# Semantic Communication for Cooperative Multi-Tasking over Rate-Limited Wireless Channels with Implicit Optimal Prior

arXiv: https://arxiv.org/abs/2506.08944 · HTML: https://mlsanity.com/paper/2506.08944/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2025.

## Built on by (most cited first, 3 of 3)

- [Semantic-Aware Task Clustering for Federated Cooperative Multi-Task Semantic Communication](https://mlsanity.com/paper/2601.17419/) (2026)
- [Semantic-Aware Task Clustering for Constructive and Cooperative Multi-Tasking](https://mlsanity.com/paper/2607.21426/) (2026)
- [Cooperative Multi-Task Semantic Communication for Joint Classification and Regression Tasks](https://mlsanity.com/paper/2609.03977/) (2026)

## Abstract (arXiv)

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.