Deterministic pilot pattern allocation optimization for sparse channel estimation based on CS theory in OFDM system

Abstract

Compressed sensing (CS)-based sparse channel estimation requires the sensing matrix with the minimum mutual coherence (MC), and its corresponding pilot pattern obtain optimal estimation performance. In order to minimize the MC of the sensing matrix, a deterministic optimized pilot pattern allocation scheme based on modified adaptive genetic algorithm (MAGA) is investigated in this paper. By adjusting the probability of mutation and crossover adaptively, the proposed scheme guides the search process to obtain the optimized pilot pattern. This method guarantees the convergence of the optimization process and prevents the process into local optimization to get the global optimization. Compared with the existing methods, simulation results prove that the proposed scheme obtain the sensing matrix with the smaller MC, whose corresponding deterministic pilot pattern effectively improve channel estimation performance.