Non-Linear Relay Optimization using Deep-Learning tools

TL;DR

This paper introduces a novel deep learning-based approach to optimize large non-linear relay networks, achieving significant performance gains over traditional linear methods by treating the relay network as a neural network.

Contribution

It presents a new deep relay optimization method that models relay networks as neural networks, leveraging non-linear transfer functions for improved performance.

Findings

Over 15dB gain compared to traditional methods

Effective implementation of network functionality over relay networks

Demonstrates potential for significant advancements in wireless systems

Abstract

Widespread deployment of relays can yield a significant boost in the throughput of forthcoming wireless networks. However, the optimal operation of large relay networks is still infeasible. This paper presents two approaches for the optimization of large relay networks. In the traditional approach, we formulate and solve an optimization problem where the relays are considered linear. In the second approach, we take an entirely new direction and consider the true non-linear nature of the relays. Using the similarity to neural networks, we leverage deep-learning methodology. Unlike previous applications of neural networks in wireless communications, where neural networks are added to the network to perform computational tasks, our deep relay optimization treats the relay network itself as a neural network. By exploiting the non-linear transfer function exhibited by each relay, we achieve over 15dB gain compared to traditional optimization methods. Moreover, we are able to implement part of the network functionality over the relay network. Our findings shed light on the potential of deep relay optimization, promising significant advancements in future wireless communication systems.