  Departing from the classic paradigm of data-centric designs, the 6G networks
for supporting edge AI features task-oriented techniques that focus on
effective and efficient execution of AI task. Targeting end-to-end system
performance, such techniques are sophisticated as they aim to seamlessly
integrate sensing (data acquisition), communication (data transmission), and
computation (data processing). Aligned with the paradigm shift, a task-oriented
over-the-air computation (AirComp) scheme is proposed in this paper for
multi-device split-inference system. In the considered system, local feature
vectors, which are extracted from the real-time noisy sensory data on devices,
are aggregated over-the-air by exploiting the waveform superposition in a
multiuser channel. Then the aggregated features as received at a server are fed
into an inference model with the result used for decision making or control of
actuators. To design inference-oriented AirComp, the transmit precoders at edge
devices and receive beamforming at edge server are jointly optimized to rein in
the aggregation error and maximize the inference accuracy. The problem is made
tractable by measuring the inference accuracy using a surrogate metric called
discriminant gain, which measures the discernibility of two object classes in
the application of object/event classification. It is discovered that the
conventional AirComp beamforming design for minimizing the mean square error in
generic AirComp with respect to the noiseless case may not lead to the optimal
classification accuracy. The reason is due to the overlooking of the fact that
feature dimensions have different sensitivity towards aggregation errors and
are thus of different importance levels for classification. This issue is
addressed in this work via a new task-oriented AirComp scheme designed by
directly maximizing the derived discriminant gain.
