Low-Complexity Massive MIMO Tensor Precoding

TL;DR

This paper introduces a low-complexity tensor-based precoding method for massive MIMO systems that leverages subspace separability in Rician channels, reducing computational load and CSI requirements.

Contribution

The paper proposes a novel tensor precoding approach for massive MIMO that exploits subspace separability, offering lower complexity and CSI needs than traditional linear methods.

Findings

Tensor precoders have lower computational complexity.

They require less instantaneous channel state information.

Simulations confirm practical effectiveness.

Abstract

We present a novel and low-complexity massive multiple-input multiple-output (MIMO) precoding strategy based on novel findings concerning the subspace separability of Rician fading channels. Considering a uniform rectangular array at the base station, we show that the subspaces spanned by the channel vectors can be factorized as a tensor product between two lower dimensional subspaces. Based on this result, we formulate tensor maximum ratio transmit and zero-forcing precoders. We show that the proposed tensor precoders exhibit lower computational complexity and require less instantaneous channel state information than their linear counterparts. Finally, we present computer simulations that demonstrate the applicability of the proposed tensor precoders in practical communication scenarios.