Semantic Communication-Empowered Traffic Management using Vehicle Count Prediction
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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. An optimization problem to improve the quality of experience (QoE) is introduced and numerically solved, taking into account constraints such as vehicle user safety, transmit power of camera devices, vehicle count prediction accuracy, and semantic entropy. 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 QoE.
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