Integrated Sensing and Communication with Massive MIMO: A Unified Tensor Approach for Channel and Target Parameter Estimation

TL;DR

This paper introduces a unified tensor-based framework for simultaneous channel estimation and target sensing in massive MIMO-ISAC systems, leveraging tensor decomposition to improve efficiency and resolution.

Contribution

It proposes a novel tensor approach that jointly estimates channel and target parameters, addressing challenges of high-dimensional data in massive MIMO-ISAC systems.

Findings

Unified tensor model for channel and target estimation

Analysis of tensor factorization uniqueness conditions

Enhanced resolution of targets using tensor decomposition

Abstract

Benefitting from the vast spatial degrees of freedom, the amalgamation of integrated sensing and communication (ISAC) and massive multiple-input multiple-output (MIMO) is expected to simultaneously improve spectral and energy efficiencies as well as the sensing capability. However, a large number of antennas deployed in massive MIMO-ISAC raises critical challenges in acquiring both accurate channel state information and target parameter information. To overcome these two challenges with a unified framework, we first analyze their underlying system models and then propose a novel tensor-based approach that addresses both the channel estimation and target sensing problems. Specifically, by parameterizing the high-dimensional communication channel exploiting a small number of physical parameters, we associate the channel state information with the sensing parameters of targets in terms of angular, delay, and Doppler dimensions. Then, we propose a shared training pattern adopting the same time-frequency resources such that both the channel estimation and target parameter estimation can be formulated as a canonical polyadic decomposition problem with a similar mathematical expression. On this basis, we first investigate the uniqueness condition of the tensor factorization and the maximum number of resolvable targets by utilizing the specific Vandermonde