Cooperative Channel Selection With Q-Reinforcement Learning and Power Distribution in Cognitive Radio Networks

Abstract

With the increasing number of wireless communication devices, there may be a shortage of non-licensed spectrum, and at the same time, licensed spectrum may be underutilized by the primary users. The utilization of licensed spectrum can be improved using cognitive radio techniques. The proposed work allows secondary users to use the correct slot period of the channel as per their need. Particle swarm optimization technique is used to optimize the resource allocation. The aim of the proposed work is to determine the optimal throughput and power of available channels between the communicating nodes and improve the routing performance by selecting the best channel. Mathematical equation is derived that represents the channel selection relationship from the Q-value, congestion throughput, and benefit value. Network simulator-2 is used to simulate the proposed work and compared with the existing work. From the simulation results, it is observed that routing performance is improved in terms of throughput, packet delivery ratio, delay, packet dropped, and normalized routing overhead.

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“…MCSUI [19] performs joint channel selection and routing which minimizes channel switching and user interferences in cognitive radio networks. In [20], particle swarm optimization is used to optimize the channel allocation when multiple secondary users listen to the channel at the same time, and the best cooperative channel selection is performed based on Q-reinforcement learning. In [21], an Extended Generalized Predictive Channel Selection Algorithm is proposed to increase the throughput and reduce the delay of secondary users.…”

“…The proposed IQRLC protocol is evaluated with different routing metrics and it is compared with CSRC [20], LCRP and CRP routing protocols [17]. In LCRP, the optimal policy for channels is derived based on the policy iteration method.…”