Autonomous Resource Allocation for QoS Enhancement in Next-Generation Heterogeneous Wireless Systems
Keywords:
Mobile Wireless Networks, Heterogeneous Networks (HetNet), Deep Reinforcement Learning (DRL), Autonomous Resource Allocation, Decision Support System (DSS)Abstract
The increasing heterogeneity of Beyond Fifth-Generation (B5G) and Sixth-Generation (6G) wireless networks, together with rapidly changing traffic demands, fluctuating channel conditions, and diverse Quality-of-Service (QoS) requirements, presents substantial challenges for autonomous resource allocation. Conventional static and reactive approaches lack the flexibility required for such dynamic environments, while existing learning-based techniques generally provide limited support for predictive, multi-objective, and temporally stable resource decisions. To address these limitations, this paper proposes a hybrid Decision Support System (DSS) that unifies Multi-Objective Optimization (MOOP), Deep Reinforcement Learning (DRL), a Predictive Environment Model (PEM), and Stability-Aware Optimization (SAO) within an autonomous resource-allocation framework. By combining real-time network observations with forecasts of forthcoming environmental conditions, the DSS autonomously determines resource allocations across multiple, potentially conflicting objectives. Within the framework, DRL facilitates adaptive policy learning, PEM anticipates future network states to support proactive decision-making, MOOP manages trade-offs among throughput, latency, fairness, and energy efficiency, and SAO promotes temporal consistency across consecutive allocation decisions. Communication-link quality is further assessed through Signal-to-Interference-plus-Noise Ratio (SINR) and data rate. Extensive simulation experiments compare the proposed DSS against static, reactive, and conventional DRL-based resource-allocation approaches. The findings demonstrate improvements in communication efficiency, resource utilisation, latency, fairness, and energy efficiency. Convergence and temporal evaluations further reveal faster and more consistent decision behaviour, while SINR and data-rate analyses indicate enhanced communication-link quality under the assumed operational conditions. Overall, the findings demonstrate that integrating adaptive learning, predictive environmental awareness, multi-objective optimisation, and stability-aware decision-making within a DSS provides an effective approach to autonomous resource management in dynamic B5G and 6G heterogeneous wireless networks while maintaining balanced trade-offs among competing objectives.
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