Power Allocation For Non-orthogonal Multiple Access Research Articles

Spectrally Efficient UAV Communications using Non-Orthogonal Multiple Access (NOMA)

Non-orthogonal multiple access (NOMA) scheme with its share of benefits such as spectral efficiency and simultaneous support of massive connectivity, is one of the keys enabling technologies for upcoming 5G. However, in the context of UAV-assisted communication, existing literature mainly focuses on conventional orthogonal multiple access (OMA) which limits the spectral efficiency and number of simultaneous connections. Existing technologies are using LTE that only can serve 1 user per resource block. The user capacity nowadays is increased, so the existing technologies should be improved to higher user capacity such as 4 users per resource block. This research project focuses on the design of NOMA scheme for multiple users aiming to maximize the system capacity and spectral efficiency of Unmanned Aerial Vehicle (UAV) hotspot with baseline model and to simultaneously support mobile users. The proposed NOMA multi-users model is then compared with baseline model of 2 users per resource block in NOMA and OMA. The proposed NOMA model increases spectral efficiency, higher throughput and improved user fairness. The project only focuses on downlink communication with stationary users. The parameters measured are Bit Error Rate, Sum Rate, and Spectral Efficiency that depend on power allocation, altitude of UAV, and distance between UAV and users. Based on the simulation results, NOMA which employs Fractional Transmit Power Allocation (FTPA) algorithm has better performance in terms of sum-rate and spectral efficiency compared to those of OMA by as much as 650%. Results are also presented for various NOMA power allocation algorithms.

Covert performance enhancement in NOMA enabled CoMP network with optimal RIS selection

In this paper, we consider the covert rate maximization problem for the cell edge user in a reconfigurable intelligent surface (RIS) aided downlink non-orthogonal multiple access (NOMA) network with coordinated multipoint (CoMP) transmission in the presence of channel estimation errors. We derive the analytical expression for the detection error probability (DEP) at the adversary (Willie) assuming the worst case scenario where they employ optimal detection threshold. With the derived analytical expression, we propose an optimal RIS selection scheme to boost the covert rate. To further improve the covert performance, we optimize the NOMA power allocation coefficients and phase shifts of the selected RIS subject to the target rate and covertness constraints. Simulation results demonstrate the improvement in covert rate and robustness offered by the proposed scheme in the presence of channel estimation errors over other benchmarks.

Power Allocation for NOMA With Cache-Aided D2D Communication

Communication networks are becoming increasingly content-centric, and reusable content like viral information and video on demand are often requested by multiple users. Caching at the user equipment enables device-to-device (D2D) communications to be used to deliver requested contents which will help reduce data traffic at base stations and also enhance the achievable data rates. In this paper, we propose a system whereby users are able to exchange valuable cached content with each other via a D2D link which underlays the reception of a downlink non-orthogonal multiple access (NOMA) signal. We formulated a sum rate maximization problem that is subject to minimum rate constraints and derived optimal solutions depending on which user is the D2D transmitter. Additionally, sub-optimal solutions based on a negligible self-interference assumption are also proposed. Simulation results demonstrate the significant performance gains of underlaid D2D communications as compared with optimal downlink cellular NOMA. Furthermore, results on the self-interference (SI) cancellation factor highlight that the sub-optimal power allocation solution offers sum rate performance close to the optimal case. The performance gains are further enhanced when SI cancellation is high.

Effect of User Mobility under Rician Fading on Power Allocation for Non-Orthogonal Multiple Access (PA-NOMA) Strategy

Effect of User Mobility under Rician Fading on Power Allocation for Non-Orthogonal Multiple Access (PA-NOMA) Strategy

User Pairing and Power Allocation in Untrusted Multiuser NOMA for Internet of Things

In the Internet-of-Things (IoT), massive sensitive and confidential information is transmitted wirelessly, making security a serious concern. This is particularly true when technologies, such as non-orthogonal multiple access (NOMA), are used, making it possible for users to access each other’s data. This paper studies secure communications in multiuser NOMA downlink systems, where each user is potentially an eavesdropper. Resource allocation is formulated to achieve the maximum sum secrecy rate, meanwhile satisfying the users’ data requirements and power constraint. We solve this non-trivial, mixed-integer non-linear programming problem by decomposing it into power allocation with a closed-form solution, and user pairing obtained effectively using linear programming relaxation and barrier algorithm. These subproblems are solved iteratively until convergence, with the convergence rate rigorously analyzed. Simulations demonstrate that our approach outperforms its existing alternatives significantly in the sum secrecy rate and computational complexity.

Jointly Optimized Beamforming and Power Allocation for Full-Duplex Cell-Free NOMA in Space-Ground Integrated Networks

Space-ground integrated networks (SGINs) have attracted substantial research interests due to their wide area coverage capability, where spectrum sharing is employed between the satellite and terrestrial networks for improving the spectral efficiency (SE). We further improve the SE by conceiving a cell-free system in SGINs, where the full-duplex (FD) multi-antenna APs simultaneously provide downlink and uplink services at the same time and within the same frequency band. Furthermore, power domain (PD) non-orthogonal multiple access (NOMA) is employed as the multiple access (MA) technique in the cell-free system. To achieve a performance enhancement, the sum-rate maximization problem is formulated for jointly optimizing the power allocation factors (PAFs) of the NOMA downlink (DL), the uplink transmit power, and both the beamformer of the satellite and of the APs. Successive convex approximation (SCA) and semi-definite programming (SDP) are adopted to transform the resultant non-convex problem into an equivalent convex one. Our simulation results reveal that 1) our proposed system outperforms the well-known approaches (i.e., frequency division duplex (FDD) and small cell systems) in terms of its SE; 2) our proposed optimization algorithm significantly improves the networking performance; 3) the conceived SIC order design outperforms the fixed-order design at the same complexity.

Minimizing Delay for MIMO-NOMA Resource Allocation in UAV-Assisted Caching Networks

As one of the important enabling techniques for 6 G, wireless caching network (WCN) attracts significant attentions. In this paper, we jointly apply unmanned aerial vehicle (UAV), millimeter wave (mmWave) multiple input multiple output (MIMO) and non-orthogonal multiple access (NOMA) in WCN. Our aim is to minimize the user delay, which is decomposed into three sub-problems, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , UAV deployment, hybrid beamforming and power allocation. Firstly, to improve the user rate, we apply K-means to reduce the distance between UAVs and users and propose a user pairing method to maintain the channel gain gap in each pair. Then, for increasing UAV hit probability, particle swarm optimization (PSO) and zero forcing are used for analog beamforming and digital beamforming, respectively. Finally, to further improve the user data rate, the genetic algorithm (GA) is applied to calculate the optimal NOMA power allocation factors. Simulation results confirm that the proposed schemes can achieve lower user delay compared with baseline schemes.

Multiuser NOMA With Multiple Reconfigurable Intelligent Surfaces for Backscatter Communication in a Symbiotic Cognitive Radio Network

In this paper, we develop an optimization framework for the symbiotic operation of a multiuser cognitive radio network (CRN) consisting of a non-orthogonal multiple access (NOMA)-based primary network (PN) and a reconfigurable intelligent surface (RIS)-based secondary network (SN) sharing the same spectrum. In the symbiotic relationship formed in this system model, the RIS devices' PN signal backscattering not only provides multiple primary receivers with a spatial diversity gain but also supports the transmission of the RIS devices' own data to their designated receiver. First, through investigation of a simple network configuration, we obtain insights into the impacts of various factors on the system performance, and thus, into the importance of radio resource allocation for CRN symbiotic operation. Then, we develop a resource allocation framework that maximizes the sum rate of the PN and SN by jointly optimizing the system resources for cases of devices with hardware imperfections. To reduce the complexity of solving this problem, we convert it into a convex weighted minimum mean square error problem. An iterative algorithm for finding the optimal NOMA power allocation for the PN and the reflection coefficients of the RISs is developed, and its overhead is analyzed. Simulation results demonstrate that the proposed algorithm outperforms various benchmarks.

On the achievable capacity of RIS-NOMA networks: A perspective on elements number

In this paper, a novel reconfigurable intelligent surface (RIS) assisted cooperative network with non-orthogonal multiple access (NOMA) is analyzed, considering the fairness of users. Specifically, the influence of RIS elements number to the system performance is studied, i.e., the power allocation of NOMA and sum-rate. Since the exact closed-form expression of the RIS elements number N is difficult to be obtained, we turn to study the equivalent sum-rates for our proposed two decoding methods at the high signal-to-noise ratio (SNR) region to analyze approximate solution of N, recalled this result as “equivalent point”. The simulation results show that, when N is less than the equivalent point, the sum-rate obtained by the first decoding method is greater. On the contrary, when N is greater than this equivalent point, the sum-rate obtained by the second decoding method is higher.

Throughput Optimization for Noma Energy Harvesting Cognitive Radio With Multi-UAV-Assisted Relaying Under Security Constraints

This paper investigates the throughput of a non-orthogonal multiple access (NOMA)-based cognitive radio (CR) system with multiple unmanned aerial vehicle (UAV)-assisted relays under system performance and security constraints. We propose a communication protocol that includes an energy harvesting (EH) phase and multiple communication phases. In the EH phase, the multiple UAV relays (URs) harvest energy from a power beacon. In the first communication phase, a secondary transmitter (ST) uses the collected energy to send confidential signals to the first UR using NOMA. Simultaneously, a ground base station communicates with a primary receiver (PR) under interference from the ST. In the subsequent communication phases, the next URs apply the decode-and-forward technique to transmit the signals. In the last communication phase, the Internet of Things destinations (IDs) receive their signals in the presence of an eavesdropper (EAV). Accordingly, the outage probability of the primary network, the throughput of the secondary network, and the leakage probability at the EAV are analyzed. On this basis, we propose a hybrid search method combining particle swarm optimization (PSO) and continuous genetic algorithm (CGA) to optimize the UR configurations and the NOMA power allocation to maximize the throughput of the secondary network under performance and security constraints.

Analysis of Fair-NOMA Power Allocation: a Comparative Study Between NOMA and OMA Systems

Non-Orthogonal Multiple Access (NOMA) system has been very interesting technique recommended for the fifth generation (5G) of wireless mobile communication networks. The strategy of a suitable multiple access technique is one of the most important aspects in improving the system capacity. Multiple access techniques can broadly be categorized into two different approaches, namely: OMA and NOMA. In this paper a downlink communication system composed of a base station and M users where each one is equipped with a single antenna is considered, and multiple users system are characterized by multipath communication known as Rayleigh fading channel which can be described by a complex fading channel coefficient. It is well known that NOMA fairness feature implies that, with NOMA approach, near and far mobile users from a base station are able to achieve at least the possible information capacity similarly to OMA users. Fair-NOMA is described by fairness index (F) and a power allocation coefficient (a) limiting the fairness region R bounded by extreme values amin and amax, this fairness index is given as a function of NOMA power allocation coefficient, SNR ratio and channel gains of NOMA users. Maximum fairness index is obtained for a power allocation coefficient belonging to the fairness region, which is large for weak SNR ratios, and slight for high levels. In addition, the calculation of NOMA and OMA ergodic capacities at fairness bounds, indicates that NOMA approach always outperforms OMA one. Further, fairness index evolution is considered in function of channel gains, power allocation coefficients and SNR ratio levels, giving as result, optimal fairness can be reached by a judicious power allocation to the furthest user from the base station.

Energy-Efficient Pairing and Power Allocation for NOMA UAV Network Under QoS Constraints

Due to the increasing number of unmanned aerial vehicles (UAVs) with generally limited battery power, energy-efficient data transmission schemes with massive connectivity capabilities are required for future wireless networks. Nonorthogonal multiple access (NOMA) is a promising technique that provides such massive connectivity by allowing superposed data transmission of multiple users over the same resource block. This article presents a user pairing and power allocation technique for energy efficiency and Quality-of-Service (QoS)-based NOMA transmission for cellular-connected mobile UAVs and ground users (GUs) in a NOMA UAV network. The aim is to minimize the energy consumption of both aerial users (AUs) and GUs during uplink data transmission and guarantee their required transmission rates through efficient pairing and power optimization. Therefore, a joint pairing and power optimization problem is formulated as a nonconvex optimization problem under QoS constraints. Furthermore, a suboptimal solution is proposed to show that the proposed energy-efficient pairing technique effectively fulfills the QoS requirements of both GUs and AUs in a network with low complexity. The simulation results of the proposed technique show the performance gain in terms of energy efficiency, outage probability, and pairing complexity compared to the conventional channel condition and power-consumption-based NOMA pairing and power allocation techniques.

Deep Learning-Based Joint NOMA Signal Detection and Power Allocation in Cognitive Radio Networks

Presently, Non-Orthogonal Multiple Access (NOMA) frequently uses Successive Interference Cancellation (SIC) with channel estimation to detect the receivers’ signal successfully. However, it’s a slow and sophisticated process. Consequently, the decoding precision of previous Primary Users (PUs) / Secondary Users (SUs) is degraded due to error propagation. These shortcomings are mitigated by the proposed Deep Learning (DL) schemes for signal detection and Power Allocation (PA) in NOMA-based futuristic Cognitive Radio Networks (CRNs). Hence, proposed technique is optimized jointly and determines the desired solution in one step without channel estimation through skilled Deep Neural Network (DNN) approach. It can understands the things because it is the core (so-called heart) of DL, which integrates user detection with channel estimation and PA. The optimization problem for fair PA is planned by combining proposed technique for optimizing systems threshold throughput. In addition, DNN training algorithms is also analyzed, and used to retrieve users’ information from the aired signal directly during the testing stage. The real-time experimental results and analytic outcomes are investigated and validate the supremacy of the proposed scheme over prevailing techniques, respectively. Furthermore, the robustness of the DL technique is additionally evaluated for its dominance over existing approaches.

Joint NOMA Clustering and Power Allocation in IoRT-Oriented Satellite Terrestrial Relay Networks

In this paper, a joint non-orthogonal multiple access (NOMA) clustering and power allocation problem is studied to maximize the uplink total sum rate in Internet of Remote Things (IoRT)-oriented satellite terrestrial relay networks (STRNs). The joint optimization problem is a mixed-integer programming (MIP) problem, which is non-convex and NP-hard. To solve this problem, we decompose it into two subproblems and propose staged algorithms to solve them. The first subproblem is an optimal NOMA clustering problem, which is still non-convex and NP-hard. To solve the subproblem efficiently, a reinforcement learning-based dynamic clustering algorithm (RL-DCA) is proposed. Using the RL-DCA, the users are able to gradually learn the optimal NOMA clustering policy in a distributed fashion. The RL-DCA has fast convergence and its computational complexity at each user remains fixed for any network size. The second subproblem is an intra-cluster optimal NOMA power allocation problem, which is still non-convex. To solve it, we firstly convert it into a convex problem by constrait approximation, then use the Karush-Kuhn-Tucker (KKT) conditions based algorithm to find the optimal power allocation policy. The second subproblem is solved in an offline fashion, which enables low computational complexity in transmission. Simulation results show that: 1) The RL-DCA has fast convergence and enables good clustering performance; 2) The joint NOMA clustering and power allocation scheme greatly outperforms the orthogonal multiple access (OMA) scheme and other NOMA schemes in terms of total sum rate.

Joint bandwidth and power resource allocation technique in multi‐carrier non‐orthogonal multiple access‐based cognitive Internet of Things networks

AbstractThe recent development of the Internet of Things (IoT) devices offers an intelligent concept for integrating massive number of interconnected wireless devices. However, supporting such massive number of IoT connections (hundreds of billions) requires an efficient and dynamic spectrum access strategy. Accordingly, the integration of non‐orthogonal multiple access (NOMA) in the cognitive radio (CR) systems, referred to as a multi‐carrier NOMA CR‐enabled system, is envisioned as a potential spectrum‐efficient networking paradigm that can support massive energy‐efficient IoT connectivity in B5G networks while maintaining the quality of services (QoS) of the IoT devices. However, the power consumption issue of the multi‐carrier NOMA CR‐based system becomes as one of key challenges, especially when considering the expected massive connectivity. Accordingly, several power‐aware optimization frameworks have been considered to address this issue. While most of the existing CR‐based multi‐carrier NOMA power allocation mechanisms only optimize the allocated per‐user power, this article proposes a novel joint bandwidth and power allocation problem over each available idle channel with the aim of minimizing the overall transmission power under a set of QoS constrains. Specifically, the bandwidth of each channel is adaptively subdivided into two variable‐width sub‐channels, each is capable to serve a group of CR users using power‐domain NOMA. This novel design is formulated as a joint optimization problem that is shown to be a non‐convex. Therefore, an iterative algorithm with a second‐order cone programming is deployed to handle the non‐convexity of the problem, and thus, determine the solution of it. Simulation results reveal that the developed variable bandwidth adaptive power allocation mechanism outperforms the conventional equal sub‐channel allocation in terms of the required transmission power, which significantly improves the energy efficiency in the system.

An MEC-Enabled Framework for Task Offloading and Power Allocation in NOMA Enhanced ABS-Assisted VANETs

This letter investigates the application of non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) in air base station (ABS)-assisted vehicular ad hoc networks (VANETs). A tethered ABS acts as the server to provide MEC services for vehicles. Vehicles adopt NOMA to offload their tasks to the ABS. For the considered network, we formulate an average task processing ratio maximization problem by jointly optimizing the offloading decision, computation resource allocation, and power allocation. For tackling this non-convex problem, we first obtain the offloading decision and the computation resource allocation with the aid of the relax-and-round method and the convex problem solver. Then, the optimal transmission power allocation of vehicles is derived by considering the successive interference cancellation decoding threshold. Finally, the simulation results show that the proposed algorithm has a significant performance improvement in the average task processing ratio compared with the current works in different urban scenarios.

Research on Interference Coordination Optimization Strategy for User Fairness in NOMA Heterogeneous Networks

In order to comprehensively improve the performance of edge users in heterogeneous cellular networks and the fairness of network users, a downlink interference coordination optimization strategy in heterogeneous cellular networks with non-orthogonal multiple access (NOMA) based on the cell range expansion (CRE) and the almost blank subframe (ABS) technology is proposed. Different from the traditional interference coordination strategy, a NOMA user pairing scheme combined with ABS technology and a dynamic NOMA power allocation scheme are designed to maximize the network fairness based on the optimized throughput of the edge users. The simulation results show that the proposed optimization strategy can balance the performance of network users more effectively to improve the throughput of edge users and network fairness than other NOMA user pairing and power allocation algorithms without the complexity being increased.

Joint Resource, Deployment, and Caching Optimization for AR Applications in Dynamic UAV NOMA Networks

The cache-enabling unmanned aerial vehicle (UAV) non-orthogonal multiple access (NOMA) networks for mixture of augmented reality (AR) and normal multimedia applications are investigated, which is assisted by UAV base stations. The user association, power allocation of NOMA, deployment of UAVs and caching placement of UAVs are jointly optimized to minimize the content delivery delay. A branch and bound (BaB) based algorithm is proposed to obtain the per-slot optimization. To cope with the dynamic content requests and mobility of users in practical scenarios, the original optimization problem is transformed to a Stackelberg game. Specifically, the game is decomposed into a leader level user association sub-problem and a number of power allocation, UAV deployment and caching placement follower level sub-problems. The long-term minimization was further solved by a deep reinforcement learning (DRL) based algorithm. Simulation result shows that the content delivery delay of the proposed BaB based algorithm is much lower than benchmark algorithms, as the optimal solution in each time slot is achieved. Meanwhile, the proposed DRL based algorithm achieves a relatively low long-term content delivery delay in the dynamic environment with lower computation complexity than BaB based algorithm.

NOMA-Based Energy-Efficiency Optimization for UAV Enabled Space-Air-Ground Integrated Relay Networks

In this paper, an unmanned aerial vehicle (UAV)-enabledspace-air-ground integrated relay system, in which UAV is equipped with phased-array antennas to receive satellite’s signal while utilizing non-orthogonal multiple access (NOMA) schemes to transmit data to land-based users, is proposed. For UAV signal collection, a detection-vector optimization problem can be addressed by introducing a serial of transformations into a second-order cone programming problem. Furthermore, the UAV-ground NOMA communication is summarized into a max-min problem in terms of UAV’s energy efficiency (EE), which is a fractional mixed-integer non-convex problem and has been widely recognized as hard work. Thus, a two-step solution is proposed. Firstly, we transfer the max-min fractional problem into a subtractive one. After that, the transformed problem is further divided into three sub-problems, which are used for jointly performing UAV trajectory optimization, NOMA scheduling optimization, and NOMA power allocation. In addition, the non-convexity of the above-mentioned problem is well solved by using the proposed iterative algorithm, achieving a rapid convergence. Numerical results illustrate the superiority of the proposed EE-NOMA scheme, which achieves three times higher energy-efficient than the conventional spectrum efficiency scheme, and NOMA is shown to globally outperform the orthogonal multiple access in UAV EE circumstance.

User scheduling and power allocation for downlink multi-cell multi-carrier NOMA systems

In Non-Orthogonal Multiple Access (NOMA), the best way to fully exploit the benefits of the system is the efficient resource allocation. For the NOMA power domain, the allocation of power and spectrum require solving the mixed-integer nonlinear programming NP-hard problem. In this paper, we investigate user scheduling and power allocation in Multi-Cell Multi-Carrier NOMA (MCMC-NOMA) networks. To achieve that, we consider Weighted Sum Rate Maximization (WSRM) and Weighted Sum Energy Efficiency Maximization (WSEEM) problems. First, we tackle the problem of user scheduling for fixed power using Fractional Programming (FP), the Lagrange dual method, and the decomposition method. Then, we consider Successive Pseudo-Convex Approximation (SPCA) to deal with the WSRM problem. Finally, for the WSEEM problem, SPCA is utilized to convert the problem into separable scalar problems, which can be parallelly solved. Thus, the Dinkelbach algorithm and constraints relaxation are used to characterize the closed-form solution for power allocation. Extensive simulations have been implemented to show the efficiency of the proposed framework and its superiority over other existing schemes.