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.
翻译:与典型的以数据为中心的设计模式脱节,支持边缘AI的6G网络在支持边缘AI的6G网络中,采用了侧重于高效益和高效率执行AI任务的任务导向技术。针对端对端系统性能,这些技术十分复杂,因为它们旨在无缝地整合遥感(数据获取)、通信(数据传输)和计算(数据处理)。与范式转变一致,本文件提出了面向任务的超空计算(AirComp)办法,供多角度分解系统使用。在考虑的系统中,从设备实时噪音感官数据中提取的以任务为导向的敏感矢量矢量矢量,通过利用多用户频道的波形超定位,在空中对空进行汇总。然后,将服务器收到的汇总性能输入一个推导模型,结果用于决定或控制动作。设计面向偏差的Aircompority(Aircommal),在边缘服务器上传输的预校正校正变校正,以最佳的方式控制合并误差,并尽量提高振度的精确度问题。在空中分类中,通过测度上测度的精确度,通过测测测度,可以测度,通过测测度,通过测测测测度,通过测度测度,将测度测度测测测度,将测度测度测度测度测度测度,测测测测测度,测度,测度。