LISAC: Learned Coded Waveform Design for ISAC with OFDM

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Opis bibliograficzny
Wydane w:	arXiv.org (Oct 14, 2024), p. n/a
1. autor:	Bian, Chenghong
Kolejni autorzy:	Zhang, Yumeng, Gunduz, Deniz
Wydane:	Cornell University Library, arXiv.org
Hasła przedmiotowe:	Recurrent neural networks Waveforms Bit error rate Error analysis Decoding Machine learning Maximum likelihood decoding Communication Orthogonal Frequency Division Multiplexing Artificial neural networks Neural networks Target detection
Dostęp online:	Citation/Abstract Full text outside of ProQuest
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Opis
Streszczenie:	We propose a novel deep learning based method to design a coded waveform for integrated sensing and communication (ISAC) system based on orthogonal frequency-division multiplexing (OFDM). Our ultimate goal is to design a coded waveform, which is capable of providing satisfactory sensing performance of the target while maintaining high communication quality measured in terms of the bit error rate (BER). The proposed LISAC provides an improved waveform design with the assistance of deep neural networks for the encoding and decoding of the information bits. In particular, the transmitter, parameterized by a recurrent neural network (RNN), encodes the input bit sequence into the transmitted waveform for both sensing and communications. The receiver employs a RNN-based decoder to decode the information bits while the transmitter senses the target via maximum likelihood detection. We optimize the system considering both the communication and sensing performance. Simulation results show that the proposed LISAC waveform achieves a better trade-off curve compared to existing alternatives.
ISSN:	2331-8422
Źródło:	Engineering Database