# Optimal WDM Power Allocation via Deep Learning for Radio on Free Space Optics Systems

## TL;DR

This paper introduces a deep learning-based approach for optimal power allocation in WDM Radio on Free Space Optics systems, enhancing capacity while respecting power and safety constraints.

## Contribution

It develops a model-free primal-dual deep learning algorithm for power allocation, outperforming traditional equal allocation methods.

## Key findings

- Deep learning algorithm achieves higher capacity than equal power allocation.
- Model-free approach does not require system knowledge.
- Significant performance improvements demonstrated through simulations.

## Abstract

Radio on Free Space Optics (RoFSO), as a universal platform for heterogeneous wireless services, is able to transmit multiple radio frequency signals at high rates in free space optical networks. This paper investigates the optimal design of power allocation for Wavelength Division Multiplexing (WDM) transmission in RoFSO systems. The proposed problem is a weighted total capacity maximization problem with two constraints of total power limitation and eye safety concern. The model-based Stochastic Dual Gradient algorithm is presented first, which solves the problem exactly by exploiting the null duality gap. The model-free Primal-Dual Deep Learning algorithm is then developed to learn and optimize the power allocation policy with Deep Neural Network (DNN) parametrization, which can be utilized without any knowledge of system models. Numerical simulations are performed to exhibit significant performance of our algorithms compared to the average equal power allocation.