Goal-oriented communications offer an attractive alternative to the Shannon-based communication paradigm, where the data is never reconstructed at the Receiver (RX) side. Rather, focusing on the case of edge inference, the Transmitter (TX) and the RX cooperate to exchange features of the input data that will be used to predict an unseen attribute of them, leveraging information from collected data sets. This chapter demonstrates that the wireless channel can be used to perform computations over the data, when equipped with programmable metasurfaces. The end-to-end system of the TX, RX, and MS-based channel is treated as a single deep neural network which is trained through backpropagation to perform inference on unseen data. Using Stacked Intelligent Metasurfaces (SIM), it is shown that this Metasurfaces-Integrated Neural Network (MINN) can achieve performance comparable to fully digital neural networks under various system parameters and data sets. By offloading computations onto the channel itself, important benefits may be achieved in terms of energy consumption, arising from reduced computations at the transceivers and smaller transmission power required for successful inference.

翻译：面向目标的通信为基于香农的通信范式提供了一种有吸引力的替代方案，其中数据在接收端从不进行重建。相反，聚焦于边缘推理场景，发射机和接收机协作交换输入数据的特征，这些特征将用于预测其未知属性，并利用从收集的数据集中获取的信息。本章论证了当配备可编程超表面时，无线信道可用于对数据进行计算。发射机、接收机和基于超表面信道的端到端系统被视为一个单一的深度神经网络，该网络通过反向传播进行训练，以对未见数据进行推理。利用堆叠智能超表面，研究表明这种超表面集成神经网络在各种系统参数和数据集下能够实现与全数字神经网络相当的性能。通过将计算卸载到信道本身，可以在能耗方面获得重要收益，这源于收发器计算量的减少以及成功推理所需传输功率的降低。