发表机构
University of Bremen（不来梅大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文将协作多任务语义通信（CMT-SemCom）框架扩展至Cityscapes数据集，采用InfoMax原则处理异构分类与回归任务，经评估其性能显著优于多种基准方法。
AI 中文摘要
多任务语义通信（SemCom）在未来智能网络中优先于比特精确重建同时执行多个任务。在我们之前的工作[1]中，我们提出了协作多任务语义通信（CMT-SemCom）框架，其中语义编码器被划分为通用单元（CU）和多个特定单元（SUs）以促进协作多任务处理。然而，CMT-SemCom仅在简单数据集上的同构分类任务中进行了评估，限制了其在现实感知系统中的适用性。在本文中，我们将CMT-SemCom扩展为在复杂的Cityscapes数据集上联合处理异构分类和回归任务。我们采用信息最大化（InfoMax）原则，使其能够适应混合离散和连续语义变量。我们将所提出的框架与独立单任务训练、传统的与任务无关的数字传输以及单编码器多解码器SemCom进行基准测试。此外，我们研究了CU容量对联合任务性能的影响，提供了设计见解。广泛的评估表明，CMT-SemCom显著优于基准方法。
英文摘要
Multi-Task semantic communication (SemCom) prioritizes simultaneous execution of multiple tasks over bit-accurate reconstruction in future intelligent networks. In our prior work [1], we introduced the cooperative multi-task SemCom (CMT-SemCom) framework, in which the semantic encoder is divided into a common unit (CU) and multiple specific units (SUs) to facilitate cooperative multi-task processing. However, CMT-SemCom has been evaluated on homogeneous classification tasks on simplistic datasets, limiting its applicability to real-world perception systems. In this paper, we extend our CMT-SemCom to jointly handle heterogeneous classification and regression tasks on the complex Cityscapes dataset. We adopt the information maximization (InfoMax) principle so that it accommodates mixed discrete and continuous semantic variables. In particular, we benchmark the proposed framework against independent single-task training, a conventional task-agnostic digital transmission, and single-encoder multi-decoder SemCom. Additionally, we investigate the impact of CU capacity on joint task performance, providing design insights. Extensive evaluations demonstrate that CMT-SemCom significantly outperforms the benchmarks.
CommentsThis work has been submitted to the IEEE for possible publication
