  Edge-device co-inference, which concerns the cooperation between edge devices
and an edge server for completing inference tasks over wireless networks, has
been a promising technique for enabling various kinds of intelligent services
at the network edge, e.g., auto-driving. In this paradigm, the concerned design
objective of the network shifts from the traditional communication throughput
to the effective and efficient execution of the inference task underpinned by
the network, measured by, e.g., the inference accuracy and latency. In this
paper, a task-oriented over-the-air computation scheme is proposed for a
multidevice artificial intelligence system. Particularly, a novel tractable
inference accuracy metric is proposed for classification tasks, which is called
minimum pair-wise discriminant gain. Unlike prior work measuring the average of
all class pairs in feature space, it measures the minimum distance of all class
pairs. By maximizing the minimum pair-wise discriminant gain instead of its
average counterpart, any pair of classes can be better separated in the feature
space, and thus leading to a balanced and improved inference accuracy for all
classes. Besides, this paper jointly optimizes the minimum discriminant gain of
all feature elements instead of separately maximizing that of each element in
the existing designs. As a result, the transmit power can be adaptively
allocated to the feature elements according to their different contributions to
the inference accuracy, opening an extra degree of freedom to improve inference
performance. Extensive experiments are conducted using a concrete use case of
human motion recognition to verify the superiority of the proposed design over
the benchmarking scheme.
