Aligning Task- and Reconstruction-Oriented Communications for Edge Intelligence

2025-02-21

AI Summary

To address the challenge in edge intelligence where reconstruction-oriented communication struggles to simultaneously satisfy AI task latency requirements and semantic fidelity, this paper proposes a novel joint source-channel coding (JSCC) framework unifying reconstruction and task-oriented objectives. Methodologically, it extends the information bottleneck principle to jointly optimize task loss and structural fidelity—thereby aligning these two paradigms for the first time. It further introduces a variational inference-based information reshaper and a classical QAM/PSK-compatible JSCC scheme that requires no modification to downstream neural networks. Evaluated on the CARLA autonomous driving simulator, the framework reduces bit consumption by 99.19% over JPEG, JPEG2000, and BPG, while preserving semantic segmentation accuracy. This yields substantial gains in communication efficiency and seamless integration with existing deep learning pipelines.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Existing communication systems aim to reconstruct the information at the receiver side, and are known as reconstruction-oriented communications. This approach often falls short in meeting the real-time, task-specific demands of modern AI-driven applications such as autonomous driving and semantic segmentation. As a new design principle, task-oriented communications have been developed. However, it typically requires joint optimization of encoder, decoder, and modified inference neural networks, resulting in extensive cross-system redesigns and compatibility issues. This paper proposes a novel communication framework that aligns reconstruction-oriented and task-oriented communications for edge intelligence. The idea is to extend the Information Bottleneck (IB) theory to optimize data transmission by minimizing task-relevant loss function, while maintaining the structure of the original data by an information reshaper. Such an approach integrates task-oriented communications with reconstruction-oriented communications, where a variational approach is designed to handle the intractability of mutual information in high-dimensional neural network features. We also introduce a joint source-channel coding (JSCC) modulation scheme compatible with classical modulation techniques, enabling the deployment of AI technologies within existing digital infrastructures. The proposed framework is particularly effective in edge-based autonomous driving scenarios. Our evaluation in the Car Learning to Act (CARLA) simulator demonstrates that the proposed framework significantly reduces bits per service by 99.19% compared to existing methods, such as JPEG, JPEG2000, and BPG, without compromising the effectiveness of task execution.

Problem

• Aligning task- and reconstruction-oriented communications
• Optimizing data transmission for edge intelligence
• Reducing bits per service for AI-driven applications

Innovation

• Integrated task and reconstruction communications
• Extended Information Bottleneck theory
• Joint source-channel coding modulation