Partial Sampling-Based Semantic Communications

Semantic communications have the potential to improve transmission efficiency and support intelligent tasks. However, the commonly used global sampling-based pattern ignores the fact that the data processing ability of edge transmitters is strictly limited and only a small part of information is available for a single sample in some scenarios. This paper proposes a novel partial sampling-based semantic communication (PSSC) framework where an edge transmitter is guided by feedback from the receiver to locate and collect only part of content relevant to the target task. Taking the vision-based task as an example, the transmitter selectively samples a small patch of a large-size image until the intelligent task is successfully executed at the receiver. The selection of sampling location is modeled as a partially observable Markov decision process problem and an intelligent approach based on reinforcement learning is proposed to solve the problem. In addition, a recurrent neural network-based receiver is designed to fuse information received over multiple transmission rounds. Besides, we prove that the feedback does not increase the semantic channel capacity. Simulation results demonstrate that the proposed framework can locate the informative areas accurately and achieve competitive performance compared to the existing global sampling-based methods.