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.
Abstract
For 6G wireless networks, efficient resource allocation is a significant problem, especially with the growing need for ultra-low latency, high-speed communication, and efficient energy consumption. The traditional approach is found to be inadequate to meet the dynamic changes and service-allocation requirements. The application of AI and DL is seen as an efficient approach to making intelligent, timely decisions in complex scenarios. This paper proposes an integrated AI- and DL-based approach for efficient, intelligent resource allocation in 6G wireless communication. The difficulties encountered in dynamic spectrum allocation, energy depletion, and attenuation are addressed through an integrated approach that combines optimal path selection with efficient allocation mechanisms. The input parameters considered are residual battery indicator (RBI), channel matrix (H), normalized spectrum availability (v), SINR values, node pairs (s, d), service levels, and historical statistics. To ensure data quality, a Recursive Hampel Filter-Based Estimation Model (ReHF-EM) has been employed. Furthermore, for fundamental decision-making, 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. Additionally, the parameters of the proposed model have been fine-tuned using the Pied Kingfisher Optimizer (PKfO) for better efficiency, thus reducing complexities associated with the model. The proposed model has been implemented using Python, and various performance parameters such as Spectrum Efficiency (SE), Energy Efficiency (EE), SINR margin, Bit Error Rate (BER), Computational Time (CT), and Accuracy have been considered to evaluate the proposed model. The results show a 23.6% increase in Energy Efficiency and a 19.2% reduction in Bit Error Rate.
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
The proposed modified Deep Reinforcement Learning-based intelligent TDD configuration framework for adaptive radio resource allocation in 5G HetNets effectively enhances network reliability, resource utilization, and communication efficiency in dynamic 5G HetNet environments.
G. Dalton, ·. A. Bamila, Virgin Louis et al.· Wireless networks· 0 citations
Simulation results position D3QN-PER as a strong candidate for deployment as a near-RT RIC xApp within the O-RAN architecture, advancing the vision of AI-native mobility management for 6G.
Kalpesh Popat, Divyakant T. Meva· Telecommunications Systems· 0 citations
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.
Lekshmi Nair M, Neelakantan Pc· International Journal of Com...· 0 citations
The 5G wireless networks are fast with low latency and support a very large number of connected devices, making it possible to support advanced applications and services. Such environment demands efficient management of network resources that can be achieved by evaluation of traffic load, distribution of devices, and state of signals to ensure quality-of-service (QoS) and network performance. Conventional resource allocation and mobility management techniques tend to have challenges of flexibility, computational effectiveness, as well as optimal choice of devices in disparate network conditions. In order to address these issues, this paper introduces a multi-stage resource management system that is adaptive in 5G networks to address QoS and mobility efficiency issues. The framework combines the set of network parameters with Adaptive Multivariate Kernel Resource Estimation (AMKRE) in case of assessing and normalizing available resources. Resource-Aware Device Selection (ORADS) is used to select the appropriate devices and then it is followed by Robust Filter-Based Rank Evaluation (RFBRE) where devices are ranked on the basis of reliability and link quality. The algorithm that optimizes the resources distribution taking into consideration the space constraints is known as DistanceAware Adaptive Particle Swarm Optimization (DAAPSO), and the algorithm that provides the continuity of mobility is known as Hybrid Predictive Soft Handover Control (HPSHC). The multi-dimensional paradigm improves the resource utilization, quality-of-service (QoS) provisioning, and mobility management in 5G networks.
G. Ramasamy, C. Chandrasekar· International journal of com...· 0 citations
The increasing demand for high-speed wireless communication services, coupled with the deployment of advanced technologies such as Massive Multiple-Input Multiple-Output (MIMO), millimeter-wave communications, Internet of Things (IoT), and Sixth Generation (6G) networks, has significantly increased the complexity of wireless channel environments. Accurate channel estimation plays a critical role in ensuring reliable communication, efficient resource utilization, and high-quality service delivery. Conventional channel estimation methods such as Least Squares (LS) and Minimum Mean Square Error (MMSE) often struggle to provide optimal performance in highly dynamic and complex communication environments due to nonlinear channel characteristics, mobility, and interference. Artificial Intelligence (AI) has emerged as a transformative technology capable of improving channel estimation accuracy through intelligent learning and adaptive optimization. This paper presents a comprehensive study of AI-based channel estimation techniques and proposes an Intelligent Deep Learning-Based Channel Estimation Framework (IDL-CEF) designed to enhance wireless communication performance. The proposed framework integrates deep neural networks, machine learning algorithms, adaptive signal processing, and real-time channel prediction mechanisms. Experimental evaluation demonstrates significant improvements in estimation accuracy, spectral efficiency, latency reduction, and communication reliability compared with traditional estimation methods. The findings indicate that AI-based channel estimation will become a fundamental component of future intelligent communication systems and 6G wireless networks.
N.Prashanth Kumar N.Prashanth Kumar, A. A. A Akshitha, Dr B Ramprasad Dr B Ramprasad· International Journal of Sci...· 0 citations