Hemed, Salem and Habaebi, Mohamed Hadi and Islam, Md. Rafiqul and Zabidi, Suriza Ahmad
(2026)
A Survey on channel impairment resilience with machine learning in free-space optical communication links.
Computers and Electrical Engineering, 139 Part B (11).
pp. 1-28.
ISSN 0045-7906
E-ISSN 1879-0755
Abstract
Free-space optical communication has emerged as a critical enabler for next-generation wireless
networks, offering license-free spectrum, inherent security, and ultra-high data rates. However,
its performance remains highly vulnerable to atmospheric impairments that severely degrade link
reliability. Traditional mitigation techniques often rely on linear assumptions and reactive
mechanisms, limiting their effectiveness in dynamic environments. Recently, machine learning
and deep learning have introduced a paradigm shift, enabling data-driven solutions capable of
capturing nonlinear, time-varying channel behaviors for both proactive and reactive resilience.
This survey focuses exclusively on deep learning techniques for mitigating free-space optical
channel impairments. We propose a unified taxonomy classifying applications into five domains:
channel estimation; prediction; modeling; signal detection and recovery; and intelligent switching
in hybrid free-space optical/radiofrequency systems. For each category, we review state-of-the-art
architectures, synthesizing their performance metrics and inherent limitations. Given the scarcity
of real-world free-space optical data, we critically evaluate existing datasets and the resulting
simulation-to-reality gap. Furthermore, we identify that practical deployment remains constrained
by limited generalization, reliance on simulated datasets, lack of standardized benchmarks,
and hardware-model mismatches. The paper highlights these challenges and outlines
future research directions focused on dataset standardization, hardware-aware model design, and
adaptive learning frameworks to enable reliable real-world deployment of intelligent free-space
optical systems.
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