOMA) system in a multi-user scenario is investigated, where the authors consider the worst case that the eavesdropper also utilises the advantage of the RISs. More specifically, they derive analytical results for the secrecy outage probability (SOP). From the numerical results, they observe that the use of RISs can improve the secrecy performance compared to traditional NOMA systems. However, for the worst case that the received signals at the eavesdropper come from the RISs and source, increasing the number of intelligent elements on the RIS has a negative impact on the secrecy performance. At high signal-to-noise ratios, the system's SOP tends to a constant. Finally, the secrecy performance can be improved through group selection.

Generalized User Grouping in NOMA Based on Overlapping Coalition Formation Game

Non-orthogonal multiple access (NOMA) is regarded as a promising technology to provide high spectral efficiency and support massive connectivity in 5G systems. In most existing NOMA user grouping approaches, users are grouped into disjoint groups, which may lead to a waste of power resources within each NOMA group. Motivated by this, in this paper we propose a novel generalized user grouping (GuG) concept for NOMA from an overlapping perspective, which allows each user to participate in multiple groups but subject to individual maximum power constraint. In order to achieve effective GuG and maximize the system sum rate, we formulate a joint power control and GuG optimization problem. Then, we address this problem by exploiting the overlapping coalition formation (OCF) game framework, and we further propose an OCF-based algorithm in which each user can be self-organized into a desirable overlapping coalition structure. Simulation results verify the efficiency of GuG in NOMA systems and indicate that compared with traditional NOMA user grouping schemes, our proposed OCF-based GuG NOMA scheme achieves significant performance gains in terms of system sum rate.