Learning-Based Predictive Beamforming for Integrated Sensing and Communication in Vehicular Networks
نویسندگان
چکیده
This paper investigates the integrated sensing and communication (ISAC) in vehicle-to-infrastructure (V2I) networks. To realize ISAC, an effective beamforming design is essential which however, highly depends on availability of accurate channel tracking requiring large training overhead computational complexity. Motivated by this, we adopt a deep learning (DL) approach to implicitly learn features historical channels directly predict matrix be adopted for next time slot maximize average achievable sum-rate ISAC system. The proposed method can bypass need explicit process reduce signaling significantly. this end, general maximization problem with Cramer-Rao lower bounds-based constraints first formulated considered system taking into account multiple access interference. Then, exploiting penalty method, versatile unsupervised DL-based predictive framework developed address problem. As realization framework, channels-based convolutional long short-term memory (LSTM) network (HCL-Net) devised ISAC-based V2I network. Specifically, convolution LSTM modules are successively HCL-Net exploit spatial temporal dependencies further improve performance. Finally, simulation results show that not only guarantees required performance, but also achieves satisfactory upper bound obtained genie-aided scheme perfect instantaneous state information available.
منابع مشابه
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ژورنال
عنوان ژورنال: IEEE Journal on Selected Areas in Communications
سال: 2022
ISSN: ['0733-8716', '1558-0008']
DOI: https://doi.org/10.1109/jsac.2022.3180803