Edge-device co-inference refers to deploying well-trained artificial intelligent (AI) models at the network edge under the cooperation of devices and edge servers for providing ambient intelligent services. For enhancing the utilization of limited network resources in edge-device co-inference tasks from a systematic view, we propose a task-oriented scheme of integrated sensing, computation and communication (ISCC) in this work. In this system, all devices sense a target from the same wide view to obtain homogeneous noise-corrupted sensory data, from which the local feature vectors are extracted. All local feature vectors are aggregated at the server using over-the-air computation (AirComp) in a broadband channel with the orthogonal-frequency-division-multiplexing technique for suppressing the sensing and channel noise. The aggregated denoised global feature vector is further input to a server-side AI model for completing the downstream inference task. A novel task-oriented design criterion, called maximum minimum pair-wise discriminant gain, is adopted for classification tasks. It extends the distance of the closest class pair in the feature space, leading to a balanced and enhanced inference accuracy. Under this criterion, a problem of joint sensing power assignment, transmit precoding and receive beamforming is formulated. The challenge lies in three aspects: the coupling between sensing and AirComp, the joint optimization of all feature dimensions' AirComp aggregation over a broadband channel, and the complicated form of the maximum minimum pair-wise discriminant gain. To solve this problem, a task-oriented ISCC scheme with AirComp is proposed. Experiments based on a human motion recognition task are conducted to verify the advantages of the proposed scheme over the existing scheme and a baseline.

翻译：边缘-设备协同推理是指在网络边缘部署训练有素的人工智能模型，通过设备与边缘服务器的协作提供环境智能服务。为从系统视角提升边缘-设备协同推理任务中有限网络资源的利用率，本文提出一种面向任务的一体化感知、计算与通信方案。在该系统中，所有设备从同一广视角感知目标，获取含同质噪声干扰的感知数据，并从中提取局部特征向量。所有局部特征向量通过宽带信道中的空中计算技术进行聚合，并采用正交频分复用技术抑制感知噪声与信道噪声。聚合后的去噪全局特征向量进一步输入至服务器端人工智能模型，以完成下游推理任务。针对分类任务，本文提出一种新颖的面向任务设计准则——最大最小成对判别增益。该准则可扩展特征空间中最近类别对的距离，从而实现均衡且增强的推理精度。在此准则下，构建了联合感知功率分配、发射预编码与接收波束成形的优化问题。其挑战体现在三方面：感知与空中计算之间的耦合性、宽带信道中所有特征维度空中计算的联合优化，以及最大最小成对判别增益形式的复杂性。为解决该问题，本文提出一种基于空中计算的面向任务一体化感知-通信-计算方案。基于人体动作识别任务的实验验证了所提方案相较于现有方案与基准方案的优越性。