Precoder and Detector Learning for Vision-based mmWave Received Power Prediction
Guo, Jia; Bennis, Mehdi; Yang, Chenyang (2023-10-31)
IEEE
31.10.2023
J. Guo, M. Bennis and C. Yang, "Precoder and Detector Learning for Vision-based mmWave Received Power Prediction," 2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Toronto, ON, Canada, 2023, pp. 1-6, doi: 10.1109/PIMRC56721.2023.10293875
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© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists,or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202502061474
https://urn.fi/URN:NBN:fi:oulu-202502061474
Tiivistelmä
Abstract
Multi-modal data collected from various sensors is instrumental in enhancing proactive handover management, beam directions and received powers prediction. However, what essential information to extract and how to effectively allocate wireless resources to transmit the information to a central processor (e.g., a base station (BS)) for decision making is a challenging task. In this work, we consider an uplink multi-user goal-oriented system, where images extracted from users’ depth cameras reflect blockage status between users and their serving BS, which are then used for future received power prediction. In the system, we employ a convolutional neural network to learn a joint semantic source and channel encoder such that essential information is extracted from images. Subsequently, we model the multi-user subcarrier communication system as a hypergraph and use hyper-edge graph neural networks to learn precoders at the user side and detector at the BS side. Simulation results demonstrate that by jointly training a deep neural network-based encoder, decoder, precoder and detector, the communication system can achieve lower prediction errors than traditional precoder and detector, especially in low signal-to-noise ratio scenarios. We also show a trade-off between prediction performance and the computational complexity.
Multi-modal data collected from various sensors is instrumental in enhancing proactive handover management, beam directions and received powers prediction. However, what essential information to extract and how to effectively allocate wireless resources to transmit the information to a central processor (e.g., a base station (BS)) for decision making is a challenging task. In this work, we consider an uplink multi-user goal-oriented system, where images extracted from users’ depth cameras reflect blockage status between users and their serving BS, which are then used for future received power prediction. In the system, we employ a convolutional neural network to learn a joint semantic source and channel encoder such that essential information is extracted from images. Subsequently, we model the multi-user subcarrier communication system as a hypergraph and use hyper-edge graph neural networks to learn precoders at the user side and detector at the BS side. Simulation results demonstrate that by jointly training a deep neural network-based encoder, decoder, precoder and detector, the communication system can achieve lower prediction errors than traditional precoder and detector, especially in low signal-to-noise ratio scenarios. We also show a trade-off between prediction performance and the computational complexity.
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