End-to-End Learning for Integrated Sensing and Communication

被引：10

作者：

Mateos-Ramos, Jose Miguel ^{[1
]}

Song, Jinxiang ^{[1
]}

Wu, Yibo ^{[1
,2
]}

Hager, Christian ^{[1
]}

Keskin, Musa Furkan ^{[1
]}

Yajnanarayana, Vijaya ^{[3
]}

Wymeersch, Henk ^{[1
]}

机构：

[1] Chalmers Univ Technol, Dept Elect Engn, Gothenburg, Sweden

[2] Ericsson Res, Stockholm, Sweden

[3] Ericsson Res, Gurugram, India

来源：

IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2022) | 2022年

基金：

欧盟地平线“2020”;

关键词：

Integrated sensing and communication; Joint radar and communications; Auto-encoder; Machine learning; WAVE-FORM DESIGN; JOINT COMMUNICATION; RADAR; CHALLENGES;

D O I：

10.1109/ICC45855.2022.9838308

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

Integrated sensing and communication (ISAC) aims to unify radar and communication systems through a combination of joint hardware, joint waveforms, joint signal design, and joint signal processing. At high carrier frequencies, where ISAC is expected to play a major role, joint designs are challenging due to several hardware limitations. Model-based approaches, while powerful and flexible, are inherently limited by how well the models represent reality. Under model deficit, data-driven methods can provide robust ISAC performance. We present a novel approach for data-driven ISAC using an auto-encoder (AE) structure. The approach includes the proposal of the AE architecture, a novel ISAC loss function, and the training procedure. Numerical results demonstrate the power of the proposed AE, in particular under hardware impairments.

引用

页码：1942 / 1947

页数：6

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