Sensing With Communication Signals: From Information Theory to Signal Processing

TL;DR

This paper provides a comprehensive overview of the theoretical foundations and signal processing techniques for integrated sensing and communication systems in 6G, focusing on the use of random communication signals for dual purposes.

Contribution

It introduces the deterministic-random tradeoff from an information-theoretic perspective and reviews signal models, processing pipelines, and design principles for communication-centric ISAC systems.

Findings

Analysis of the average squared auto-correlation function for random ISAC signals

Design principles for modulation, constellation, and pulse shaping in ISAC

Extension of single-antenna analysis to multi-antenna MIMO systems

Abstract

The Integrated Sensing and Communications (ISAC) paradigm is anticipated to be a cornerstone of the upcoming 6G networks. In order to optimize the use of wireless resources, 6G ISAC systems need to harness the communication data payload signals, which are inherently random, for both sensing and communication (S&C) purposes. This tutorial paper provides a comprehensive technical overview of the fundamental theory and signal processing methodologies for ISAC transmission with random communication signals. We begin by introducing the deterministic-random tradeoff (DRT) between S&C from an information-theoretic perspective, emphasizing the need for specialized signal processing techniques tailored to random ISAC signals. Building on this foundation, we review the core signal models and processing pipelines for communication-centric ISAC systems, and analyze the average squared auto-correlation function (ACF) of random ISAC signals, which serves as a fundamental performance metric for multi-target ranging tasks. Drawing insights from these theoretical results, we outline the design principles for the three key components of communication-centric ISAC systems: modulation schemes, constellation design, and pulse shaping filters. The goal is to either enhance sensing performance without compromising communication efficiency or to establish a scalable tradeoff between the two. We then extend our analysis from a single-antenna ISAC system to its multi-antenna counterpart, discussing recent advancements in multi-input multi-output (MIMO) precoding techniques specifically designed for random ISAC signals. We conclude by highlighting several open challenges and future research directions in the field of sensing with communication signals.