Low-Rank Tensor MMSE Equalization

TL;DR

This paper introduces a low-rank tensor equalizer for massive MIMO systems that reduces computational complexity and improves robustness over traditional methods.

Contribution

It proposes a novel canonical polyadic tensor filter design for MMSE equalization in large-scale antenna systems.

Findings

Requires fewer calculations than benchmark methods.

More robust to short training sequences.

Maintains low mean square error performance.

Abstract

New-generation wireless communication systems will employ large-scale antenna arrays to satisfy the increasing capacity demand. This massive scenario brings new challenges to the channel equalization problem due to the increased signal processing complexity. We present a novel low-rank tensor equalizer to tackle the high computational demands of the classical linear approach. Specifically, we propose a method to design a canonical polyadic tensor filter to minimize the mean square error criterion. Our simulation results indicate that the proposed equalizer needs fewer calculations and is more robust to short training sequences than the benchmark.