On the View-and-Channel Aggregation Gain in Integrated Sensing and Edge AI

Abstract

Sensing and edge artificial intelligence (AI) are two key features of the sixth-generation (6G) mobile networks. Their natural integration, termed Integrated sensing and edge AI (ISEA), is envisioned to automate wide-ranging Internet-of-Tings (IoT) applications. To achieve a high sensing accuracy, multi-view features are uploaded to an edge server for aggregation and inference using an AI model. The view aggregation is realized efficiently using over-the-air computing (AirComp), which also aggregates channels to suppress channel noise. At its nascent stage, ISEA still lacks a characterization of the fundamental performance gains from view-and-channel aggregation, which motivates this work. Our framework leverages a well-established distribution model of multi-view sensing data where the classic Gaussian-mixture model is modified by adding sub-spaces matrices to represent individual sensor observation perspectives. Based on the model, we study the End-to-End sensing (inference) uncertainty, a popular measure of inference accuracy, of the said ISEA system by a novel approach involving designing a scaling-tight uncertainty surrogate function, global discriminant gain, distribution of receive Signal-to-Noise Ratio (SNR), and channel induced discriminant loss. We prove that the E2E sensing uncertainty diminishes at an exponential rate as the number of views/sensors grows, where the rate is proportional to global discriminant gain. Given channel distortion, we further show that the exponential scaling remains with a reduced decay rate related to the channel induced discriminant loss. Furthermore, we benchmark AirComp against equally fast, traditional analog orthogonal access, which reveals a sensing-accuracy crossing point between the schemes, leading to the proposal of adaptive access-mode switching. Last, the insights from our framework are validated by experiments using real-world dataset.