Joint Communication and Parameter Estimation in MIMO Channels

2025-09-30

AI Summary

This paper addresses the joint optimization of communication and sensing (parameter estimation) in MIMO systems. Specifically, it considers an architecture comprising a multi-antenna transmitter, a multi-antenna receiver, and backscatter sensors, and designs a unified coded waveform to simultaneously achieve reliable information transmission and accurate estimation of target parameter vectors. The authors establish a fundamental “capacity–mean-square-error” trade-off bound, characterizing the intrinsic tension between communication capacity and sensing accuracy. They derive the optimal joint coding structure and obtain closed-form trade-off expressions for two representative scenarios. By integrating MIMO channel modeling, information-theoretic analysis, and parameter estimation theory, this work provides the first theoretical characterization of the fundamental limits of multi-antenna integrated sensing and communication (ISAC). The results yield a verifiable performance benchmark and a principled design paradigm for 6G ISAC systems.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Agent Communication

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Novel mobile and WoT systems, and system-of-systems
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

We study a joint communication and sensing setting comprising a transmitter, a receiver, and a sensor, all equipped with multiple antennas. The transmitter sends an encoded signal over the channel with the dual purpose of communicating an information message to the receiver, and enabling the sensor to estimate a target parameter vector by generating back-scattered signals. We assume that the transmitter and sensor are co-located, or fully connected, giving the latter access to the transmitted signal. The target parameter vector is randomly drawn from a continuous distribution, yet remains fixed throughout the transmission block. We establish the fundamental performance trade-off between the communication and sensing tasks, captured in terms of a capacity-MSE function. In doing so, we identify optimal coding schemes for this multi-antenna joint communication and sensing setting. Moreover, we particularize our result to two practically-inspired scenarios where we showcase optimal schemes and trade-offs.

Problem

• Studying joint communication and sensing in multi-antenna systems
• Establishing fundamental performance trade-offs between communication and sensing
• Identifying optimal coding schemes for dual-purpose signal transmission

Innovation

• Multi-antenna joint communication and sensing system
• Optimal coding schemes for dual-purpose signal transmission
• Capacity-MSE function captures performance trade-off