Abstract
In this paper, a semantic communication framework where the transmitter and the receiver possess different knowledge (i.e., different methods to extract semantic information and regenerate source data) is investigated. In the proposed framework, the transmitter extracts semantic information according to its knowledge, and the receiver processes the received semantic information based on its own knowledge. To ensure the receiver can understand the semantic information as anticipated, the transmitter will ask a series of questions to estimate the receiver's knowledge and adjust the method of semantic information extraction according to the estimation. Due to the limited wireless resources and communication time, the size of the extracted semantic information and the number of questions that the transmitter can ask are limited. This problem is formulated as an optimization problem whose goal is to maximize worse case answer similarities over semantic generation and question selection decisions. More specifically, the transmitter aims to ask the questions to pinpoint the largest knowledge divergence and adjusts its semantic generation method to minimize this divergence. An adversarial reinforcement learning (ARL) inspired algorithm, combined with a matching network, is designed to achieve such opposite goals by searching the optimal semantic information generation scheme and question selection scheme in an adversarial manner. Simulation results demonstrate that the proposed framework can improve the semantic similarity of answers by up to 5.5% gain and can achieve up to 10.7% gain in terms of the average similarity of texts compared to the algorithm without knowledge estimation.