Power and Channel Allocation for Non-orthogonal Multiple Access in 5G Systems: Tractability and Computation

Network capacity calls for significant increase for 5G cellular systems. A promising multi-user access scheme, non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC), is currently under consideration. In NOMA, spectrum efficiency is improved by allowing more than one user to simultaneously access the same frequency-time resource and separating multi-user signals by SIC at the receiver. These render resource allocation and optimization in NOMA different from orthogonal multiple access in 4G. In this paper, we provide theoretical insights and algorithmic solutions to jointly optimize power and channel allocation in NOMA. For utility maximization, we mathematically formulate NOMA resource allocation problems. We characterize and analyze the problems' tractability under a range of constraints and utility functions. For tractable cases, we provide polynomial-time solutions for global optimality. For intractable cases, we prove the NP-hardness and propose an algorithmic framework combining Lagrangian duality and dynamic programming (LDDP) to deliver near-optimal solutions. To gauge the performance of the obtained solutions, we also provide optimality bounds on the global optimum. Numerical results demonstrate that the proposed algorithmic solution can significantly improve the system performance in both throughput and fairness over orthogonal multiple access as well as over a previous NOMA resource allocation scheme.Comment: IEEE Transactions on Wireless Communications, revisio

Deep Learning Technique for Power Domain Non-Orthogonal Multiple Access Using Optimised LSTM in Cooperative Networks

Non-orthogonal Multiple Access (NOMA) is the technique proposed for multiple accesses in the fifth-generation (5G) cellular network. In NOMA, different users are allocated different power levels and are served using the same time/frequency Resource Blocks (RBs). The main challenges in existing NOMA systems are the limited channel feedback and the difficulty of merging them with advanced adaptive coding and modulation schemes. The 5G system in NOMA aims to access low latency, efficiency in superior spectra, and balanced user fairness. NOMA allows multiple users with different power levels to share resources in radio frequency time. The existing Orthogonal Multiple Access (OMA) system produces high latency, high computational complexity, and throughput complexity in modifying wireless channels. To overcome these issues, this paper proposed optimising deep learning-based power domain NOMA of Long Short-Term Memory (LSTM) with particles Swarm optimisation (PSO) technique. This proposed work (LSTM-PSO) is deployed with a Cooperative network model. The advantage of LSTM-PSO in Cooperative Non-orthogonal Multiple Access (CNOMA) is that it provides high performance, better utilisation of downlink, efficiency in sharing of resources, enhancing the activity of users, capacity of the base station and improving quality of service, estimation of channel condition. LSTM-PSO got a higher accuracy rate of 92.05%, LSTM got 86.45%, PSO got 88.13%, and the accuracy rate of ANN and DNN was 83.76% and 84.70%

Beamforming Techniques for Non-Orthogonal Multiple Access in 5G Cellular Networks

In this paper, we develop various beamforming techniques for downlink transmission for multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) systems. First, a beamforming approach with perfect channel state information (CSI) is investigated to provide the required quality of service (QoS) for all users. Taylor series approximation and semidefinite relaxation (SDR) techniques are employed to reformulate the original non-convex power minimization problem to a tractable one. Further, a fairness-based beamforming approach is proposed through a max-min formulation to maintain fairness between users. Next, we consider a robust scheme by incorporating channel uncertainties, where the transmit power is minimized while satisfying the outage probability requirement at each user. Through exploiting the SDR approach, the original non-convex problem is reformulated in a linear matrix inequality (LMI) form to obtain the optimal solution. Numerical results demonstrate that the robust scheme can achieve better performance compared to the non-robust scheme in terms of the rate satisfaction ratio. Further, simulation results confirm that NOMA consumes a little over half transmit power needed by OMA for the same data rate requirements. Hence, NOMA has the potential to significantly improve the system performance in terms of transmit power consumption in future 5G networks and beyond.Comment: accepted to publish in IEEE Transactions on Vehicular Technolog

Design and Optimization of Scheduling and Non-orthogonal Multiple Access Algorithms with Imperfect Channel State Information

Non-orthogonal multiple access (NOMA) is a promising candidate technology for 5G cellular systems. In this paper, design and optimization of scheduling and NOMA algorithms is investigated. First, the impact of power allocation for NOMA systems with round-robin scheduling is analyzed. A statistic model is developed for network performance analysis of joint scheduling of spectrum resource and power for NOMA algorithms. Then, proportional fairness (PF) scheduling for NOMA algorithms is proposed with a two-step approach, with its objectives to ensure low computational complexity, high throughput, and user fairness. In the first step, an optimal power allocation strategy is developed with an objective maximizing weighted sum rate. In the second step, three fast and scalable scheduling and user pairing algorithms with QoS guarantee are proposed, in which only a few user pairs are checked for NOMA multiplex. The algorithms are extended to the cases with imperfect channel state estimation and more than two users being multiplexed over one resource block. Numerical results show that the proposed algorithms are significantly faster and more scalable than the existing algorithms, and can maintain a higher throughput gain than orthogonal multiple access

On Short Term Fairness and Throughput of User Clustering for Downlink Non-Orthogonal Multiple Access System

Non-orthogonal multiple access (NOMA) with power-domain user multiplexing has been considered as one of the potential candidates of the fifth-Generation (5G) systems. In this paper, a two-stage user selection algorithm is proposed based on proportional fairness for downlink NOMA with zero-forcing beamforming (PF-NOMA-ZFBF) in order to improve the throughput-fairness trade-off for NOMA system. We focus on short term fairness, where short term refers to the minimum time window in which a specified fairness is guaranteed and evaluated using Jain's index. A transmit power allocation then applied to enhance the throughput of NOMA system. Simulation results show that the proposed PF-NOMA-ZFBF significantly improves user fairness index with at least 38.96% over conventional NOMA-ZFBF while maintaining the total system sum-rate (near maximum)

5G NOMA user grouping using discrete particle swarm optimization approach

Non-orthogonal multiple access (NOMA) technology meets the increasing demand for high-seed cellular networks such as 5G by offering more users to be accommodated at once in accessing the cellular and wireless network. Moreover, the current demand of cellular networks for enhanced user fairness, greater spectrum efficiency and improved sum capacity further increase the need for NOMA improvement. However, the incurred interference in implementing NOMA user grouping constitutes one of the major barriers in achieving high throughput in NOMA systems. Therefore, this paper presents a computationally lower user grouping approach based on discrete particle swarm intelligence in finding the best user-pairing for 5G NOMA networks and beyond. A discrete particle swarm optimization (DPSO) algorithm is designed and proposed as a promising scheme in performing the user-grouping mechanism. The performance of this proposed approach is measured and demonstrated to have comparable result against the existing state-of-the art approach

A Novel Approach to Fair Power Allocation for NOMA in Visible Light Communication

This paper addresses the growing demand for high-bandwidth wireless data transmission by exploring Visible Light Communication (VLC) as an alternative to Radio Frequency (RF) communication. In indoor scenarios, VLC systems utilize existing lighting infrastructure for high-speed data transmission. To meet the data rate demands of 5G and beyond, the paper proposes Non-Orthogonal Multiple Access (NOMA) and introduces Empirical Fair Optical Power Allocation (EFOPA) to simplify resource allocation in NOMA. EFOPA integrates NOMA with VLC, utilizing the Artificial Bee Colony (ABC) optimization algorithm for offline resource allocation planning. The approach then derives a simplified power allocation equation from ABC outcomes, ensuring fair resource distribution among users. EFOPA is compared against existing power allocation methods, demonstrating superior fairness and reduced computational complexity. Numerical evaluations reveal EFOPA consistently outperforms other methods across various channel conditions, making it a robust and efficient solution for fair power allocation in NOMA-VLC systems