A Two-Phase Maximum-Likelihood Sequence Estimation for Receivers with Partial CSI

TL;DR

This paper introduces a two-phase low-complexity maximum-likelihood sequence estimation algorithm for receivers with partial channel state information, improving performance over conventional methods and approaching optimal exhaustive checking with moderate training sequences.

Contribution

It proposes a novel two-phase MLSE algorithm that effectively utilizes partial CSI, reducing complexity while maintaining near-optimal performance.

Findings

Approaches the performance of exhaustive MLSE with moderate training sequences.

Outperforms conventional MLSE when training sequence bandwidth is limited.

Effective in practical scenarios with partial CSI and limited training data.

Abstract

The optimality of the conventional maximum likelihood sequence estimation (MLSE), also known as the Viterbi Algorithm (VA), relies on the assumption that the receiver has perfect knowledge of the channel coefficients or channel state information (CSI). However, in practical situations that fail the assumption, the MLSE method becomes suboptimal and then exhaustive checking is the only way to obtain the ML sequence. At this background, considering directly the ML criterion for partial CSI, we propose a two-phase low-complexity MLSE algorithm, in which the first phase performs the conventional MLSE algorithm in order to retain necessary information for the backward VA performed in the second phase. Simulations show that when the training sequence is moderately long in comparison with the entire data block such as 1/3 of the block, the proposed two-phase MLSE can approach the performance of the optimal exhaustive checking. In a normal case, where the training sequence consumes only 0.14 of the bandwidth, our proposed method still outperforms evidently the conventional MLSE.