Channel access framework for cognitive radio-based wireless sensor networks using reinforcement learning

Abolarinwa, J. A.; Latiff, N. M. Abdul; Yusof, Sharifah Kamilah Syed

doi:10.1109/scored.2013.7002615

Cited by 1 publication

(1 citation statement)

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“…The reinforcement learning (RL) technique has been applied to many existing works related to cognitive radio, but they were mainly applied to spectrum sensing [ 2 ], cooperative sensing [ 3 , 19 ], spectrum sharing [ 20 ], channel sensing [ 21 , 22 ] and dynamic channel access [ 23 , 24 , 25 ]. For example, the authors in [ 3 , 19 ] applied RL to enhance cooperative gain and mitigate cooperative overhead in cognitive radio.…”

Section: Introductionmentioning

confidence: 99%

An Energy-Efficient Spectrum-Aware Reinforcement Learning-Based Clustering Algorithm for Cognitive Radio Sensor Networks

Mustapha

Ali

Rasid

et al. 2015

Sensors

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It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach.

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