Aligning Task- and Reconstruction-Oriented Communications for Edge Intelligence

TL;DR

This paper introduces a novel communication framework that combines reconstruction- and task-oriented methods for edge intelligence, optimizing data transmission for AI applications like autonomous driving using an extended Information Bottleneck theory.

Contribution

It extends IB theory to unify reconstruction and task-oriented communications, proposes a variational approach for mutual information, and develops a JSCC scheme compatible with existing digital infrastructure.

Findings

Reduces bits per service by 99.19% in autonomous driving scenarios.

Maintains task performance while significantly decreasing data transmission.

Compatible with classical modulation techniques for practical deployment.

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.