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1887

access icon free Analytical and learning-based spectrum sensing time optimisation in cognitive radio systems

© The Institution of Engineering and Technology
Received 29/05/2012, Accepted 12/12/2012, Revised 24/11/2012, Published

In this study, the average throughput maximisation of a secondary user (SU) by optimising its spectrum sensing time is formulated, assuming that a priori knowledge of the presence and absence probabilities of the primary users (PUs) is available. The energy consumed to find a transmission opportunity is evaluated, and a discussion on the impacts of the number of PUs on SU throughput and consumed energy are presented. To avoid the challenges associated with the analytical method, as a second solution, a systematic adaptive neural network-based sensing time optimisation approach is also proposed. The proposed scheme is able to find the optimum value of the channel sensing time without any prior knowledge or assumption about the wireless environment. The structure, performance and cooperation of the artificial neural networks used in the proposed method are explained in detail, and a set of illustrative simulation results is presented to validate the analytical results as well as the performance of the proposed learning-based optimisation scheme.

Inspec keywords: neural nets; optimisation; radio spectrum management; cognitive radio

Other keywords: transmission opportunity; channel sensing time; wireless environment; learning based optimisation scheme; systematic adaptive neural network based sensing time optimisation; artificial neural networks; secondary user; cognitive radio system; primary users; learning based spectrum sensing time optimisation; average throughput maximisation

Subjects: Neural nets (circuit implementations); Radio links and equipment; Optimisation techniques

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