Improving bit-error-rate performance of the free-space optical communications system with channel estimation based on radiative transfer theory

  • Authors:

  • Colin Reinhardt;Yasuo Kuga;Sermsak Jaruwatanadilok;Akira Ishimaru

  • Affiliations:

  • Electromagnetics and Remote Sensing Laboratory, Department of Electrical Engineering, University of Washington, Seattle, WA;Electromagnetics and Remote Sensing Laboratory, Department of Electrical Engineering, University of Washington, Seattle, WA;Electromagnetics and Remote Sensing Laboratory, Department of Electrical Engineering, University of Washington, Seattle, WA;Electromagnetics and Remote Sensing Laboratory, Department of Electrical Engineering, University of Washington, Seattle, WA

  • Venue:
  • IEEE Journal on Selected Areas in Communications - Special issue on optical wireless communications
  • Year:
  • 2009

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Abstract

In order to improve the performance of terrestrial free-space optical communication systems in adverse visibility conditions, we present a method for estimation of the atmospheric channel impulse response function which governs the optical intensity propagation. This method reduces run-time computational demands and system complexity in comparison to our previously proposed dual-wavelength channel estimation technique. We consider propagation of optical wavelengths in fog, where the droplet diameters are close to the wavelength and thus scattering and absorption effects are significant. A method for rapid calculation of a channel response function based on estimating the effective optical depth of the channel and curve-fitting is described. The channel response estimate can then be used to design a receiver-side equalizer (minimum mean-squared error linear equalizer) to correct the signal distortion due to propagation through the dispersive channel. The channel estimates are based on parametric curve-fitting functions which have been developed using the modified-vector radiative transfer theory to model the channel response. The optimal fit parameters are found using particle-swarm optimization to minimize the simulated bit-error rate of the received signal.