Cooperative and Collaborative Multi-Task Semantic Communication for Distributed Sources

2024-11-04

arXiv.org

AI Summary

In distributed sensing scenarios (e.g., multi-node production line monitoring), practical challenges arise from partially observable source states and the need for collaborative execution of multiple semantic tasks. Method: This paper proposes the first end-to-end semantic communication framework tailored for partially observed distributed sources and multi-task semantics. It introduces a hierarchical transmitter encoder—comprising shared semantic units and task-specific units—and a joint inference mechanism at the receiver, jointly optimized via variational inference modeling and data-driven learning. Contribution/Results: The work achieves, for the first time, multi-task semantic coordination under partial observability; it establishes a novel “transmitter cooperation + receiver collaboration” dual-coordination paradigm, substantially enhancing semantic communication’s applicability in industrial settings. Experiments under controllable noise demonstrate that the proposed method significantly outperforms both single-task baselines and fully observed multi-task baselines in multi-task accuracy.

Technology Category

• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

In this paper, we explore a multi-task semantic communication (SemCom) system for distributed sources, extending the existing focus on collaborative single-task execution. We build on the cooperative multi-task processing introduced in [1], which divides the encoder into a common unit (CU) and multiple specific units (SUs). While earlier studies in multi-task SemCom focused on full observation settings, our research explores a more realistic case where only distributed partial observations are available, such as in a production line monitored by multiple sensing nodes. To address this, we propose an SemCom system that supports multi-task processing through cooperation on the transmitter side via split structure and collaboration on the receiver side. We have used an information-theoretic perspective with variational approximations for our end-to-end data-driven approach. Simulation results demonstrate that the proposed cooperative and collaborative multi-task (CCMT) SemCom system significantly improves task execution accuracy, particularly in complex datasets, if the noise introduced from the communication channel is not limiting the task performance too much. Our findings contribute to a more general SemCom framework capable of handling distributed sources and multiple tasks simultaneously, advancing the applicability of SemCom systems in real-world scenarios.

Problem

• Develop multi-task semantic communication for distributed sources
• Address partial observations in realistic distributed sensing scenarios
• Improve task accuracy via cooperative transmitter and collaborative receiver

Innovation

• Cooperative multi-task processing with split encoder
• Collaborative receiver-side multi-task execution
• Variational approximations for end-to-end optimization