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Title	Semantic Communication-Empowered Vehicle Count Prediction For Traffic Management
ID_Doc	48221
Authors	Kadam S.; Kim D.I.
Year	2024
Published	IEEE Wireless Communications and Networking Conference, WCNC
DOI	http://dx.doi.org/10.1109/WCNC57260.2024.10571211
Abstract	Vehicle count prediction is an important aspect of smart city traffic management. Most major roads are monitored by cameras with computing and transmitting capabilities. These cameras provide data to the central traffic controller (CTC), which is in charge of traffic control management. In this paper, we propose a joint CNN-LSTM-based semantic communication (SemCom) model in which the semantic encoder of a camera extracts the relevant semantics from raw images. The encoded semantics are then sent to the CTC by the transmitter in the form of symbols. The semantic decoder of the CTC predicts the vehicle count on each road based on the sequence of received symbols and develops a traffic management strategy accordingly. Using numerical results, we show that the proposed SemCom model reduces overhead by 54.42% when compared to source encoder/decoder methods. Also, we demonstrate through simulations that the proposed model outperforms state-of-the-art models in terms of mean absolute error (MAE) and mean-squared error (MSE). © 2024 IEEE.
Author Keywords	6G; Deep Learning; Semantic Communications; Traffic Control; Wireless Communications

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