用于分布式感知的空中多视图合并
Over-the-Air Multi-View Pooling for Distributed Sensing
摘要：
感测被设想为6 G移动网络的关键网络功能。人工智能（AI）授权的传感融合了来自边缘网络中分布的设备的多个传感视图的特征，以便边缘服务器执行准确的推理。此过程称为多视图池，由于许多设备的多访问而造成通信瓶颈。为了缓解这个问题，我们提出了一个面向任务的分布式传感的同时访问计划称为空中池（AirPooling）。现有的空中计算（Over-the-Air Computing，AirComp）技术可以直接应用于通过利用多接入信道的波形叠加属性来实现平均空中池化。然而，尽管在实践中最受欢迎，但称为Max-AirPooling的空中最大化并不是AirComp可实现的，因为AirComp解决了有限的功能子集。我们解决的挑战，提出了新的广义AirPooling框架，可以配置为支持最大和平均AirPooling通过控制配置参数。前者是通过在AirComp中添加所设计的设备端预处理和服务器端后处理来实现的。为了表征端到端的感知性能，应用分类裕度理论将分类精度和AirPooling误差联系起来。此外，分析揭示了一个固有的权衡最大空气池之间的池函数近似的精度和噪声抑制的有效性。利用这种折衷，我们优化了Max-AirPooling的结构参数，得到了一种次优的自适应参数控制的封闭形式。在真实世界数据集上获得的实验结果表明，AirPooling提供的传感精度接近传统数字空中接口所能达到的精度，但大大降低了通信延迟。
英文摘要：
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.
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