ACOBAT – A Reinforcement Learning Based on Fused ACO and BAT Algorithm for Spectrum Sharing

Abstract

Spectrum is the vital resource for the communication among wireless devices. For the past several years the requirement for spectrum frequency is increased due to the improvement in mobile communication. Spectrum sharing helps to meet the frequency needs through spectrum detection and proper allocation. The problem in spectrum sharing is addressed in this paper, where the time allocation is minimized. This research contributes to designing a new methodology that optimizes the allocation of the unallocated frequency channel without any time delay. In this paper, a new method is proposed to overcome the problems faced in spectrum sharing.  The proposed method is based on swarm intelligence to share the spectrum in a multichannel system. The novel methodology based on the Ant Colony Optimization technique (ACO) and Bats algorithm (BA) is efficient and provides a speed of operation with minimum delay. The proposed method’s performance is found better compared to the existing methods. This has been achieved through the decentralized, self-organized, and natural characteristics of the method. The analysis is done with respect to transmission delay, noise identification, and throughput. In future noise identification and throughput based on that will be investigated.