O2SC: Realizing Channel-Adaptive Semantic Communication With One-Shot Online-Learning

Abstract

Motivated by progress in data-driven supervised learning, semantic communication has witnessed remarkable advancements in improving the efficiency of data transmission under various channel conditions. These advancements typically require a substantial amount of training data for offline training, which is challenging in practical systems. Therefore, in this work, we propose O2SC, a one-shot online-learning framework for semantic communication to achieve adaptive transmission under different channel conditions. Since semantic communication relies on acquired channel state information (CSI), we jointly design the channel estimation and semantic communication processes. Specifically, we introduce a denoising module based on one-shot self-supervised learning, allowing semantic communication systems to adapt to new channel conditions without the need to collect extensive training data. The denoising module is utilized to eliminate noise in the received data samples, using only the data samples themselves. Following this, we further exploit meta-learning to allow the system to quickly adapt to diverse channel conditions, by finding an appropriate initialization for each data sample in a timely way. Simulation results demonstrate that the proposed method achieves performance close to that of supervised learning-based approaches while also providing improved generalizability across different channel conditions.