Deep Reinforcement Learning for Adaptive Resource Allocation in 6G Wireless Networks

Abstract

The emergence of 6G wireless networks introduces unprecedented challenges in dynamic resource allocation due to extreme requirements for ultra-low latency, massive connectivity, high energy efficiency, and intelligent spectrum utilization. Traditional optimization and heuristic-based resource allocation techniques struggle to adapt to the highly dynamic, heterogeneous, and large-scale nature of 6G environments. To address these limitations, Deep Reinforcement Learning (DRL) has emerged as a promising paradigm for adaptive and intelligent resource management in next-generation wireless systems. This research proposes a Deep Reinforcement Learning Framework for Adaptive Resource Allocation in 6G Wireless Networks, designed to optimize spectrum allocation, power control, user scheduling, and network slicing in real-time dynamic environments. The framework leverages advanced DRL architectures such as Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Actor-Critic methods to learn optimal policies from continuous network interactions. The proposed system integrates network state representation, reward-driven optimization, and environment-aware learning to dynamically allocate resources while maximizing throughput, minimizing latency, and improving energy efficiency. Simulation results demonstrate that the DRL-based framework significantly outperforms traditional optimization methods in terms of spectral efficiency, convergence speed, and adaptive decision-making capability under varying network loads. The study contributes a scalable and intelligent resource allocation framework suitable for complex 6G scenarios such as ultra-dense networks, IoT-driven communication systems, and intelligent edge computing environments. The results confirm that DRL-based adaptive control is a viable solution for next-generation wireless network optimization problems.