Joint Transmission Scheduling and Power Allocation in Non-Orthogonal Multiple Access

Nonorthogonal multiple access (NOMA) can effectively improve the spectral efficiency by exploiting the power difference and employing successive interference cancellation (CSI) at receivers. In NOMA, the power allocation is crucial and can be performed with known CSI. It is also possible to carry out the power allocation with statistical CSI to meet target outage probabilities for given transmission rates. In this paper, we consider joint rate and power allocation to minimize the total transmission power with individual throughput constraints in NOMA when statistical CSI is available. An approach to find the optimal solution to the joint rate and power allocation problem is proposed. Based on simulation results, it is shown that NOMA can have a much lower total transmission power than OMA for given target throughput of each user.

This paper investigates the application of nonorthogonal multiple access (NOMA) in millimeter-Wave (mmWave) communications to address resource allocation issues for multiuser downlink transmission. Inspired by the K-means clustering algorithm, a NOMA user grouping policy is first employed in accordance with the channel correlation between users. The first-stage decoding order for each NOMA group is then determined by merely utilizing the users’ channel gain to formulate a joint power allocation and hybrid beamforming optimization problem. Specifically, we use signal-to-leakage-plus-noise ratio (SLNR) as the performance index of the optimization problem to reduce the computational complexity, because it can decouple the design of the beamforming and power allocation issues so they can be executed iteratively. Under given beamforming matrix, the power allocation issue is expressed as a quadratic programming (QP) problem, and then transformed into a convex problem by introducing an auxiliary-positive real variable to solve through the Lagrange multiplier method. On the contrary, the beamforming optimization problem is non-convex and very difficult to solve due to the constant modulus constraints in the hybrid architecture. However, the optimum solution of the ideal full-digital beamforming can be acquired primarily by generalized eigenvalue decomposition (GED). In this condition, two hybrid beamforming algorithms are proposed, in which normalized phase matching and equal gain transmission are performed respectively in the analog domain. On this basis, a second-stage decoding order can be employed according to the effective channel gain of users to perform successive interference cancellation (SIC) successfully. Numerical results show that the proposed joint power allocation and hybrid beamforming algorithms are superior to the classical mmWave-NOMA and orthogonal multiple access (OMA) schemes not only in lower implementation complexity, but also in better spectrum efficiency and energy efficiency performance.

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

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.

In a wireless cellular system, resource allocation among active users is a challenging task. The cellular systems with nonorthogonal multiple access (NOMA) solve the problem of bandwidth and time allocation. All the active users in the same NOMA cluster are allowed to access the complete cluster bandwidth at the same time. However, power allocation in NOMA is a multiconstraint optimization problem. The complexity of power allocation increases with the increase in the number of users inside the cluster. This article presents a novel technique for power allocation in a NOMA downlink cluster, using the magic sum of the magic matrix. The sum throughput (SUMTP) and the min–max fairness index (MMFI) are calculated and compared with an existing method for the downlink NOMA system. The proposed method shows comparable SUMTP performance to the conventional optimized method while ensuring better MMFI. We observed an inverse association between SUMTP and MMFI. In addition, the execution time of the proposed method is much lesser than that of the conventional optimized method owing to lower number of flops required for magic-matrix-based power allocation.

In this paper, we investigate the user scheduling and power allocation scheme for a millimeter wave non-orthogonal multiple access system. To reduce the feedback overhead, random beamforming is adopted at a base station. The optimization problem is formulated to maximize the energy efficiency. To solve this problem, we first address the user scheduling and power allocation problem separately, then an iterative algorithm is proposed to jointly optimize the user scheduling and power allocation. Simulation results show that the proposed scheme achieves higher energy efficiency than the conventional scheme.

Non-orthogonal multiple access (NOMA) is one of the leading technologies for 5G communication. User pairing (UP) and power allocation (PA) are the key controlling mechanisms for the optimization of the performance of NOMA systems. This paper presents a novel UP and PA (UPPA) technique for capacity and fairness maximization in NOMA called (CFM-UPPA). The impact of the power allocation coefficient and the ratio between the channel gains of the paired users on the sum-rate capacity and the fairness in NOMA is firstly investigated. Then, based on this investigation, the PA and UP algorithms of the CFM-UPPA technique are proposed. The power allocation coefficient of the proposed PA is formulated as an exponentially decaying function of the ratio between the channel gains of the paired users to maximize the capacity and the fairness, and its maximum value is adjusted to guarantee the successive interference cancellation (SIC) constraints. The proposed UP is based on selecting the user that has the highest channel gain per subcarrier as the strong user to maximize the capacity and selecting the user that has the closest lower channel gain to the strong user’s channel gain as the weak user to improve the fairness and capacity. The performance evaluation of the proposed CFM-UPPA technique in terms of capacity, fairness, and outage probability demonstrates that its performance significantly outperforms that of the orthogonal multiple access (OMA) system and that of the NOMA system with random UP. Also, the simulation results demonstrate the efficiency of the proposed PA in improving the performance of other UP algorithms, such as the random UP algorithm.

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.

This paper investigates the optimal resource allocation of a downlink non-orthogonal multiple access (NOMA) system consisting of one base station and multiple users. Unlike existing short-term NOMA designs that focused on the resource allocation for only the current transmission timeslot, we aim to maximize a long-term network utility by jointly optimizing the data rate control at the network layer and the power allocation among multiple users at the physical layer, subject to practical constraints on both the short-term and long-term power consumptions. To solve this problem, we leverage the recently-developed Lyapunov optimization framework to convert the original long-term optimization problem into a series of online rate control and power allocation problems in each timeslot. The power allocation problem, however, is shown to be non-convex in nature and thus cannot be solved with a standard method. However, we explore two structures of the optimal solution and develop a dynamic programming based power allocation algorithm, which can derive a globally optimal solution, with a polynomial computational complexity. Extensive simulation results are provided to evaluate the performance of the proposed joint rate control and power allocation framework for NOMA systems, which demonstrate that the proposed NOMA design can significantly outperform multiple benchmark schemes, including orthogonal multiple access (OMA) schemes with optimal power allocation and NOMA schemes with non-optimal power allocation, in terms of average throughput and data delay.

International audience

With the transformation of modern medical care towards a prevention-centred model and the growing demand for high-density sensor deployments in wireless body area networks (WBANs), traditional orthogonal multiple access (OMA) schemes face critical limitations in spectral efficiency and energy sustainability. Non-orthogonal multiple access (NOMA) combined with radio frequency energy harvesting (RFEH) emerges as a promising solution, yet existing research often overlooks the strict energy causality constraints of sensors. To address this issue, this paper proposes a novel joint optimization framework for NOMA-assisted WBANs, which strategically pairs predefined sensor groups (classified by body positions) into NOMA clusters and optimizes power allocation while accounting for harvested RF energy. The proposed sorted-greedy joint algorithm enhances successive interference cancellation (SIC) efficiency by pairing sensor groups with the largest channel gain differences, followed by a greedy power allocation strategy that maximizes throughput under energy and power constraints. Simulation results demonstrate that the proposed algorithm outperforms conventional greedy and random clustering approaches in total system throughput across varying numbers of sensor groups, sensors per group, and transmission powerlevels.

The power-domain nonorthogonal multiple access (NOMA) based on successive interference cancellation (SIC) provides opportunities for fast media access in industrial wireless networks. In this article, given the traffic loads of wireless sensors, we study the shortest uplink scheduling (SUS) problem by joint power allocation and wireless sensor (WS) scheduling. A key term named maximum decoded level (MDL), which models the transmitting characteristics of WSs under SIC, and thus, lays the foundations for revealing a sufficient and necessary condition for successive transmissions under SIC, is presented in the first step. Then, guided by the theoretical condition that decouples WS scheduling from power allocation, we present a two-step greedy algorithm for the SUS problem in the case of continuous transmit powers. We also prove that the proposed algorithm is optimal for two regular cases. One is for any traffic loads under 2-SIC, the other is for unit traffic load under k-SIC. Furthermore, in the case of discrete transmit powers, we further propose an optimal algorithm under 2-SIC and an approximation algorithm under k-SIC by adapting the above-mentioned greedy algorithm for the case of discrete transmit powers. Experimental evaluations reveal the effectiveness of the three algorithms.

Most existing optimization objectives considered in non-orthogonal multiple access (NOMA) power allocation schemes are non-delay-sensitive metrics. In order to apply NOMA to various Internet of Things scenarios, the delay must be considered. The effective capacity of users, which characterizes the capacity under specific expiration probabilities, can potentially be a performance metric of statistical delay quality of service (QoS). In this paper, we propose two novel dynamic power allocation schemes with statistical delay QoS guarantee in the uplink NOMA system with paired users. One of the schemes maximizes the sum effective capacity (SEC) of the strong and weak users, which is a non-convex nonlinear optimization problem and is solved by Lagrangian dual decomposition and successive convex approximation (SCA). The other one maximizes the effective energy efficiency (EEE) of uplink NOMA, which is a fractional optimization problem and is solved by integrating the Dinkelbach method, SCA, and Lagrangian dual decomposition. Numerical results show that the SEC and EEE can be significantly improved by the proposed schemes, compared to the existing NOMA and orthogonal multiple access power allocation schemes.

The increasing number of consumers with diverse data rate needs is leading to increased heterogeneity in traditional cellular networks. Nonorthogonal multiple access (NOMA) has emerged as a promising method to serve a large number of users, but research shows that weak users (WU) and strong users (SU) have different throughputs. Intra-group interference reduces WUs throughput due to the superposition of signals. Improper power distribution impacts NOMA performance and lowers the total system rate. The multi-objective sum rate dwarf mongoose optimization algorithm (M-SRDMOA) is implemented as a solution to the NOMA network power allocation problems. The DMOA approach distributes adequate power to all NOMA users to increase the large sum rate. The effectiveness of the M-SRDMOA approach is supported by existing studies on fair NOMA scheduler (FANS) and multi-objective sum rate-based butterfly optimization algorithm (M-SRBOA). The M-SRDMOA’s potential sum rate with an SNR of 9dB and a noise variation=2 is 14.06 bps/Hz, which is high compared to M-SRBOA and FANS.

Non-orthogonal multiple access (NOMA) provides higher spectral efficiency and access to more users than orthogonal multiple access. However, the issue of resource allocation in NOMA is dynamic and produces a high computation burden when using traditional methods. In this paper, a symmetry-aware double deep Q network (DDQN) algorithm in deep reinforcement learning is employed to allocate power to users in NOMA while guaranteeing quality of service for the weakest users. The research process is divided into two parts. Firstly, users in the communication system are grouped using a method that synergistically considers gain difference and similarity, exploiting symmetrical properties within the user groups. Secondly, the DDQN algorithm is used to allocate power to multiple users in a NOMA system, which utilizes the inherent symmetry in the signal-to-interference noise ratio of each user as an objective function. By recognizing and leveraging these symmetrical patterns, the algorithm can dynamically adjust the power allocation to optimize system performance. Finally, the proposed algorithm is compared with conventional NOMA power allocation algorithms and shows significant improvements in system performance. The results of the convergence function show that the algorithm proposed in this paper can converge in approximately 1800 iterations, which effectively solves the problem of large arithmetic and complex processes existing in the traditional method.