Multi-dimensional hierarchical dictionary search for large MIMO-OFDM systems

TL;DR

This paper introduces a low-complexity, structurally-aware method for atom selection in sparse recovery algorithms tailored for large MIMO-OFDM systems, significantly reducing computational costs.

Contribution

It proposes a novel hierarchical dictionary search strategy that leverages system structure, with theoretical validation and practical testing demonstrating efficiency gains.

Findings

Reduced computational complexity in sparse recovery for large MIMO systems

Theoretical proof of the proposed method's efficiency

Experimental results with realistic data confirm performance improvements

Abstract

Sparse recovery algorithms are of utmost importance for estimation processes in wireless communications. However, communication systems such as massive multiple input multiple output (MIMO) systems are rapidly growing in dimension, which consequently increases the computational complexity of these algorithms. This work proposes a low-complexity strategy for the efficient implementation of the ''atom selection step'' in these greedy sparse recovery algorithms, based on the structural features of these systems. A theoretical justification is presented along with tests using realistic channel data, to demonstrate the computational gain induced by the proposed approach and compare it to the classical sparse recovery approach.