From Conventional to Semantic Communications based on Deep Learning

To transmit text messages, speeches, or pictures, we usually convert them into a symbol sequence and transmit the symbols in a conventional communication system, which is designed based on the block structure with coding, decoding, modulation, demodulation, etc. It has been demonstrated recently that deep learning (DL) has great potential to break the bottleneck of the block-based communication system. In this talk, we first provide our recent endeavors in developing end-to-end (E2E) communications, which combine all blocks at the transmitter by a neural network and those at the receiver by another neural network. Even if deep learning-based E2E communication systems have the potential to outperform conventional block-based communication systems in terms of performance and complexity, their spectrum efficiency is still limited by Shannon capacity since they essentially transmit bits or symbols. Semantic communication systems transmit and recover the desired meaning of the transmitted content (for example, a text message or a picture) directly and can significantly improve transmission efficiency. We will present our initial results on semantic communications.