Electromagnetic Property Sensing: A New Paradigm of Integrated Sensing and Communication

被引:1
|
作者
Jiang, Yuhua [1 ,2 ,3 ]
Gao, Feifei [1 ,2 ,3 ]
Jin, Shi [4 ]
机构
[1] Tsinghua Univ THUAI, Inst Artificial Intelligence, Beijing 100190, Peoples R China
[2] Tsinghua Univ THUAI, State Key Lab Intelligent Technol & Syst, Beijing 100190, Peoples R China
[3] Beijing Natl Res Ctr Informat Sci & Technol BNRis, Beijing 100084, Peoples R China
[4] Southeast Univ, Natl Mobile Commun Res Lab, Nanjing 210096, Peoples R China
基金
中国国家自然科学基金;
关键词
Sensors; Vectors; Transmitters; OFDM; Receivers; Image reconstruction; Array signal processing; Electromagnetic property sensing; material identification; integrated sensing and communication (ISAC); compressive sensing; orthogonal frequency division multiplexing (OFDM); BORN; SCATTERING; ISAC;
D O I
10.1109/TWC.2024.3401859
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Integrated sensing and communication (ISAC) has opened up numerous game-changing opportunities for future wireless systems. In this paper, we develop a novel scheme that utilizes orthogonal frequency division multiplexing (OFDM) pilot signals in ISAC systems to sense the electromagnetic (EM) property of the target and thus also identify the material of the target. Specifically, we first establish an end-to-end EM propagation model by means of Maxwell equations, where the EM property of the target is captured by a closed-form expression of the ISAC channel, incorporating the Lippmann-Schwinger equation and the method of moments (MOM) for discretization. We then model the relative permittivity and conductivity distribution (RPCD) within a specified detection region. Based on the sensing model, we introduce a multi-frequency-based EM property sensing method by which the RPCD can be reconstructed from compressive sensing techniques that exploits the joint sparsity structure of the EM property vector. To improve the sensing accuracy, we design a beamforming strategy from the communications transmitter based on the Born approximation that can minimize the mutual coherence of the sensing matrix. The optimization problem is cast in terms of the Gram matrix and is solved iteratively to obtain the optimal beamforming matrix. Simulation results demonstrate the efficacy of the proposed method in achieving high-quality RPCD reconstruction and accurate material classification. Furthermore, improvements in RPCD reconstruction quality and material classification accuracy are observed with increased signal-to-noise ratio (SNR) or reduced target-transmitter distance.
引用
收藏
页码:13471 / 13483
页数:13
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