Poor utilization of an electromagnetic spectrum has led to surge of interest in paradigms such as cognitive radio, unlicensed LTE etc. Such paradigms allow opportunistic spectrum access in the vacant fre- quency bands of the licensed spectrum. Though various spectrum detec- tors to check the status of frequency bands (i.e., vacant or occupied) have been studied, the selection of the frequency band from wideband spectrum is a challenging problem especially in the decentralized net- work. In this paper, a testbed for analyzing the performance of decision making policies (DMPs) for identifying optimum frequency band in the decentralized network is presented. Furthermore, experimental results using real radio signals show that the proposed DMP using Bayesian multi-armed bandit algorithm leads to 7-12 % improvement in an average spectrum utilization over existing DMPs. Added advantages of 6-20 % lower switching cost and 30-46 % fewer collisions make the proposed DMP energy-efficient.
['Sumit Jagdish Darak', 'Amor Nafkha', 'Christophe Moy', 'Jacques Palicot']
Is Bayesian Multi-armed Bandit Algorithm Superior?: Proof-of-Concept for Opportunistic Spectrum Access in Decentralized Networks