Performance Optimization for Multi-Cell Multi-Hop Semantic 6G Cloudified Networks Considering Compression, Transmission and Computation

Deep learning (DL)-enabled semantic communication has been widely regarded as a promising 6G technology due to its efficient wireless transmission capabilities, providing a novel technological foundation for bandwidth-constrained Internet of Things (IoT) scenarios. Combining semantic communication with classical multicell network architectures, which are commonly employed in long-distance IoT communications, is essential for enabling its large-scale deployment in future 6G systems. However, the performance optimization of semantic transmission in this scene still faces significant challenges due to the complex coupling among compression, transmission, and computation under resource-limited conditions, which is difficult to solve by traditional methods due to the closed box nature of the artificial intelligence models used. To address this problem, in this article, we formulate an optimization problem to minimize the weighted sum of end-to-end latency and information reconstruction performance by adjusting the compression control strategy, radio block assignment, and computing resource allocation decision. The case of image transmission is investigated. To address this nonconvex mixed-integer nonlinear programming problem, we adopt a lower bound approximation method to decompose the formulated problem and design an optimization method combining the Lagrange multiplier method and multiagent deep reinforcement learning algorithm. We also introduce an image reconstruction performance prediction model to empower the agent with semantic-aware optimization ability. The results demonstrate that the proposed method can achieve superior performance compared to other approaches.