Over-the-Air View-Pooling for Low-Latency Distributed Sensing

Artificial intelligence (AI)-empowered sensing, a key function of 6G networks, fuses features of multiple sensing views from distributed devices 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). Exploiting the waveform superposition property of a multi-access channel, the existing Over-the-Air Computing (AirComp) technique supports over-the-air feature averaging, but over-the-air maximization, called Max-AirPooling, has no direct AirComp realization. The proposed generalized AirPooling 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 (E2E) sensing performance in object recognition, the theory of classification margin is applied to bound the classification accuracy in terms of the AirPooling error, allowing the latter to be a tractable surrogate of the former. Experimental results show that AirPooling provides sensing accuracies close to those achievable by the digital air interface but dramatically reduces the communication latency by up to an order of magnitude.