Reinforcement Learning Based Transmission Strategy of Cognitive User in IEEE 802.11 based Networks

TL;DR

This paper proposes a Q-Learning based online transmission strategy for secondary users in IEEE 802.11 networks, enabling opportunistic access without full primary user state awareness, improving spectrum utilization.

Contribution

Introduces a novel reinforcement learning algorithm for secondary user transmission in cognitive radio networks with limited primary user information.

Findings

The proposed Q-Learning algorithm outperforms existing solutions in simulation.

Secondary users can effectively learn to access channels without full primary state knowledge.

The method enhances spectrum efficiency in IEEE 802.11 networks.

Abstract

Traditional concept of cognitive radio is the coexistence of primary and secondary user in multiplexed manner. we consider the opportunistic channel access scheme in IEEE 802.11 based networks subject to the interference mitigation scenario. According to the protocol rule and due to the constraint of message passing, secondary user is unaware of the exact state of the primary user. In this paper, we have proposed an online algorithm for the secondary which assist determining a backoff counter or the decision of being idle for utilizing the time/frequency slot unoccupied by the primary user. Proposed algorithm is based on conventional reinforcement learning technique namely Q-Learning. Simulation has been conducted in order to prove the strength of this algorithm and also results have been compared with our contemporary solution of this problem where secondary user is aware of some states of primary user.