Sensing Mutual Information for Communication Signal with Deterministic Pilots and Random Data Payloads

2026-01-16

AI Summary

This work addresses a critical limitation in existing integrated sensing and communication (ISAC) systems, which often overlook the hybrid structure of practical signals comprising both deterministic pilots and random data, leading to inaccurate sensing performance evaluation. For the first time, this study derives a closed-form analytical expression for sensing mutual information under such hybrid signaling, leveraging random matrix theory. Building upon this theoretical foundation, the authors propose a precoding optimization framework that jointly enhances sensing and communication performance, formulated as a constrained optimization problem incorporating transmit power and communication rate requirements, and solved efficiently via the alternating direction method of multipliers (ADMM). Simulations validate the accuracy of the derived model and demonstrate that the proposed scheme significantly outperforms conventional benchmarks in terms of sensing performance.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

The recent emergence of the integrated sensing and communication (ISAC) framework has sparked significant interest in quantifying the sensing capabilities inherent in communication signals. However, existing literature has mainly focused on scenarios involving either purely random or purely deterministic waveforms. This overlooks a critical reality: operational communication standards invariably utilize a hybrid structure comprising both deterministic pilots for channel estimation and random payloads for data transmission. To bridge this gap, this paper investigates the sensing mutual information (SMI) and precoding design specifically for ISAC systems employing communication signals with both pilots and data payloads. First, by utilizing random matrix theory (RMT), we derive a tractable closed-form expression for the SMI that accurately accounts for the statistical properties of the hybrid signal. Building upon this theoretical foundation, we formulate a precoding optimization problem to maximize SMI with constraints on the transmit power and communication rate, which is solved via an efficient alternating direction method of multipliers framework. Simulation results validate the accuracy of the theoretical results and demonstrate the superiority of the proposed precoding design over conventional benchmarks.

Problem

• integrated sensing and communication
• sensing mutual information
• deterministic pilots
• random data payloads
• hybrid signal structure

Innovation

• Sensing Mutual Information
• Integrated Sensing and Communication
• Hybrid Signal Structure
• Random Matrix Theory
• Precoding Optimization