Deep learning for channel estimation in FSO communication system

TL;DR

This paper proposes low-cost, low-complexity deep learning methods for channel estimation in free-space optical communication systems, demonstrating performance close to perfect estimation across various atmospheric turbulence conditions.

Contribution

It introduces novel combinations of deep learning with conventional structures for channel estimation, showing immunity to turbulence variations and suitability for mobile systems.

Findings

Deep learning approaches achieve near-perfect channel estimation performance.

Proposed methods are low cost and low complexity.

Effective across all atmospheric turbulence regimes.

Abstract

Perfect channel estimation is very hard, time/ power consuming, and expensive; so it is not preferred (e.g. in mobile) communication systems. This paper seeks for new, cheap, low complexity, deep learning based solution. Several new combinations of deep learning and conventional structures (in different parts such as constellation shaper, channel estimator, and detector) are presented investigated, and compared over all atmospheric turbulence regimes from weak to strong (considering Gamma-Gamma atmospheric turbulence model). Results indicate that deep learning could provide close enough performance to the perfect channel estimation scheme, and it is immune to the atmospheric turbulence variation. The proposed deep learning based solutions are low cost, low complexity, with favorable performance. Accordingly, they are recommended for channel estimation in mobile communication systems. Because these system should deliver favorable, and cheap services to the costumers, which use a small mobile as transceiver that needs to be cheap, low complexity and low power consuming.