Rayleigh-Based Signal Processing relies on the statistical properties of Rayleigh distributions to analyze and interpret signal fluctuations, especially in wireless and optical communications. It models random variations in signal amplitude caused by multipath propagation or scattering. This method supports system design for channel estimation, noise mitigation, and performance evaluation. Researchers apply it to optimize data transmission reliability under uncertain conditions. The approach is integral to both theoretical modeling and practical network implementation.
We introduce anchor vectors to monitor Rayleigh-backscattering variability in a fiber-optic computing system that performs nonlinear random projection for image classification. With a ~0.4-s calibration interval, system stability can be maintained with a linear decoder, achieving an average accuracy of 80%-90%.
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