Deep Q-learning based optimal resource allocation method for energy harvested cognitive radio networks

Abstract

In this article, we propose a deep Q-learning based algorithm for optimal resource allocation in energy harvested cognitive radio networks (EH-CRN). In EH-CRN, channel resources of primary users (PU) networks are shared with secondary users (SU) and energy harvesting allows nodes of the CRN to acquire energy from the environment for operation sustainability. However, amount of energy harvested from the environment is not fixed and requires dynamic allocation of resources for obtaining optimum network and throughput capacity. In this work, we overcome the limitations of existing Q-learning based resource allocation schemes which are constrained by large state-space systems and have slow convergence. Proposed deep Q-learning based algorithm improves the resource allocation in EH-CRN, while considering quality of service (QoS), energy and interference constraints. Simulation results show that proposed algorithm provide improved convergence and better resource utilization compared to other techniques in literature.