  In this paper, a semantic communication framework for image transmission is
developed. In the investigated framework, a set of servers cooperatively
transmit images to a set of users utilizing semantic communication techniques.
To evaluate the performance of studied semantic communication system, a
multimodal metric is proposed to measure the correlation between the extracted
semantic information and the original image. To meet the ISS requirement of
each user, each server must jointly determine the semantic information to be
transmitted and the resource blocks (RBs) used for semantic information
transmission. We formulate this problem as an optimization problem aiming to
minimize each server's transmission latency while reaching the ISS requirement.
To solve this problem, a value decomposition based entropy-maximized
multi-agent reinforcement learning (RL) is proposed, which enables servers to
coordinate for training and execute RB allocation in a distributed manner to
approach to a globally optimal performance with less training iterations.
Compared to traditional multi-agent RL, the proposed RL improves the valuable
action exploration of servers and the probability of finding a globally optimal
RB allocation policy based on local observation. Simulation results show that
the proposed algorithm can reduce the transmission delay by up to 16.1%
compared to traditional multi-agent RL.
