영문내용
(English Abstract) |
Semantic communication will significantly increase data transmission effectiveness by only transferring the semantic features. Unfortunately, most of the study in this area primarily concentrates on a single type of application, such as image, text, or audio. However, some of the existing studies also focus on goal-oriented communication. In this study, a goal-oriented semantic communication framework for the vehicular ad-hoc network has been proposed. A deep autoencoder (DAE) has been used to capture the semantic information from traffic signs. The encoder of the DAE extracts the semantic information from a traffic sign, and then this semantic information is transmitted to the CAVs. After receiving the semantic information, the CAVs use the decoder of the DAE to reconstruct the traffic sign. Then the CAVs use a Deep Q-Network (DQN) to take the appropriate action based on the reconstructed traffic sign. The experimental result indicates that our proposed model can minimize up to 90.81% of the communication cost. |
