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

Ruoyu Zhang, Lei Cheng, Shuai Wang, Yi Lou, Yulong Gao, Wen Wu, Derrick Wing Kwan Ng

TL;DR

This work addresses the challenge of jointly estimating wireless channels and target parameters in a massive MIMO-ISAC system. It introduces a unified tensor framework that leverages a shared training pattern to formulate both channel estimation and target parameter estimation as structured CPD problems, exploiting Vandermonde structures to enhance identifiability. The proposed algorithms recover AoA, AoD, delay, Doppler, and reflection coefficients, with extensions to handle beam squint via segment-based training. Theoretical analysis provides relaxed uniqueness conditions, and simulations show improved resolvability, reduced training overhead, and robust performance under beam squint. The approach has practical significance for high-efficiency sensing and communication in 6G-like systems, enabling simultaneous high-resolution radar sensing and reliable downlink communication using shared resources.

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

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

TL;DR

This work addresses the challenge of jointly estimating wireless channels and target parameters in a massive MIMO-ISAC system. It introduces a unified tensor framework that leverages a shared training pattern to formulate both channel estimation and target parameter estimation as structured CPD problems, exploiting Vandermonde structures to enhance identifiability. The proposed algorithms recover AoA, AoD, delay, Doppler, and reflection coefficients, with extensions to handle beam squint via segment-based training. Theoretical analysis provides relaxed uniqueness conditions, and simulations show improved resolvability, reduced training overhead, and robust performance under beam squint. The approach has practical significance for high-efficiency sensing and communication in 6G-like systems, enabling simultaneous high-resolution radar sensing and reliable downlink communication using shared resources.

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
Paper Structure (18 sections, 3 theorems, 62 equations, 8 figures, 2 tables, 2 algorithms)

This paper contains 18 sections, 3 theorems, 62 equations, 8 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Massive MIMO-ISAC System Model
  3. Communication Transmission Model
  4. Radar Sensing Model
  5. Unified Model for Massive MIMO-ISAC
  6. Unified Tensor Formulation and Analysis for Channel and Target Parameter Estimation
  7. Unified Tensor Modeling for Channel and Target Parameter Estimation
  8. Uniqueness Property Analysis for Unified Tensor Formulation
  9. Unified Channel and Target Parameter Estimation Algorithm
  10. Estimation of Factor Matrices
  11. Extraction of Unknown Parameters
  12. Discussions
  13. Parameter Estimation With Beam Squint Effects
  14. CPD Formulation With Beam Squint Effects
  15. Uniqueness Condition
  16. ...and 3 more sections

Key Result

Lemma 1

Let $\mathbf{B}^{(1)} \in \mathbb{C}^{I_{1} \times Q}$, $\mathbf{B}^{(2)}\in \mathbb{C}^{I_{2} \times Q}$, and $\mathbf{B}^{(3)}\in \mathbb{C}^{I_{3} \times Q}$ be the factor matrices of $\bm{\mathcal{X}} \in \mathbb{C}^{I_{1} \times I_{2} \times I_{3}}$. Suppose the Kruskal’s condition $k_{\mathbf{

Figures (8)

  • Figure 1: Massive MIMO-ISAC system model and the associated communication/sensing channel models.
  • Figure 2: Target parameter estimation performance for the benchmark algorithm (left column) and the proposed Algorithm 1 (right column), where SNR $=10$ dB, $N = 16$, $K = 16$. The first row shows the AoA estimation; The second row shows the AoD and range estimation (polar axis); The third row shows the range and velocity estimation (x-axis range, y-axis velocity).
  • Figure 3: The success rate of correctly distinguishing the targets.
  • Figure 4: The RMSE performance of the AoD and AoA estimation versus SNR.
  • Figure 5: The RMSE performance of the range and velocity estimation versus SNR.
  • ...and 3 more figures

Theorems & Definitions (6)

  • Lemma 1
  • proof
  • Lemma 2
  • proof
  • Proposition 1
  • proof