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.

翻译：感知与边缘人工智能（AI）是第六代（6G）移动网络的两大关键特性。两者的自然融合——即集成感知与边缘AI（ISEA）——有望实现广泛的物联网（IoT）应用自动化。为达到高感知精度，多视图特征被上传至边缘服务器，通过AI模型进行聚合与推理。视图聚合通过空中计算（AirComp）高效实现，该过程同时聚合信道以抑制信道噪声。在ISEA的初期发展阶段，视图-信道聚合带来的基本性能增益仍缺乏系统性刻画，这正是本工作的动机。我们的框架采用一个成熟的多视图感知数据分布模型，该模型在经典高斯混合模型基础上，通过添加子空间矩阵来表征单个传感器的观测视角。基于该模型，我们采用一种新颖方法研究所述ISEA系统的端到端感知（推理）不确定性（一种常用的推理精度度量），该方法包括设计尺度紧致的不确定性代理函数、全局判别增益、接收信噪比（SNR）分布以及信道诱发的判别损失。我们证明，E2E感知不确定性随视图/传感器数量的增加呈指数级下降，其速率与全局判别增益成正比。考虑信道失真时，我们进一步证明指数缩放特性依然存在，但衰减速率因信道诱发的判别损失而降低。此外，我们将AirComp与同样快速的经典模拟正交接入进行对比，发现两种方案之间存在感知精度交叉点，由此提出自适应接入模式切换方案。最后，我们利用真实数据集实验验证了框架的洞见。