Deep Learning-Empowered Predictive Precoder Design for OTFS Transmission in URLLC

Chang Liu1; Shuangyang Li2; Weijie Yuan3; Xuemeng Liu4; Derrick Wing Kwan Ng5

10.1109/ICC45041.2023.10278742

2023-05-28

IEEE International Conference on Communications (IEEE ICC)

1938-1883

978-1-5386-7463-5

ICC 2023 - IEEE International Conference on Communications

2023-May

5651-5657

28 May-1 June 2023

Rome, Italy

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

To guarantee excellent reliability performance in ultra-reliable low-latency communications (URLLC), pragmatic precoder design is an effective approach. However, an efficient precoder design highly depends on the accurate instantaneous channel state information at the transmitter (ICSIT), which however, is not always available in practice. To overcome this problem, in this paper, we focus on the orthogonal time frequency space (OTFS)-based URLLC system and adopt a deep learning (DL) approach to directly predict the precoder for the next time frame to minimize the frame error rate (FER) via implicitly exploiting the features from estimated historical channels in the delay-Doppler domain. By doing this, we can guarantee the system reliability even without the knowledge of ICSIT. To this end, a general precoder design problem is formulated where a closed-form theoretical FER expression is specifically derived to characterize the system reliability. Then, a delay-Doppler domain channels-aware convolutional long short-term memory (CLSTM) network (DDCL-Net) is proposed for predictive precoder design. In particular, both the convolutional neural network and LSTM modules are adopted in the proposed neural network to exploit the spatial-temporal features of wireless channels for improving the learning performance. Finally, simulation results demonstrated that the FER performance of the proposed method approaches that of the perfect ICSI-aided scheme.

Wireless communication Time-frequency analysis Transmitters Simulation Receivers Ultra reliable low latency communication Reliability engineering

其他

英语

EI ; CPCI-S

Telecommunications

Telecommunications

WOS:001094862605124

20234815114190

Channel state information ; Convolution ; Convolutional neural networks ; Learning systems ; Reliability

Information Theory and Signal Processing:716.1

IEEE

1.Department of Electronic and Information Engineering, The Hong Kong Polytechnic University
2.School of Engineering, University of Western Australia, Perth, Australia
3.Department of Electronic and Electrical Engineering, Southern University of Science and Technology, China
4.School of Electrical and Information Engineering, University of Sydney, Sydney, Australia
5.School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, Australia

Chang Liu,Shuangyang Li,Weijie Yuan,et al. Deep Learning-Empowered Predictive Precoder Design for OTFS Transmission in URLLC[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2023:5651-5657.