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Adaptive Data-Driven Wideband Compressive Spectrum Sensing for Cognitive Radio Networks

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  • Published:
Journal of Communications and Information Networks

Abstract

This paper presents a novel adaptive wideband compressed spectrum sensing scheme for cognitive radio (CR) networks. Compared to the traditional CSSbased CR scenarios, the proposed approach reconstructs neither the received signal nor its spectrum during the compressed sensing procedure. On the contrary, a precise estimation of wide spectrum support is recovered with a fewer number of compressed measurements. Then, the spectrum occupancy is determined directly from the reconstructed support vector. To carry out this process, a data-driven methodology is utilized to obtain the minimum number of necessary samples required for support reconstruction, and a closed-form expression is obtained that optimally estimates the number of desired samples as a function of the sparsity level and number of channels. Following this phase, an adjustable sequential framework is developed where the first step predicts the optimal number of compressed measurements and the second step recovers the sparse support and makes sensing decision. Theoretical analysis and numerical simulations demonstrate the improvement achieved with the proposed algorithm to significantly reduce both sampling costs and average sensing time without any deterioration in detection performance. Furthermore, the remainder of the sensing time can be employed by secondary users for data transmission, thus leading to the enhancement of the total throughput of the CR network.

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Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, Semnan University, Semnan, 35196-45399, Iran

    Mohsen Ghadyani & Ali Shahzadi

Authors
  1. Mohsen Ghadyani
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  2. Ali Shahzadi
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Corresponding author

Correspondence to Ali Shahzadi.

Additional information

The associate editor coordinating the review of this paper and approving it for publication was J. H. He.

Mohsen Ghadyani received his B.Sc. degree from the Khaje Nasir Toosi University of Technology (KNTU) in 2005, his M.Sc. degree from the Shahed University in 2009 and his Ph.D. degree from the Semnan University in 2017, all in Electrical Engineering. He is currently working on Wireless Networks, Cognitive Radio Systems, 5G Communication Systems, Deviceto- Device (D2D) Heterogeneous Networks and Internet of Things (IoT).

Ali Shahzadi [corresponding author] received his B.Sc., M.Sc. and Ph.D. degrees in Electrical and Computer Engineering from Iran University of Science and Technology in 1997, 2000 and 2005, respectively. He is now an assistant professor of Semnan University, Semnan, Iran. His main research interests are Wireless, Mobile and Broadband Communications and Networking, Digital Signal Processing and Artificial Intelligence Applications in Communication Theory.

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Ghadyani, M., Shahzadi, A. Adaptive Data-Driven Wideband Compressive Spectrum Sensing for Cognitive Radio Networks. J. Commun. Inf. Netw. 3, 84–92 (2018). https://doi.org/10.1007/s41650-018-0016-3

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  • DOI: https://doi.org/10.1007/s41650-018-0016-3

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