Semi-Blind Channel-and-Signal Estimation for Uplink Massive MIMO With Channel Sparsity

TL;DR

This paper introduces a semi-blind channel and signal estimation method for uplink massive MIMO systems with channel sparsity, leveraging short pilot sequences to improve detection accuracy and reduce ambiguity.

Contribution

It proposes a novel semi-blind estimation scheme that efficiently integrates pilot knowledge into message passing algorithms, and develops a simplified version for large user systems.

Findings

Outperforms existing blind detection schemes in short-pilot scenarios.

The simplified SCSE reduces computational complexity for large user numbers.

Semi-blind scheme significantly improves detection accuracy over training-based methods.

Abstract

This paper considers the transceiver design for uplink massive multiple-input multiple-output (MIMO) systems with channel sparsity in the angular domain. Recent progress has shown that sparsity-learning-based blind signal detection is able to retrieve the channel and data by using massage-passing based sparse matrix factorization methods. Short pilots sequences are inserted into user packets to eliminate the so-called phase and permutation ambiguities inherent in sparse matrix factorization. In this paper, to exploit the knowledge of these short pilot sequences more efficiently, we propose a semi-blind channel-and-signal estimation (SCSE) scheme in which the knowledge of the pilot sequences are integrated into the message passing algorithm for sparse matrix factorization. The SCSE algorithm involves enumeration over all possible user permutations, and so is time-consuming when the number of users is relatively large. To reduce complexity, we further develop the simplified SCSE (S-SCSE) to accommodate systems with a large number of users. We show that our semi-blind signal detection scheme substantially outperforms the state-of-the-art blind detection and training-based schemes in the short-pilot regime.