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To guarantee excellent reliability performance in ultra-reliable low-latencycommunications (URLLC), pragmatic precoder design is an effective approach.However, an efficient precoder design highly depends on the accurateinstantaneous channel state information at the transmitter (ICSIT), whichhowever, is not always available in practice. To overcome this problem, in thispaper, we focus on the orthogonal time frequency space (OTFS)-based URLLCsystem and adopt a deep learning (DL) approach to directly predict the precoderfor the next time frame to minimize the frame error rate (FER) via implicitlyexploiting the features from estimated historical channels in the delay-Dopplerdomain. By doing this, we can guarantee the system reliability even without theknowledge of ICSIT. To this end, a general precoder design problem isformulated where a closed-form theoretical FER expression is specificallyderived to characterize the system reliability. Then, a delay-Doppler domainchannels-aware convolutional long short-term memory (CLSTM) network (DDCL-Net)is proposed for predictive precoder design. In particular, both theconvolutional neural network and LSTM modules are adopted in the proposedneural network to exploit the spatial-temporal features of wireless channelsfor improving the learning performance. Finally, simulation resultsdemonstrated that the FER performance of the proposed method approaches that ofthe perfect ICSI-aided scheme.

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