Spectrum Allocation in Cognitive Radio: A PSO-based Approach

  • Subhashree Mishra School of Electronics Engineering, Kalinga Institute of Industrial Technology Deemed to be University, 751024-Bhubaneswar, India
  • Santwana Sagnika School of Computer Engineering, Kalinga Institute of Industrial Technology Deemed to be University, 751024-Bhubaneswar, India
  • Sudhansu Sekhar Singh School of Electronics Engineering, Kalinga Institute of Industrial Technology Deemed to be University, 751024-Bhubaneswar, India
  • Bhabani Shankar Prasad Mishra School of Computer Engineering, Kalinga Institute of Industrial Technology Deemed to be University, 751024-Bhubaneswar, India

Abstract

Cognitive radio systems have taken a fore-running position in the wireless communication technology. With most of the communication taking place through multi-carrier systems, the allocation of available spectrum to various carriers is a prominent issue. Since cognitive systems provide an environment of dynamic spectrum allocation, it becomes necessary to perform dynamic spectrum allocation swiftly with due consideration of parameters, like power consumption, fair distribution and minimal error. This paper considers a Particle Swarm Optimization-based approach, popularly used for solving large problems involving complex solution spaces to reach an optimal solution within feasible time. The mentioned spectrum allocation problem has been solved using PSO with a view to maximize the total transfer rate of the system, within specified constraints of maximum error rate, maximum power consumption and minimum transfer rate per user. The results have been compared with the existing Genetic Algorithm-based approach and have proved to be more effective.

Keywords: cognitive radio, spectrum allocation, Particle Swarm Optimization, Genetic Algorithm, multi-carrier systems
Published online
2019-01-10
How to Cite
Mishra, S., Sagnika, S., Singh, S. and Mishra, B. (2019) “Spectrum Allocation in Cognitive Radio: A PSO-based Approach”, Periodica Polytechnica Electrical Engineering and Computer Science, 63(1), pp. 23-29. doi: https://doi.org/10.3311/PPee.13074.
Section
Articles