Multimodal Learning for Integrated Sensing and Communication Networks

被引：0

作者：

Liu, Xiaonan ^{[1
]}

Ratnarajah, Tharmalingam ^{[1
]}

Sellathurai, Mathini ^{[2
]}

Eldar, Yonina C. ^{[3
]}

机构：

[1] Univ Edinburgh, Sch Engn, Edinburgh, Midlothian, Scotland

[2] Heriot Watt Univ, Dept Engn & Phys Sci, Edinburgh, Midlothian, Scotland

[3] Weizmann Inst Sci, Fac Math & Comp Sci, Rehovot, Israel

来源：

32ND EUROPEAN SIGNAL PROCESSING CONFERENCE, EUSIPCO 2024 | 2024年

关键词：

Integrated sensing and communication; multimodal learning; RADAR; EDGE;

D O I：

10.23919/EUSIPCO63174.2024.10715410

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Integrated sensing and communication (ISAC) is a promising technique for beyond 5G networks. In ISAC networks, the sensed environmental data may be multimodal data, which may result in high computation and communication latency due to the large size of data modalities and limited computation capability of mobile devices. To solve the problem, in this paper, we propose multimodal learning in ISAC networks. Simulation results show that the proposed multimodal learning design significantly outperforms several benchmarks without considering multimodal data sensing and communication.

引用

页码：1177 / 1181

页数：5

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