In this work, we expand the cooperative multi-task semantic communication framework (CMT-SemCom) introduced in [1], which divides the semantic encoder on the transmitter side into a common unit (CU) and multiple specific units (SUs), to a more applicable design. Our proposed system model addresses real-world constraints by introducing a general design that operates over rate-limited wireless channels. Further, we aim to tackle the rate-limit constraint, represented through the Kullback-Leibler (KL) divergence, by employing the density ratio trick alongside the implicit optimal prior method (IoPm). By applying the IoPm to our multi-task processing framework, we propose a hybrid learning approach that combines deep neural networks with kernelized-parametric machine learning methods, enabling a robust solution for the CMT-SemCom. Our framework is grounded in information-theoretic principles and employs variational approximations to bridge theoretical foundations with practical implementations. Simulation results demonstrate the proposed system's effectiveness in rate-constrained multi-task SemCom scenarios, highlighting its potential for enabling intelligence in next-generation wireless networks.

翻译：本文扩展了文献[1]提出的协同多任务语义通信框架（CMT-SemCom），该框架将发射端的语义编码器划分为公共单元（CU）与多个专用单元（SU）。我们提出了一种更具适用性的系统模型，通过引入可在速率受限无线信道上运行的通用设计来应对实际约束。进一步地，我们旨在解决以Kullback-Leibler（KL）散度表征的速率限制约束，采用密度比技巧结合隐式最优先验方法（IoPm）。通过将IoPm应用于我们的多任务处理框架，我们提出了一种结合深度神经网络与核化参数机器学习方法的混合学习方案，为CMT-SemCom提供了鲁棒的解决方案。本框架基于信息论原理，并采用变分近似来连接理论基础与实际实现。仿真结果表明，所提系统在速率受限的多任务语义通信场景中具有显著效能，展现了其在赋能下一代无线网络智能化方面的潜力。