  This work presents a novel semantic transmission framework in wireless
networks, leveraging the joint processing technique. Our framework enables
multiple cooperating base stations to efficiently transmit semantic information
to multiple users simultaneously. To enhance the semantic communication
efficiency of the transmission framework, we formulate an optimization problem
with the objective of maximizing the semantic spectral efficiency of the
framework and propose a lowcomplexity dynamic semantic mapping and resource
allocation algorithm. This algorithm, based on deep reinforcement learning and
alternative optimization, achieves near-optimal performance while reducing
computational complexity. Simulation results validate the effectiveness of the
proposed algorithm, bridging the research gap and facilitating the practical
implementation of semantic communication systems.
