Particle Swarm Optimization Based HMM Parameter Estimation for Spectrum Sensing in Cognitive Radio System

Abstract

Spectrum Estimation has emerged as the major bottleneck for the development of advanced technologies (IoT and 5G) that demand for a unperturbed continuous availability of the spectrum resources. Opportunistic dynamic access of spectrum by unlicensed users when the licensed user is not using the resources is seen as a solution to the pressing issue of spectrum scarcity. The idea proposed for spectrum estimation is to model the Cognitive Radio (CR) network as Hidden Markov Model (HMM). The spectral estimation is done once in a frame. 100 such frames with 3000 slots each is considered for performing the experiment, assuming that the PU activity is known for a fraction of $3.33\%$ of the slots i.e., for 100 slots. The parameters of the HMM are estimated by maximizing the generating probability of the sequence using the Particle Swarm Optimization (PSO). For the typical values of the network parameters, the experiments are performed and the results are presented. A novel sum squared error minimization based “Empirical Match” algorithm is proposed for an improved latent sequence estimation.