Jul 2026· International Journal of Computer & Organization Trends· 0 citations· 99 references
TL;DR
A focused review of power allocation strategies in NOMA is presented, with emphasis on the progression from static and optimization-based dynamic schemes to data-driven Artificial Intelligence (AI) and Machine Learning (ML) driven approaches.
Abstract
The transition of 5G and beyond wireless networks toward intelligence-driven and autonomous operation has revitalized strong interest in Non-Orthogonal Multiple Access (NOMA) as an efficient multiple access framework. Power allocation critically governs NOMA performance, directly impacting throughput, user fairness, and SIC effectiveness. This survey presents a focused review of power allocation strategies in NOMA, with emphasis on the progression from static and optimization-based dynamic schemes to data-driven Artificial Intelligence (AI) and Machine Learning (ML) driven approaches. In contrast to conventional strategies that require instantaneous channel state information and iterative optimization, AI/ML techniques enable adaptive, scalable, and low-latency decision-making in highly dynamic and nonconvex environments. Recent advances in reinforcement learning and deep learning for NOMA power control are discussed, highlighting key challenges like imperfect CSI, inter-cluster interference, and distributed learning constraints. This survey provides a concise AI-centric analysis and identifies promising directions for a practical learning-driven NOMA power allocation framework for future wireless networks. A consolidated, critically comparative analysis of NOMA power allocation that bridges the gap between 5G practice and 6G imperatives is also presented in this survey.
This work demonstrates the viability of RL for distributed resource management and provides a reproducible simulation toolkit to support further research in AI-driven wireless communication systems.
Mugerwa Joseph, Ajaegbu Chigozirim· International Journal Of Eng...· 0 citations
Sixth-generation (6G) mobile communication poses unprecedented challenges for resource scheduling under personalized demands. Cell-free massive multiple-input multiple-output (CF-mMIMO), with its user-centric characteristics, has emerged as a key technology for satisfying personalized demands. However, faced with heterogeneous quality-of-service (QoS) requirements, existing reinforcement learning schemes are constrained by partial observability, making it difficult to balance overall system performance and personalized demands. Consequently, we propose a graph-embedded multi-agent deep deterministic policy gradient (G-MADDPG) scheme. Guided by personalized demands, proposed G-MADDPG formulates a maximization problem for system weighted sum spectral efficiency and introduces differentiated QoS penalties. In addition, graph neural networks (GNNs) are embedded into the policy learning and value estimation processes of reinforcement learning, endowing agents with enhanced structural reception and cooperative capabilities. Simulation results demonstrate that proposed G-MADDPG scheme outperforms existing benchmark schemes in both convergence speed and performance evaluation.
Yu-Heng An· 2026 8th International Confe...· 0 citations
A Dual-Stage Multi-Time-Scale Temporal Attention-Based LSTM network (D-MTSTA-LSTM) has been architected, which effectively learns short- and long-term relationships in network trends, thereby precisely predicting optimal communication routes and associated power and spectrum allocation.
Nishu Gupta, Rupali Bhartiya, S. Rathod et al.· Scientific Reports· 0 citations
Recently, the development and deployment of intelligent controllers for radio access networks (RAN) has attracted significant attention from network operators and international telecommunications organizations, driven by rapid advances in artificial intelligence. Mobility management plays a fundamental role in ensuring seamless connectivity and service quality in 5G RAN. In fact, optimal control in 5G RAN is highly challenging due to its complex, dynamic, and distributed environment. Many approaches have been proposed to address this problem, particularly those based on deep reinforcement learning (DRL). However, contrary to the dense reward assumption in many DRL-based studies, mobility feedback in practical RAN environments is characteristically sparse and delayed. In this paper, we propose WHO (World Model for Handover Optimization), a novel method designed to bridge the gap between sparse feedback and efficient learning in 5G networks. WHO utilizes a world model to convert event-driven rewards into dense predictive signals, facilitating robust multi-agent optimization. Field experiments involving 13 base stations and 39 cells show that the proposed method significantly improves handover performance and network stability compared to conventional DRL approaches, achieving 19–40% higher prediction precision and up to 32% improvement in key performance indicators (KPIs).
Uyen Thi Thu Truong, Doan Van Nguyen, Do Ngoc Tuan et al.· International Conference on...· 0 citations
To fulfill the ultra-low latency and high-reliability requirements of sixth-generation (6G) hyperconnectivity, this paper proposes VLC-Net, a supervised deep learning framework for real-time resource allocation in multicell visible light communication (VLC) networks employing non-orthogonal multiple access (NOMA). A feedforward deep neural network (DNN) with a Bayesian-optimized architecture was trained using twenty thousand near-optimal power allocation labels generated by a fairness-centric constrained genetic algorithm (CGA). Extensive Monte Carlo simulations for networks with four, six, eight, and ten users show that VLC-Net achieves an inference time as low as 2.65 milliseconds for ten users and an average inference time of 13.71 milliseconds across all evaluated user densities. This performance represents an approximately 388 times speedup over iterative evolutionary solvers. Furthermore, the proposed model improves Jain’s fairness index by 66 percent compared with the greedy Water Filling approach while maintaining an energy efficiency of 10.87 megabits per second per watt under high-interference conditions with ten users. Paired t tests yield probability values below 0.05 for all evaluated user densities, confirming statistically significant performance differences. These results demonstrate that VLC-Net offers a robust, scalable, and low-latency solution for practical intelligent power management in future dense indoor optical wireless network deployments.
Marcelia Chintya Hartakaadi, Aminah Indahsari Marsuki, Intan Nisa Bani et al.· International Conference on...· 0 citations
This research is among the first to employ MARL to this extent, and it offers an end-to-end solution that combines cellular, Wi-Fi, and device-to-device (D2D) communications and considers practical network environments like user mobility and channel conditions.
Nabeel Abdolrazagh Yaseen Alrashedi, Rasool Sadeghi, Wael Hussein Zayer Al-Lamy et al.· Journal of universal compute...· 0 citations
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