Intelligent resource allocation in wireless networks via deep reinforcement learning

This study addresses the challenge of optimal power allocation in stochastic wireless networks by employing a Deep Reinforcement Learning (DRL) framework. Specifically, we design a Deep Q-Network (DQN) agent capable of learning adaptive power control policies directly from channel state observations, effectively bypassing the need for explicit system models. We formulate the resource allocation problem as a Markov Decision Process (MDP) and benchmark the proposed approach against classical heuristics, including fixed allocation, random assignment, and the theoretical water-filling algorithm. Empirical results demonstrate that the DQN agent achieves a system throughput of 3.88 Mbps, effectively matching the upper limit of the water fill, while outperforming the random and fixed allocation strategies by approximately 73% and 27%, respectively. Moreover, the agent exhibits emergent fairness, maintaining a Jain's Index of 0.91, and successfully optimizes the trade-off between spectral efficiency and energy consumption. These findings substantiate the efficacy of model-free DRL as a robust and scalable solution for resource management in next-generation communication systems.