The effect of quantized ETF, grouping, and power allocation on non-orthogonal multiple accesses for wireless communication networks

Abstract

Nonorthogonal multiple access (NOMA) is a significant technology in radio resource sharing and it has been recognized as a favorable method in fifth-generation (5G) wireless networks to meet the requirements of system capacity, service latency, and user connectivity. Many schemes for NOMA have been proposed in the last few years. such as transmitter linear spreading-based NOMA as a code domain, as well as a linear minimum mean square error (LMMSE), parallel interference cancellation (PIC), and serial interference cancellation (SIC) with power allocation and grouping as a power domain at the receiver side for uplink NOMA. This work aims to evaluate the performance of multiple types of linear spreading-based NOMA schemes. Simulations are achieved for the error-rate performance evaluation of these NOMA schemes, received signal after detection, and received signal and effect of every user on the other. Evaluating the performance of these technologies with comparison is also achieved through using grouping and power allocation. Simulations are achieved for the sum rate and spectral efficiency. For the future, 5G NOMA development, an equiangular tight frame (ETF) is suggested for improving performance and suggests grouping with 64qam-quantized Grassmannian for improving performance favorite about grouping with Generalized welch-bound equality (GWBE)

AI-Based Q-Learning Approach for Performance Optimization in MIMO-NOMA Wireless Communication Systems

“…NOMA has emerged as a seminal technology to address these burgeoning challenges. Unlike conventional Orthogonal Multiple Access (OMA) schemes, NOMA enables the concurrent utilization of identical time-fre-quency resources by multiple users, thereby substantially augmenting both SE and system connectivity [3,4]. MIMO-NOMA systems, which integrate MIMO technology into NOMA, further enhance system capacity and efficiencies, providing an advanced solution for next-gen wireless networks [5].…”

“…1. MIMO-NOMA wireless system scheme [4] Reinforcement Learning (RL) offers a promising avenue for addressing the complexities of optimizing MIMO-NOMA systems. RL enables an agent to learn optimal strategies through trial-and-error interactions with the environment, receiving feedback as rewards or punishments.…”