Features-over-the-Air: Contrastive Learning Enabled Cooperative Edge Inference

TL;DR

This paper introduces a novel cooperative edge inference framework using contrastive learning and semantic NOMA, enabling efficient image retrieval over wireless channels with improved accuracy, especially in low SNR and bandwidth-limited scenarios.

Contribution

It proposes a new contrastive learning-based semantic communication paradigm for cooperative edge inference, leveraging over-the-air feature aggregation for enhanced image retrieval.

Findings

Achieves state-of-the-art retrieval accuracy in experiments.

Significantly improves performance in low SNR conditions.

Effective under limited bandwidth constraints.

Abstract

We study the collaborative image retrieval problem at the wireless edge, where multiple edge devices capture images of the same object, which are then used jointly to retrieve similar images at the edge server over a shared multiple access channel. We propose a semantic non-orthogonal multiple access (NOMA) communication paradigm, in which extracted features from each device are mapped directly to channel inputs, which are then added over-the-air. We propose a novel contrastive learning (CL)-based semantic communication (CL-SC) paradigm, aiming to exploit signal correlations to maximize the retrieval accuracy under a total bandwidth constraints. Specifically, we treat noisy correlated signals as different augmentations of a common identity, and propose a cross-view CL algorithm to optimize the correlated signals in a coarse-to-fine fashion to improve retrieval accuracy. Extensive numerical experiments verify that our method achieves the state-of-the-art performance and can significantly improve retrieval accuracy, with particularly significant gains in low signla-to-noise ratio (SNR) and limited bandwidth regimes.