AirBreath Sensing: Protecting Over-the-Air Distributed Sensing Against Interference

TL;DR

AirBreath sensing introduces a spectrum-efficient method combining feature compression and spread spectrum to protect over-the-air distributed sensing systems from interference, optimizing the tradeoff between accuracy and interference suppression.

Contribution

This paper proposes AirBreath sensing, a novel interference mitigation framework for over-the-air computing in 6G sensing systems, with a new control algorithm for optimal tradeoff management.

Findings

Effective interference mitigation demonstrated on real datasets.

The control algorithm achieves near-optimal performance.

Tradeoff between sensing accuracy and interference suppression is characterized.

Abstract

A distinctive function of sixth-generation (6G) networks is the integration of distributed sensing and edge artificial intelligence (AI) to enable intelligent perception of the physical world. This resultant platform, termed integrated sensing and edge AI (ISEA), is envisioned to enable a broad spectrum of Internet-of-Things (IoT) applications, including remote surgery, autonomous driving, and holographic telepresence. Recently, the communication bottleneck confronting the implementation of an ISEA system is overcome by the development of over-the-air computing (AirComp) techniques, which facilitate simultaneous access through over-the-air data feature fusion. Despite its advantages, AirComp with uncoded transmission remains vulnerable to interference. To tackle this challenge, we propose AirBreath sensing, a spectrum-efficient framework that cascades feature compression and spread spectrum to mitigate interference without bandwidth expansion. This work reveals a fundamental tradeoff between these two operations under a fixed bandwidth constraint: increasing the compression ratio may reduce sensing accuracy but allows for more aggressive interference suppression via spread spectrum, and vice versa. This tradeoff is regulated by a key variable called breathing depth, defined as the feature subspace dimension that matches the processing gain in spread spectrum. To optimally control the breathing depth, we mathematically characterize and optimize this aforementioned tradeoff by designing a tractable surrogate for sensing accuracy, measured by classification discriminant gain (DG). Experimental results on real datasets demonstrate that AirBreath sensing effectively mitigates interference in ISEA systems, and the proposed control algorithm achieves near-optimal performance as benchmarked with a brute-force search.