A Novel Frequency Offset Estimation Method for Distributed MIMO Systems with Large Estimation Range

摘要

In this paper, we consider the problem of multiple frequency offsets estimation in a flat-fading multi-input multi-output (MIMO) system. Different from existing algorithms based on correlation method using orthogonal training sequences, the proposed algorithm has no restriction to the training sequences. Multi-frequency offsets estimation problem is formulated as a multi-players noncooperative game (NG) optimization based on maximum likelihood (ML) estimation, and the estimation range is extended to the upper bound of the ML estimator. Simulation results evidence the proposed algorithm mitigates the multi-antenna interference (MAI) and performance achieves the Cramer-Rao lower bound (CRLB) in the moderate signal to noise ratio (SNR) environment.