Cooperative Artificial Neural Networks for Rate-Maximization in Optical Wireless Networks

TL;DR

This paper introduces cooperative artificial neural networks to optimize user association and resource allocation in optical wireless networks, significantly improving rate maximization with low computational cost.

Contribution

It proposes a novel ANN-based framework for real-time optimization of user association and resource allocation in optical wireless networks.

Findings

The proposed model effectively maximizes network sum rate.

ANN solutions are close to optimal with low computational time.

The approach adapts to dynamic user requirements efficiently.

Abstract

Recently, Optical wireless communication (OWC) have been considered as a key element in the next generation of wireless communications due to its potential in supporting unprecedented communication speeds. In this paper, infrared lasers referred to as vertical-cavity surface-emitting lasers (VCSELs) are used as transmitters sending information to multiple users. In OWC, rate-maximization optimization problems are usually complex due to the high number of optical access points (APs) needed to ensure coverage. Therefore, practical solutions with low computational time are essential to cope with frequent updates in user-requirements that might occur. In this context, we formulate an optimization problem to determine the optimal user association and resource allocation in the network, while the serving time is partitioned into a series of time periods. Therefore, cooperative ANN models are designed to estimate and predict the association and resource allocation variables for each user such that sub-optimal solutions can be obtained within a certain period of time prior to its actual starting, which makes the solutions valid and in accordance with the demands of the users at a given time. The results show the effectiveness of the proposed model in maximizing the sum rate of the network compared with counterpart models. Moreover, ANN-based solutions are close to the optimal ones with low computational time.