Spatially correlated channel estimation based on block iterative support detection for large-scale MIMO

TL;DR

This paper introduces a block iterative support detection algorithm for large-scale MIMO downlink channel estimation, significantly reducing pilot overhead by exploiting spatial correlations and block sparsity, outperforming classical CS methods.

Contribution

The paper proposes a novel block-ISD algorithm that leverages block sparsity and spatial correlations, reducing pilot overhead without requiring prior sparsity knowledge.

Findings

Achieves 84% reduction in pilot overhead.

Outperforms classical CS algorithms in NMSE.

Demonstrates improved channel estimation accuracy.

Abstract

Downlink channel estimation with low pilot overhead is an important and challenging problem in large-scale MIMO systems due to the substantially increased MIMO channel dimension. In this letter, we propose a block iterative support detection (block-ISD) based algorithm for downlink channel estimation to reduce the pilot overhead, which is achieved by fully exploiting the block sparsity inherent in the block-sparse equivalent channel derived from the spatial correlations of MIMO channels. Furthermore, unlike conventional compressive sensing (CS) algorithms that rely on prior knowledge of the sparsity level, block-ISD relaxes this demanding requirement and is thus more practically appealing. Simulation results demonstrate that block-ISD yields better normalized mean square error (NMSE) performance than classical CS algorithms, and achieve a reduction of 84% pilot overhead than conventional channel estimation techniques.