Task-Oriented Communication with Reliability-Driven Retransmission Request
Ziheng Ding, Qianqian Yang, Zhaoyang Zhang · 2023
The advanced Deep Learning (DL) techniques have enabled the development of semantic communication systems by their remarkable information processing and end - to-end optimization capabilities. However, the lack of performance guarantee of these DL-based methods also brings concerns on the reliability of semantic communication systems. To address this issue, we propose a semantic communication scheme with reliability-driven retransmission requests in order to improve the transmission efficiency and guarantee the reliability of the inference result at the same time. In particular, the transmitter first sends a basic amount of the information, and the receiver infers with this information, and assesses the reliability of the results. If the derived reliability is below a given threshold, a retransmission request is sent to the sender for more information to be transmitted. We exploit the entropy of the output logits to quantify the reliability of the classification results. More specifically, lower entropy corresponds to higher confidence, indicating higher reliability. Experimental results validate the effectiveness of the proposed scheme in terms of improving transmission efficiency and computational efficiency while maintaining the reliability of the inference results.