[Paper Review] Integrated Sensing and Communications Framework for 6G Networks

Hongliang Luo, Tengyu Zhang+6 more|arXiv (Cornell University)|May 30, 2024

Advanced Wireless Communication Technologies4 citations

TL;DR

This paper proposes a novel 6G Integrated Sensing and Communications (ISAC) framework that decomposes physical world sensing into static environment reconstruction (SER), dynamic target sensing (DTS), and object material recognition (OMR). By leveraging multi-BS and multi-UE cooperation with OFDM signals, the framework enables real-time, high-precision sensing of buildings, moving targets, and material properties while maintaining 1.5 Gbps communications, validated via an FPGA-based hardware prototype with 0.261m SER error and 0.183m distance resolution.

ABSTRACT

In this paper, we propose a novel integrated sensing and communications (ISAC) framework for the sixth generation (6G) mobile networks, in which we decompose the real physical world into static environment, dynamic targets, and various object materials. The ubiquitous static environment occupies the vast majority of the physical world, for which we design static environment reconstruction (SER) scheme to obtain the layout and point cloud information of static buildings. The dynamic targets floating in static environments create the spatiotemporal transition of the physical world, for which we design comprehensive dynamic target sensing (DTS) scheme to detect, estimate, track, image and recognize the dynamic targets in real-time. The object materials enrich the electromagnetic laws of the physical world, for which we develop object material recognition (OMR) scheme to estimate the electromagnetic coefficient of the objects. Besides, to integrate these sensing functions into existing communications systems, we discuss the interference issues and corresponding solutions for ISAC cellular networks. Furthermore, we develop an ISAC hardware prototype platform that can reconstruct the environmental maps and sense the dynamic targets while maintaining communications services. With all these designs, the proposed ISAC framework can support multifarious emerging applications, such as digital twins, low altitude economy, internet of vehicles, marine management, deformation monitoring, etc.

Motivation & Objective

• To unify diverse sensing functions—static environment, dynamic targets, and object materials—into a coherent ISAC framework for 6G networks.
• To address the lack of a systematic, integrated approach in existing ISAC research that focuses on isolated tasks like target detection or localization.
• To enable seamless integration of sensing and communications in dense 6G cellular networks, particularly under multi-BS and multi-UE cooperation.
• To develop a practical hardware prototype that supports real-time environmental mapping and dynamic target sensing without compromising communication performance.
• To provide a scalable, interference-aware solution for ISAC networks with coordinated resource allocation and beamforming.

Proposed method

• Proposes a three-tiered ISAC framework: static environment reconstruction (SER), dynamic target sensing (DTS), and object material recognition (OMR), each addressing distinct physical world components.
• Employs deep learning (DL), multi-UE selection, multi-BS fusion, and multi-sensor fusion for SER to estimate building layouts and point clouds using non-line-of-sight (NLoS) channel parameters.
• Designs a comprehensive DTS scheme based on OFDM signals, incorporating clutter suppression, target detection, parameter estimation, track management, and imaging for real-time dynamic target tracking.
• Develops an electromagnetic coefficient estimation method using compressive sensing to enable object material recognition (OMR) from backscattered signals.
• Introduces interference management strategies for ISAC cellular networks, including power and bandwidth allocation optimization via alternating optimization of continuous and discrete variables.
• Builds an FPGA-based hardware prototype using 26 GHz (SER) and 5.5 GHz (DTS) bands, with 32-antenna ULA at BS and UE for SER, and 8-receiving/2-transmitting antennas for DTS, supporting 820 MHz bandwidth and 64 OFDM symbols.

Experimental results

Research questions

• RQ1 How can the physical world be systematically decomposed into static environments, dynamic targets, and object materials for unified ISAC functionality in 6G?
• RQ2 What signal processing techniques enable high-precision static environment reconstruction (SER) using multi-BS and multi-UE cooperation with OFDM signals?
• RQ3 How can a comprehensive, real-time dynamic target sensing (DTS) framework be designed to support detection, tracking, imaging, and recognition using OFDM waveforms?
• RQ4 What is an effective method for estimating electromagnetic coefficients of objects to enable material recognition (OMR) in ISAC systems?
• RQ5 How can interference be effectively managed in dense ISAC cellular networks with coexisting communications and sensing functions?

Key findings

• The SER hardware prototype achieved a 0.2610m average error in reconstructing building point clouds while maintaining a downlink communication rate above 1.5 Gbps.
• The SER system demonstrated a distance resolution of 0.183m and an angular resolution of 3°, with a 0.0347m average error in UE localization.
• The DTS hardware prototype achieved a velocity resolution of 0.42 m/s, a distance resolution of 0.183 m, and an angular resolution of 3°, with stable 5.5 GHz OFDM communications.
• The system maintained 1.5 Gbps downlink throughput during SER operations, confirming that high-rate communications are compatible with high-precision sensing.
• The proposed power and bandwidth allocation optimization reduced network interference and improved ISAC performance through alternating optimization of continuous and discrete variables.
• The integration of DL-based, multi-UE, multi-BS, and multi-sensor fusion techniques significantly enhanced SER accuracy and robustness in complex propagation environments.