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

2025-06-10

AI Summary

To address the practicality bottleneck of multi-task semantic communication over rate-constrained wireless channels, this paper proposes a collaborative multi-task semantic communication architecture. It decouples the semantic encoder into shared common and task-specific submodules, and— for the first time—incorporates implicit maximum prior and density ratio estimation into this framework. A hybrid training paradigm is further designed, integrating deep neural networks with kernelized parametric learning to jointly optimize information-theoretic objectives (KL-divergence constraints and variational approximations) and semantic fidelity. Simulation results demonstrate that the proposed method significantly improves multi-task semantic transmission accuracy and channel robustness under low-bitrate conditions. The architecture is deployable and scalable, offering a novel semantic communication solution for 6G intelligent wireless networks.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Search and Optimization: Learning to Search
• Computer Vision: Multi-modal Vision

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Enhance cooperative multi-task semantic communication over rate-limited channels
• Address rate-limit constraints using KL divergence and implicit optimal prior
• Combine neural networks and kernelized ML for robust multi-task SemCom

Innovation

• Hybrid learning combines neural and kernel methods
• Implicit optimal prior tackles rate-limit constraint
• Variational approximations bridge theory and practice