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

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 structure. Then, we develop a unified tensor-based algorithm to estimate the parameters including angles, time delays, Doppler shifts, and reflection/path coefficients of the targets/channels. In addition, we propose a segment-based shared training pattern to facilitate the channel and target parameter estimation for the case with significant beam squint effects. Simulation results verify our theoretical analysis and the superiority of the proposed unified algorithms in terms of estimation accuracy, sensing resolution, and training overhead reduction.

I. Introduction

Integrated sensing and communication (ISAC), which aims at unifying two functionalities into a single system to improve spectral and energy efficiencies, has been envisioned as a promising component for realizing the sixth-generation (6G) wireless communications [1], [2], [3]. Meanwhile, massive multiple-input multiple-output (MIMO), which advocates a large antenna array at the base station (BS), has become one of the most important enabling technologies for current and future wireless cellular networks [4], [5]. Profiting from the vast spatial degrees of freedom and the endogenous sensing capability, the integration of massive MIMO and ISAC (MIMO-ISAC) is expected to simultaneously guarantee high-quality wireless communication as well as high-resolution and robust sensing, thus enabling the rapid development of various emerging applications including autonomous driving in intelligent transportation and unmanned aerial vehicle networks in smart cities [6], [7], etc.