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Enhancing performance of next generation FSO communication systems using soft computing-based predictions
Optics Express
|June 12, 2009
Summary
Soft-computing tools enhance free-space optical (FSO) communication performance by predicting key parameters. A multi-layer neural network predictor accurately forecasts values, improving antenna tracking and reducing errors during atmospheric turbulence.
Area of Science:
- Optical communications
- Atmospheric physics
- Artificial intelligence
Background:
- Atmospheric turbulence distorts free-space optical (FSO) beam wave-fronts, degrading system performance and link availability.
- Accurate prediction of FSO system parameters is crucial for mitigating transmission errors and maintaining reliable communication.
Purpose of the Study:
- To investigate the efficacy of soft-computing (SC) tools for enhancing FSO communication system performance.
- To develop and evaluate a multi-layer neural network predictor (MNNP) for forecasting critical FSO parameters.
Main Methods:
- Utilized measured data from an experimental FSO communication system for training and testing.
- Developed a multi-layer neural network predictor (MNNP) to forecast future parameter values.
- Applied SC-based tools for parameter prediction to improve antenna tracking accuracy.
Main Results:
- The MNNP demonstrated acceptable conformity between predicted parameter values and original measurements.
- The SC-based prediction tool proved suitable for improving FSO communication system performance.
- Enhanced antenna tracking accuracy was achieved through the prediction of key parameters.
Conclusions:
- Soft-computing tools, specifically the proposed MNNP, are effective for improving FSO communication system performance.
- Accurate parameter prediction is vital for reducing transmission errors, especially under strong atmospheric turbulence.
- The developed MNNP shows promise for real-time application in FSO systems to ensure reliable data transmission.
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