Sensing is envisioned as a key network function of the 6G mobile networks. Artificial intelligence (AI)-empowered sensing fuses features of multiple sensing views from devices distributed in edge networks for the edge server to perform accurate inference. This process, known as multi-view pooling, creates a communication bottleneck due to multi-access by many devices. To alleviate this issue, we propose a task-oriented simultaneous access scheme for distributed sensing called Over-the-Air Pooling (AirPooling). The existing Over-the-Air Computing (AirComp) technique can be directly applied to enable Average-AirPooling by exploiting the waveform superposition property of a multi-access channel. However, despite being most popular in practice, the over-the-air maximization, called Max-AirPooling, is not AirComp realizable as AirComp addresses a limited subset of functions. We tackle the challenge by proposing the novel generalized AirPooling framework that can be configured to support both Max- and Average-AirPooling by controlling a configuration parameter. The former is realized by adding to AirComp the designed pre-processing at devices and post-processing at the server. To characterize the end-to-end sensing performance, the theory of classification margin is applied to relate the classification accuracy and the AirPooling error. Furthermore, the analysis reveals an inherent tradeoff of Max-AirPooling between the accuracy of the pooling-function approximation and the effectiveness of noise suppression. Using the tradeoff, we optimize the configuration parameter of Max-AirPooling, yielding a sub-optimal closed-form method of adaptive parametric control. Experimental results obtained on real-world datasets show that AirPooling provides sensing accuracies close to those achievable by the traditional digital air interface but dramatically reduces the communication latency.

翻译：感知被视为6G移动网络的关键网络功能。基于人工智能的感知通过融合分布在边缘网络中的多个设备的多视图特征，使边缘服务器能够进行精确推理。该过程称为多视图池化，由于众多设备的多址接入，造成了通信瓶颈。为缓解这一问题，我们提出了一种面向任务的分布式感知同时接入方案，称为空中池化（AirPooling）。利用多址信道的波形叠加特性，可直接应用现有的空中计算（AirComp）技术实现平均空中池化（Average-AirPooling）。然而，尽管在实践中最为常见，空中最大化池化（Max-AirPooling）却无法通过AirComp实现，因为AirComp仅支持有限的功能子集。我们通过提出新型广义AirPooling框架来应对这一挑战，该框架可通过控制配置参数同时支持Max-和Average-AirPooling。前者通过在AirComp基础上增加设备端设计的预处理和服务器端的后处理来实现。为刻画端到端感知性能，我们应用分类边界理论将分类精度与AirPooling误差关联起来。进一步分析揭示了Max-AirPooling在池化函数逼近精度与噪声抑制效果之间存在固有折衷。基于该折衷，我们优化了Max-AirPooling的配置参数，提出了一种次优闭式自适应参数控制方法。在真实数据集上的实验结果表明，AirPooling能够提供接近传统数字空中接口的感知精度，同时显著降低通信延迟。