Low-complexity near-optimal signal detection for uplink large-scale MIMO systems

TL;DR

This paper introduces a low-complexity, near-optimal signal detection algorithm for uplink large-scale MIMO systems that avoids matrix inversion by leveraging the symmetry of the MMSE filtering matrix, significantly reducing computational complexity.

Contribution

The paper proves the MMSE filtering matrix is symmetric positive definite and proposes a Richardson method-based detection algorithm with reduced complexity from O(K^3) to O(K^2).

Findings

The proposed algorithm converges rapidly.

It achieves near-MMSE optimal performance.

Complexity is significantly reduced.

Abstract

Minimum mean square error (MMSE) signal detection algorithm is near- optimal for uplink multi-user large-scale multiple input multiple output (MIMO) systems, but involves matrix inversion with high complexity. In this letter, we firstly prove that the MMSE filtering matrix for large- scale MIMO is symmetric positive definite, based on which we propose a low-complexity near-optimal signal detection algorithm by exploiting the Richardson method to avoid the matrix inversion. The complexity can be reduced from O(K3) to O(K2), where K is the number of users. We also provide the convergence proof of the proposed algorithm. Simulation results show that the proposed signal detection algorithm converges fast, and achieves the near-optimal performance of the classical MMSE algorithm.