An Energy-Efficient Resource Access Scheme in NOMA Heterogeneous Wireless Communication Systems

N. Keshava; M. Ramesha

doi:10.48084/etasr.11975

Authors

N. Keshava Department of EECE, GITAM (Deemed to be University), Bengaluru Campus, India | Department of ECE, Cambridge Institute of Technology North Campus, VTU, India
M. Ramesha Department of Electrical, Electronics and Communication Engineering, GITAM (Deemed to be University), Bengaluru Campus, India

Volume: 15 | Issue: 6 | Pages: 29165-29172 | December 2025 | https://doi.org/10.48084/etasr.11975

Received: 7 May 2025 | Revised: 10 August 2025 | Accepted: 22 August 2025 | Online: 8 December 2025

Corresponding author: N. Keshava

Abstract

This research explores the potential of Multi-Input Multi-Output Non-Orthogonal Multiple Access (MIMO-NOMA) within the framework of 5G and 6G wireless communication networks. Recognizing the critical role of NOMA in enhancing connectivity and spectral efficiency, this study focuses on addressing the challenge of resource allocation in MIMO-NOMA environments. To optimize throughput while minimizing energy consumption, a novel State-Action Game-based Energy Efficient Resource Allocation Optimization (SAG-EERAO) model is introduced. This model effectively accommodates real-world challenges such as imperfect Channel State Information (CSI), mobility, and dynamic throughput requirements. The proposed SAG-EERAO framework is assessed within an urban-highway interference environment, demonstrating its robustness and adaptableness. Comparative performance evaluations indicate that SAG-EERAO surpasses existing approaches by significantly reducing energy consumption, improving spectrum utilization efficiency, and enhancing throughput. This study provides innovative perspectives and practical advancements in the deployment and optimization of MIMO-NOMA, underscoring the effectiveness of SAG-based methodologies for dynamic and efficient resource management in next-generation wireless communication systems.

Keywords:

Deep Reinforcement Learning (DRL), energy efficiency, game theory, Multi-Input Multi-Output (MIMO), Non-Orthogonal Multiple Access (NOMA), power optimization, resource access, State-Action Game (SAG)

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