Toward Seamless Sensing Coverage for Cellular Multi-Static Integrated Sensing and Communication

The sixth generation (6G) mobile communication networks are expected to offer a new paradigm of cellular integrated sensing and communication (ISAC). However, due to the intrinsic difference between wireless sensing and communication in terms of coverage requirement, current cellular networks that a...

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Published inIEEE transactions on wireless communications Vol. 23; no. 6; pp. 5363 - 5376
Main Authors Li, Ruoguang, Xiao, Zhiqiang, Zeng, Yong
Format Journal Article
LanguageEnglish
Published New York IEEE 01.06.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Array signal processing
Beamforming
Beamforming optimization
bi-static
Cellular communication
Communication
Communication networks
Communications equipment
coordinated multi-point with joint transmission (CoMP-JT)
integrated sensing and communication (ISAC)
multi-static
Optimization
Radar
Radar cross-sections
Sensors
Signal processing
Signal to noise ratio
Wireless communication
Online AccessGet full text
ISSN1536-1276
1558-2248
DOI10.1109/TWC.2023.3325849

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Abstract The sixth generation (6G) mobile communication networks are expected to offer a new paradigm of cellular integrated sensing and communication (ISAC). However, due to the intrinsic difference between wireless sensing and communication in terms of coverage requirement, current cellular networks that are deliberately planned mainly for communication coverage are difficult to achieve seamless sensing coverage. Therefore, this paper studies the coverage issue for cellular ISAC systems, which aims to concurrently sense a prescribed region while serving a group of communication users equipment (UEs). Towards this end, the radar sensing signal processing procedures and communication signal models are presented in a general multi-static cellular ISAC system with coordinated multi-point joint transmission (CoMP-JT), and an optimization problem is formulated to maximize the worst-case sensing signal-to-noise ratio (SNR) in the prescribed sensing coverage region, subject to the signal-to-interference-plus-noise ratio (SINR) requirement for each communication UE. To gain useful insights, we first investigate the basic bi-static ISAC system, for which a closed form expression of the optimal beamforming is obtained for the special case with one UE and one sensing point. Then, the general case with multiple communication UEs and contiguous regional sensing coverage is further studied, in which the mesh grid approach and direction discretization approach are proposed for ease of solving the optimization problem. Afterward, we further investigate the beamforming optimization in the multi-static ISAC system to maximize the probability of detection of the prescribed sensing coverage region. We show that the problem is equivalent to maximize the sum of sensing SNR from the different transmit BSs. The formulated problems are non-convex, and we propose an efficient algorithm based on successive convex approximation (SCA) technique. Numerical results demonstrate that the proposed ISAC design is able to achieve seamless sensing coverage in the prescribed region while guaranteeing the communication requirements of the UEs.
AbstractList The sixth generation (6G) mobile communication networks are expected to offer a new paradigm of cellular integrated sensing and communication (ISAC). However, due to the intrinsic difference between wireless sensing and communication in terms of coverage requirement, current cellular networks that are deliberately planned mainly for communication coverage are difficult to achieve seamless sensing coverage. Therefore, this paper studies the coverage issue for cellular ISAC systems, which aims to concurrently sense a prescribed region while serving a group of communication users equipment (UEs). Towards this end, the radar sensing signal processing procedures and communication signal models are presented in a general multi-static cellular ISAC system with coordinated multi-point joint transmission (CoMP-JT), and an optimization problem is formulated to maximize the worst-case sensing signal-to-noise ratio (SNR) in the prescribed sensing coverage region, subject to the signal-to-interference-plus-noise ratio (SINR) requirement for each communication UE. To gain useful insights, we first investigate the basic bi-static ISAC system, for which a closed form expression of the optimal beamforming is obtained for the special case with one UE and one sensing point. Then, the general case with multiple communication UEs and contiguous regional sensing coverage is further studied, in which the mesh grid approach and direction discretization approach are proposed for ease of solving the optimization problem. Afterward, we further investigate the beamforming optimization in the multi-static ISAC system to maximize the probability of detection of the prescribed sensing coverage region. We show that the problem is equivalent to maximize the sum of sensing SNR from the different transmit BSs. The formulated problems are non-convex, and we propose an efficient algorithm based on successive convex approximation (SCA) technique. Numerical results demonstrate that the proposed ISAC design is able to achieve seamless sensing coverage in the prescribed region while guaranteeing the communication requirements of the UEs.
Author Li, Ruoguang
Zeng, Yong
Xiao, Zhiqiang
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Snippet The sixth generation (6G) mobile communication networks are expected to offer a new paradigm of cellular integrated sensing and communication (ISAC). However,...
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SubjectTerms Algorithms
Array signal processing
Beamforming
Beamforming optimization
bi-static
Cellular communication
Communication
Communication networks
Communications equipment
coordinated multi-point with joint transmission (CoMP-JT)
integrated sensing and communication (ISAC)
multi-static
Optimization
Radar
Radar cross-sections
Sensors
Signal processing
Signal to noise ratio
Wireless communication
Title Toward Seamless Sensing Coverage for Cellular Multi-Static Integrated Sensing and Communication
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Volume 23
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