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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| Vydáno v: | IEEE International Conference on Communications (2003) s. 5651 - 5657 |
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IEEE
28.05.2023
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| ISSN: | 1938-1883 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Ng, Derrick Wing Kwan Li, Shuangyang Liu, Xuemeng Yuan, Weijie Liu, Chang |
| Author_xml | – sequence: 1 givenname: Chang surname: Liu fullname: Liu, Chang organization: The Hong Kong Polytechnic University,Department of Electronic and Information Engineering – sequence: 2 givenname: Shuangyang surname: Li fullname: Li, Shuangyang organization: School of Engineering, University of Western Australia,Perth,Australia – sequence: 3 givenname: Weijie surname: Yuan fullname: Yuan, Weijie organization: Southern University of Science and Technology,Department of Electronic and Electrical Engineering,China – sequence: 4 givenname: Xuemeng surname: Liu fullname: Liu, Xuemeng organization: School of Electrical and Information Engineering, University of Sydney,Sydney,Australia – sequence: 5 givenname: Derrick Wing Kwan surname: Ng fullname: Ng, Derrick Wing Kwan organization: School of Electrical Engineering and Telecommunications, University of New South Wales,Sydney,Australia |
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| Snippet | To guarantee excellent reliability performance in ultra-reliable low-latency communications (URLLC), pragmatic precoder design is an effective approach.... |
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| SubjectTerms | Receivers Reliability engineering Simulation Time-frequency analysis Transmitters Ultra reliable low latency communication Wireless communication |
| Title | Deep Learning-Empowered Predictive Precoder Design for OTFS Transmission in URLLC |
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