An Efficient Cooperative Spectrum Sensing Method Using Renyi Entropy Weighted Optimal Likelihood Ratio for CRN-Reference-Cited by-同舟云学术

An Efficient Cooperative Spectrum Sensing Method Using Renyi Entropy Weighted Optimal Likelihood Ratio for CRN

Published:2023-09-01 Issue:2023 Volume:3 Page:41-48
ISSN:1509-4553
Container-title:Journal of Telecommunications and Information Technology
language:
Short-container-title:JTIT

Author:

Shrote Sandip B.,Poshattiwar Sadhana D.

Abstract

The main concept behind employing cognitive radio is to enable secondary users (SUs) or unlicensed users to utilize the available spectrum. Spectrum sensing methods detect the existence of primary users (PUs) and have become the main topic of research in the CRN industry and in academia. This paper proposes a new framework based on the Adam gradient descent (Adam GD) algorithm to develop a spectrum sensing mechanism used in CRNs and detecting the availability of free channels. The signal's components are extracted from the received signal and the spectrum is searched for availability which is detected through a fusion center using the proposed algorithm. The proposed Adam GD algorithm attains the maximum detection probability rate and the minimum false alarm probability of 0.71 and 0.39, respectively, for a Rayleigh channel.

Publisher

National Institute of Telecommunications

Subject

Electrical and Electronic Engineering,Computer Networks and Communications

Reference29 articles.

1. M. Amjad, M.H. Rehmani, and S. Mao, "Wireless Multimedia Cognitive Radio Networks: A Comprehensive Survey", IEEE Communications Surveys & Tutorials, vol. 20, no. 2, pp. 1056-1103, 2018.

2. Y. Chen and H.S. Oh, "A Survey of Measurement-based Spectrum Occupancy Modeling for Cognitive Radios", IEEE Communications Surveys & Tutorials, vol. 18, no. 1, pp. 848-859, 2014.

3. J. Lunden, V. Koivunen, and H.V. Poor, "Spectrum Exploration and Exploitation for Cognitive Radio: Recent Advances", IEEE Signal Processing Magazine, vol. 32, no. 3, pp. 123-140, 2015.

4. C. Liu, J. Wang, X. Liu, and Y.C. Liang, "Deep CM-CNN for Spectrum Sensing in Cognitive Radio", IEEE Journal on Selected Areas in Communications, vol. 37, no. 10, pp. 2306-2321, 2019.

5. Y. Ma, Y. Gao, Y.C. Liang, and S. Cui, "Reliable and Efficient Sub-Nyquist Wideband Spectrum Sensing in Cooperative Cognitive Radio Networks", IEEE Journal on Selected Areas in Communications, vol. 34, no. 10, pp. 2750-2762, 2016.