A Reinforcement Learning-Based Green Resource Allocation for Heterogeneous Services in Cooperative Cognitive Radio Networks
Amandeep Kaur, Krishan Kumar
Abstract
The explosive growth of heterogeneous mobile data traffic with stringent Quality of Service (QoS) requirements put significant pressure on the existing network infrastructure and also present an important challenge even to very anticipated Cognitive Radio (CR) networks. In this perspective, to meet the demand of upcoming QoS requirements tied to their distinctive needs for resources, an intelligent solution is required which offers higher flexibility and to handle both current and future upcoming QoS challenges. The conventional resource allocation strategies face significant problems in meeting the increasing demand for bandwidth-hungry services due to stochastic behavior of wireless networks. To solve this problem, an efficient resource allocation scheme is proposed which maximizes energy efficiency while maintaining QoS requirements for all users. The Reinforcement learning based Q-Learning (Q-L) scheme is found most suitable for green resource allocation based on current network conditions that manages the QoS provisioning for heterogeneous traffic even under dynamic environmental conditions. Further, to enhance the performance of such sophisticated scheme, cooperative framework is introduced to improve the convergence speed. Experimental and simulated results demonstrate the effectiveness of proposed cooperative scheme. The proposed scheme outperforms current benchmark schemes in terms of meeting the energy-efficiency and stringent heterogeneous QoS requirements.