Learning-Based Constellation Design for Uplink Bi-Static Integrated Sensing and Communication

This paper proposes an end-to-end deep learning based constellation design for integrated sensing and communication (ISAC) for the uplink of orthogonal frequency division multiplexing (OFDM) systems. Utilizing an auto-encoder architecture, the system designs and optimizes constellation mappings to b...

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Vydáno v:IEEE transactions on vehicular technology Ročník 74; číslo 8; s. 13219 - 13224
Hlavní autoři: Hu, Jiaming, Han, Kawon, Jiang, Lai, Meng, Kaitao, Liu, Fan, Masouros, Christos
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.08.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Deep learning
Integrated sensing and communication
Integrated sensing and communications (ISAC)
Measurement
multiple imput and multiple output (MIMO)
OFDM
Orthogonal Frequency Division Multiplexing
orthogonal frequency division multiplexing (OFDM)
precoder design
Radar
Receivers
Symbols
Time-domain analysis
Training
Uplink
Uplinking
Vectors
ISSN:0018-9545, 1939-9359
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Shrnutí:This paper proposes an end-to-end deep learning based constellation design for integrated sensing and communication (ISAC) for the uplink of orthogonal frequency division multiplexing (OFDM) systems. Utilizing an auto-encoder architecture, the system designs and optimizes constellation mappings to balance the trade-off between communication and sensing performance under a bi-static scenario where receiver has no knowledge about transmitted signals. The constellation design is trained to adapt to specific channel conditions, offering flexible control over the communication-sensing performances by adjusting a radar weighting factor. Simulation results show that this design outperforms conventional constellation formats such as 64-QAM and 64-PSK in symbol error rate (SER), while outperforming the 64-QAM in sensing error. Furthermore, the proposed constellation design demonstrates robust performance even under channel state information (CSI) errors of up to 1.5%.
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2025.3554439