Improvement in Co-Operative Spectrum Sensing Using ILP and GA in Cognitive Radio Network

Authors

  • Nisha Morasada Department of Electronics and Communication, Dr. S. & S. S. Ghandhy Government College, Surat, Gujarat, India
  • Ketki Pathak Department of Electronics and Communication, Sarvajanik College of Engineering and Technology, Surat, Gujarat, India

DOI:

https://doi.org/10.51983/ajes-2021.10.2.3143

Keywords:

Cognitive Radio Network (CRN), Co-Operative Spectrum Sensing (CSS), Genetic Algorithm (GA), Integer Linear Programming (ILP)

Abstract

Wireless technologies have grown at a speed and with the passing year, it shows clearly that their consumers are also increasing, that increment in wireless spectrum consumer increases the demand for spectrum. But for our wireless systems, the spectrum is divided into two categories: the first is licensed and the second is unlicensed. Licensed spectrum is used by authorized users and unlicensed spectrum is free for all users. But most of the time it is shown the hat licensed spectrum may not be properly utilized by primary users (PUs), at that time spectrum band is free. To mitigate that inappropriate use of spectrum cognitive radio (CR) network is used. In CR there is one challenge that among the CR node some nodes experience an impact of multipath and shadowing, and another is to sense the spectrum under a lower signal to noise ratio. To overcome the effect of multipath and shadowing co-operative spectrum sensing has been used but it has large energy utilization. This extra energy is consumed in sensing the spectrum and reporting each nodes local decision to Fusion Centre (FC). In this paper we discuss three different schemes for total sensing time and energy decrement or throughput improvement. Here we go after for the genetic algorithm and integer linear programming scheme for overall energy minimization and throughput maximization.

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Published

05-11-2021

How to Cite

Morasada, N., & Pathak, K. (2021). Improvement in Co-Operative Spectrum Sensing Using ILP and GA in Cognitive Radio Network. Asian Journal of Electrical Sciences, 10(2), 29–39. https://doi.org/10.51983/ajes-2021.10.2.3143