AI 中文总结
研究协作式多任务语义通信中任务的建设性合作问题，提出语义感知任务聚类方法，通过顺序多阶段优化，将问题分解为语义聚类和簇内端到端学习两阶段，有效减轻破坏性合作和负迁移，提升准确率。
AI 中文摘要
协作式多任务语义通信（CMT-SemCom）通过利用共享表示来提高任务执行性能。但如[1]中所示，协作式多任务可能具有建设性或破坏性，这取决于任务间的语义关系。为确保建设性合作，我们提出一种用于CMT-SemCom的语义感知任务聚类方法。我们制定了一个顺序多阶段优化问题，在短暂的初始训练阶段后，对语义对齐的任务进行聚类，然后仅在发现的组内进行端到端联合训练。具体来说，该问题分解为两个阶段：（i）利用基于层次密度的空间聚类的语义聚类问题，（ii）簇内端到端CMT-SemCom学习问题。仿真结果表明，该框架有效减轻了破坏性合作和负迁移，与未聚类的多任务和单独训练基线相比，提高了准确率。
英文摘要
Cooperative multi-task semantic communication (CMT-SemCom) improves task execution performance by leveraging shared representations. However, as we demonstrated in [1], cooperative multi-tasking can be either constructive or destructive, depending on the semantic relationships among tasks. To ensure constructive cooperation, we propose a semantic-aware task clustering method for CMT-SemCom. We have formulated a sequential multi-stage optimization problem in which semantically aligned tasks are clustered once after a short initial training phase, and then end-to-end (E2E) joint training is conducted exclusively within the discovered groups. Specifically, the problem decomposes into two stages: (i) a semantic clustering problem leveraging hierarchical density-based spatial clustering, and (ii) an intra-cluster E2E CMT-SemCom learning problem. Simulation results demonstrate that the proposed framework effectively mitigates destructive cooperation and negative transfer, yielding accuracy gains compared to unclustered multi-tasking and individual training baselines.
CommentsThis work has been submitted to the IEEE for possible publication
