Sikkanan Sobana, Kumar Chandrasekaran, Manoharan Premkumar, Ravichandran Sowmya
Department of Electronics and Communication Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu, 641032, India.
Department of Electrical and Electronics Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu, 641032, India.
Sci Rep. 2024 Sep 13;14(1):21406. doi: 10.1038/s41598-024-71751-2.
Non-orthogonal Multiple Access (NOMA) techniques offer potential enhancements in spectral efficiency for 5G and 6G wireless networks, facilitating broader network access. Central to realizing optimal system performance are factors like joint power control, user grouping, and decoding order. This study investigates power control and user grouping to optimize spectral efficiency in NOMA uplink systems, aiming to reduce computational difficulty. While previous research on this integrated optimization has identified several near-optimal solutions, they often come with considerable system and computational overheads. To address this, this study employed an improved Grey Wolf Optimizer (GWO), a nature-inspired metaheuristic optimization method. Although GWO is effective, it can sometimes converge prematurely and might lack diversity. To enhance its performance, this study introduces a new version of GWO, integrating Competitive Learning, Q-learning, and Greedy Selection. Competitive learning adopts agent competition, balancing exploration and exploitation and preserving diversity. Q-learning guides the search based on past experiences, enhancing adaptability and preventing redundant exploration of sub-optimal regions. Greedy selection ensures the retention of the best solutions after each iteration. The synergistic integration of these three components substantially enhances the performance of the standard GWO. This algorithm was used to manage power and user-grouping in NOMA systems, aiming to strengthen system performance while restricting computational demands. The effectiveness of the proposed algorithm was validated through numerical evaluations. Simulated outcomes revealed that when applied to the joint challenge in NOMA uplink systems, it surpasses the spectral efficiency of conventional orthogonal multiple access. Moreover, the proposed approach demonstrated superior performance compared to the standard GWO and other state-of-the-art algorithms, achieving reduced system complexity under identical constraints.
非正交多址接入（NOMA）技术为5G和6G无线网络的频谱效率提供了潜在的提升，有助于实现更广泛的网络接入。实现最优系统性能的核心因素包括联合功率控制、用户分组和解码顺序。本研究调查了功率控制和用户分组，以优化NOMA上行链路系统中的频谱效率，旨在降低计算难度。虽然此前关于这种综合优化的研究已经确定了几种近似最优解，但它们往往伴随着相当大的系统和计算开销。为了解决这个问题，本研究采用了一种改进的灰狼优化器（GWO），这是一种受自然启发的元启发式优化方法。尽管GWO很有效，但它有时会过早收敛，可能缺乏多样性。为了提高其性能，本研究引入了一种新版本的GWO，集成了竞争学习、Q学习和贪婪选择。竞争学习采用智能体竞争，平衡探索和利用并保持多样性。Q学习根据过去的经验指导搜索，增强适应性并防止对次优区域的冗余探索。贪婪选择确保每次迭代后保留最佳解。这三个组件的协同集成大大提高了标准GWO的性能。该算法用于管理NOMA系统中的功率和用户分组，旨在在限制计算需求的同时增强系统性能。通过数值评估验证了所提算法的有效性。仿真结果表明，当应用于NOMA上行链路系统的联合挑战时，它超过了传统正交多址接入的频谱效率。此外，与标准GWO和其他现有算法相比，所提方法表现出卓越的性能，在相同约束下实现了更低的系统复杂度。
