Low Complexity Time Domain Semi-Blind MIMO-OFDM Channel Estimation Using Adaptive Bussgang Algorithm

TL;DR

This paper introduces a low complexity semi-blind MIMO-OFDM channel estimation method in the time domain, combining LMS with decision-directed and Bussgang algorithms to improve accuracy in time-varying channels.

Contribution

It develops a novel semi-blind LMS-based algorithm that adapts with decision-directed and Bussgang techniques, enhancing channel estimation in time-varying MIMO-OFDM systems.

Findings

Near full training case estimation error achieved

Performance varies with scenario, combining methods is beneficial

Adaptive step size improves estimation accuracy

Abstract

In this paper, a low complexity time domain semi-blind algorithm is proposed to estimate and track the time varying MIMO OFDM channels. First, the proposed least mean squares (LMS) based algorithm is developed for the training mode and then is extended for the blind mode of the operation by combining with the decision direction (DD) or adaptive Bussgang algorithm (ABA) techniques. In the blind mode, because of decision errors, a smaller step size is considered for the LMS algorithm and the channel estimation is run a few times to improve its precision. In each round of the estimation in the blind mode, the step size is decreased to form some kind of annealing. Both DD LMS and ABA LMS techniques are simulated and compared to the full training case and MSE of channel estimation error is considered as comparison criterion. It is shown for 2x4 DD LMS and for 4x4 ABA LMS algorithms present near full training case estimation error. Of course in some scenarios the former proposed technique performs better and in other scenarios the latter is better and therefore combine of it can be very interesting in all channel conditions.