Network Optimization -- Using Relays as Neurons

TL;DR

This paper introduces a novel approach to optimize relay networks by modeling relays as neurons in a neural network, leveraging deep learning techniques to enhance performance and functionality.

Contribution

It presents a new method that treats relay nodes as neural network neurons, enabling advanced optimization and increased transmission power without distortion.

Findings

Significant performance gains over traditional relay optimization.

Enables relays to operate in their non-linear regime effectively.

Allows simpler receiver design due to improved relay functionality.

Abstract

We consider the optimization of a network with amplify-and-forward relays. Observing that each relay has a power limit, and hence a non-linear transfer function, we focus on the similarity between relay networks and neural networks. This similarity allows us to treat relays as neurons, and use deep learning tools to achieve better optimization of the network. Deep learning optimization allows relays to work in their non-linear regime (and hence increase their transmission power) while still avoiding harmful distortion. Moreover, like neural networks, which can implement almost any functionality, we can take advantage of the non-linearities and implement parts of the received functionalities over the relay network. By treating each relay element as a node in a deep neural network, our optimization results in huge gains over traditional relay optimization, and also allows the use of simpler receivers.