Open Access

ARTICLE

Intelligent Spectrum Detection Model Based on Compressed Sensing in Cognitive Radio Network

Yanli Ji^{1, *}, Weidong Wang², Yinghai Zhang²

1 School of Network Education, Beijing University of Posts and Telecommunications, Beijing, China.
2 Information and Electronic Technology Lab, Beijing University of Posts and Telecommunications, Beijing, China.
* Corresponding Author: Yanli Ji. Email: kxhjmewh11@163.com.

(This article belongs to the Special Issue: Security Enhancement of Image Recognition System in IoT based Smart Cities)

Computer Modeling in Engineering & Sciences 2020, 122(2), 691-701. https://doi.org/10.32604/cmes.2020.07861

Received 05 July 2019; Accepted 25 September 2019; Issue published 01 February 2020

Abstract

In view of the uncertainty of the status of primary users in cognitive networks and the fact that the random detection strategy cannot guarantee cognitive users to accurately find available channels, this paper proposes a joint random detection strategy using the idle cognitive users in cognitive wireless networks. After adding idle cognitive users for detection, the compressed sensing model is employed to describe the number of available channels obtained by the cognitive base station to derive the detection performance of the cognitive network at this time. Both theoretical analysis and simulation results show that using idle cognitive users can reduce service delay and improve the throughput of cognitive networks. After considering the time occupied by cognitive users to report detection information, the optimal participation number of idle cognitive users in joint detection is obtained through the optimization algorithm.

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Keywords

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