Over-the-Air Goal-Oriented Communications

TL;DR

This paper explores how programmable metasurfaces can be integrated into wireless channels to perform neural network computations, enabling goal-oriented communication that reduces energy consumption and enhances inference efficiency.

Contribution

It introduces a novel approach where the wireless channel acts as part of a neural network, trained to perform inference, leveraging programmable metasurfaces for computation.

Findings

Metasurface-integrated neural networks achieve comparable accuracy to digital neural networks.

Using the channel for computation reduces energy consumption and transmission power.

The approach is effective across various system parameters and data sets.

Abstract

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.