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

arXiv: https://arxiv.org/abs/2401.01738 · HTML: https://mlsanity.com/paper/2401.01738/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2024.

## Built on by (most cited first, 15 of 15)

- [SIMAC: A Semantic-Driven Integrated Multimodal Sensing And Communication Framework](https://mlsanity.com/paper/2503.08726/) (2025)
- [CSI-GPT: Integrating Generative Pre-Trained Transformer with Federated-Tuning to Acquire Downlink Massive MIMO Channels](https://mlsanity.com/paper/2406.03438/) (2024)
- [Near-Field Channel Estimation for XL-MIMO: A Deep Generative Model Guided by Side Information](https://mlsanity.com/paper/2505.06900/) (2025)
- [STT-GS: Sample-Then-Transmit Edge Gaussian Splatting with Joint Client Selection and Power Control](https://mlsanity.com/paper/2510.13186/) (2025)
- [Model-Based Learning for DOA Estimation with One-Bit Single-Snapshot Sparse Arrays](https://mlsanity.com/paper/2502.17473/) (2025)
- [Channel Fingerprint Construction for Massive MIMO: A Deep Conditional Generative Approach](https://mlsanity.com/paper/2505.07893/) (2025)
- [When Bayesian Tensor Completion Meets Multioutput Gaussian Processes: Functional Universality and Rank Learning](https://mlsanity.com/paper/2512.21486/) (2025)
- [Downlink MIMO Channel Estimation from Bits: Recoverability and Algorithm](https://mlsanity.com/paper/2411.16043/) (2024)
- [PLAIN: Scalable Estimation Architecture for Integrated Sensing and Communication](https://mlsanity.com/paper/2503.21242/) (2025)
- [Accelerating Large-Scale Regularized High-Order Tensor Recovery](https://mlsanity.com/paper/2506.09594/) (2025)
- [Efficient Beam Selection for ISAC in Cell-Free Massive MIMO via Digital Twin-Assisted Deep Reinforcement Learning](https://mlsanity.com/paper/2506.18560/) (2025)
- [FuseFi: Combining Irregularly Sampled CSI from Diverse Communication Packets and Frequency Bands for Wi-Fi Sensing](https://mlsanity.com/paper/2512.22143/) (2025)
- [Structure-Informed Estimation for Pilot-Limited MIMO Channels via Tensor Decomposition](https://mlsanity.com/paper/2602.04083/) (2026)
- [Deep Learning-Driven Friendly Jamming for Secure Multicarrier ISAC Under Channel Uncertainty](https://mlsanity.com/paper/2603.05062/) (2026)
- [Statistical Channel Fingerprint Construction for Massive MIMO: A Unified Tensor Learning Framework](https://mlsanity.com/paper/2604.27574/) (2026)

## Abstract (arXiv)

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