  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.
