Multicarrier ISAC: Advances in Waveform Design, Signal Processing and Learning under Non-Idealities

TL;DR

This paper reviews recent advances in multicarrier ISAC systems, focusing on waveform design, signal processing, and learning techniques to enhance joint sensing and communication performance in complex radio environments.

Contribution

It provides a comprehensive overview of waveform co-design, optimization strategies, and learning-based methods for multicarrier ISAC, addressing practical non-idealities and dynamic scenarios.

Findings

Trade-offs between communication and sensing performance analyzed

Model-based and learning-based optimization methods compared

Techniques for overcoming transceiver non-idealities discussed

Abstract

This paper addresses the topic of integrated sensing and communications (ISAC) in 5G and emerging 6G wireless networks. ISAC systems operate within shared, congested or even contested spectrum, aiming to deliver high performance in both wireless communications and radio frequency (RF) sensing. The expected benefits include more efficient utilization of spectrum, power, hardware (HW) and antenna resources. Focusing on multicarrier (MC) systems, which represent the most widely used communication waveforms, it explores the co-design and optimization of waveforms alongside multiantenna transceiver signal processing for communications and both monostatic and bistatic sensing applications of ISAC. Moreover, techniques of high practical relevance for overcoming and even harnessing challenges posed by non-idealities in actual transceiver implementations are considered. To operate in highly dynamic radio environments and target scenarios, both model-based structured optimization and learning-based methodologies for ISAC systems are covered, assessing their adaptability and learning capabilities under real-world conditions. The paper presents trade-offs in communication-centric and radar-sensing-centric approaches, aiming for an optimized balance in densely used spectrum.