Problem
Research questions and friction points this paper is trying to address.
Cooperative Multi-Task Semantic Communication
Classification and Regression Tasks
Cityscapes dataset
Cooperative Multi-Task Semantic Communication for Joint Classification and Regression Tasks
🤖 AI Summary
本文扩展了CMT-SemCom框架以处理复杂数据集上的分类和回归任务，采用信息最大化原则，并探讨了公共单元容量对性能的影响。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Cooperative Multi-Task Semantic Communication
InfoMax Principle
Heterogeneous Tasks
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