Predictive Precoder Design for OTFS-Enabled URLLC: A Deep Learning Approach

Liu, Chang1; Li, Shuangyang2; Yuan, Weijie3; Liu, Xuemeng4; Ng, Derrick Wing Kwan5

2023-07-01

10.1109/JSAC.2023.3280984

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS   影响因子和分区

0733-8716

1558-0008

This paper investigates the orthogonal time frequency space (OTFS) transmission for enabling ultra-reliable low-latency communications (URLLC). To guarantee excellent reliability performance, pragmatic precoder design is an effective and indispensable solution. However, the design requires accurate instantaneous channel state information at the transmitter (ICSIT) which is not always available in practice. Motivated by this, we adopt a deep learning (DL) approach to exploit implicit features from estimated historical delay-Doppler domain channels (DDCs) to directly predict the precoder to be adopted in the next time frame for minimizing the frame error rate (FER), that can further improve the system reliability without the acquisition of ICSIT. To this end, we first establish a predictive transmission protocol and formulate a general problem for the precoder design where a closed-form theoretical FER expression is derived serving as the objective function to characterize the system reliability. Then, we propose a DL-based predictive precoder design framework which exploits an unsupervised learning mechanism to improve the practicability of the proposed scheme. As a realization of the proposed framework, we design a DDCs-aware convolutional long short-term memory (CLSTM) network for the precoder design, where both the convolutional neural network and LSTM modules are adopted to facilitate the spatial-temporal feature extraction from the estimated historical DDCs to further enhance the precoder performance. Simulation results demonstrate that the proposed scheme facilitates a flexible reliability-latency tradeoff and achieves an excellent FER performance that approaches the lower bound obtained by a genie-aided benchmark requiring perfect ICSI at both the transmitter and receiver.

Index Terms- Ultra-reliable low-latency communications (URLLC) orthogonal time frequency space (OTFS) predictive precoder design deep learning

SCI ; EI

英语

通讯

National Natural Science Foundation of China[62101232] ; Guangdong Provincial Natural Science Foundation[2022A1515011257] ; Shenzhen Science and Technology Program[JCYJ20220530114412029] ; Australian Research Council's Discovery Project["DP210102169","DP230100603"]

Engineering ; Telecommunications

Engineering, Electrical & Electronic ; Telecommunications

WOS:001024232900019

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

20232314199662

Benchmarking ; Channel estimation ; Channel state information ; Convolution ; Extraction ; Frequency estimation ; Long short-term memory ; Signal encoding ; Testbeds ; Transmitters

Information Theory and Signal Processing:716.1 ; Computer Applications:723.5 ; Chemical Operations:802.3

COMPUTER SCIENCE

Web of Science

1.Hong Kong Polytech Univ, Dept Elect & Informat Engn, Hong Kong, Peoples R China
2.Tech Univ Berlin, Dept Elect Engn & Comp Sci, D-10587 Berlin, Germany
3.Southern Univ Sci & Technol, Dept Elect & Elect Engn, Shenzhen 518055, Peoples R China
4.Univ Sydney, Sch Elect & Informat Engn, Sydney, NSW 2006, Australia
5.Univ New South Wales, Sch Elect Engn & Telecommun, Sydney, NSW 2052, Australia

Liu, Chang,Li, Shuangyang,Yuan, Weijie,et al. Predictive Precoder Design for OTFS-Enabled URLLC: A Deep Learning Approach[J]. IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS,2023,41(7):2245-2260.

Liu, Chang,Li, Shuangyang,Yuan, Weijie,Liu, Xuemeng,&Ng, Derrick Wing Kwan.(2023).Predictive Precoder Design for OTFS-Enabled URLLC: A Deep Learning Approach.IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS,41(7),2245-2260.

Liu, Chang,et al."Predictive Precoder Design for OTFS-Enabled URLLC: A Deep Learning Approach".IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 41.7(2023):2245-2260.