Decentralized Hybrid Precoding for Massive MU-MIMO ISAC

TL;DR

This paper proposes a decentralized hybrid precoding method for Massive MU-MIMO ISAC systems to reduce multi-user interference, improve data rates, and lower hardware complexity in dense user scenarios.

Contribution

It introduces a novel decentralized baseband processing precoding approach that models MUI with CRB minimization and employs hybrid precoding with PCS to enhance performance.

Findings

Significant improvement in data rates and energy efficiency.

Reduction in hardware complexity for Massive MU-MIMO ISAC.

Effective mitigation of multi-user interference in dense scenarios.

Abstract

Integrated sensing and communication (ISAC) is a very promising technology designed to provide both high rate communication capabilities and sensing capabilities. However, in Massive Multi User Multiple-Input Multiple-Output (Massive MU MIMO-ISAC) systems, the dense user access creates a serious multi-user interference (MUI) problem, leading to degradation of communication performance. To alleviate this problem, we propose a decentralized baseband processing (DBP) precoding method. We first model the MUI of dense user scenarios with minimizing Cramer-Rao bound (CRB) as an objective function.Hybrid precoding is an attractive ISAC technique, and hybrid precoding using Partially Connected Structures (PCS) can effectively reduce hardware cost and power consumption. We mitigate the MUI between dense users based on ThomlinsonHarashima Precoding (THP). We demonstrate the effectiveness of the proposed method through simulation experiments. Compared with the existing methods, it can effectively improve the communication data rates and energy efficiency in dense user access scenario, and reduce the hardware complexity of Massive MU MIMO-ISAC systems. The experimental results demonstrate the usefulness of our method for improving the MUI problem in ISAC systems for dense user access scenarios.