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

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 howe...

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Bibliographic Details
Published in:IEEE International Conference on Communications (2003) pp. 5651 - 5657
Main Authors: Liu, Chang, Li, Shuangyang, Yuan, Weijie, Liu, Xuemeng, Ng, Derrick Wing Kwan
Format: Conference Proceeding
Language:English
Published: IEEE 28.05.2023
Subjects:
Receivers
Reliability engineering
Simulation
Time-frequency analysis
Transmitters
Ultra reliable low latency communication
Wireless communication
ISSN:1938-1883
Online Access:Get full text
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Summary: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.
ISSN:1938-1883
DOI:10.1109/ICC45041.2023.10278742