A survey of machine learning for optical communication that classifies many references by algorithm type, but contains factual inaccuracies and an unsupported first-time claim.
Extreme Learning Machine-Based Receiver for MIMO LED Communications
1 Pith paper cite this work. Polarity classification is still indexing.
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Pith paper citing it
abstract
This work concerns receiver design for light-emitting diode (LED) multiple input multiple output (MIMO) communications where the LED nonlinearity can severely degrade the performance of communications. In this paper, we propose an extreme learning machine (ELM) based receiver to jointly handle the LED nonlinearity and cross-LED interference, and a circulant input weight matrix is employed, which significantly reduces the complexity of the receiver with the fast Fourier transform (FFT). It is demonstrated that the proposed receiver can efficiently handle the LED nonlinearity and cross-LED interference.
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eess.SP 1years
2019 1verdicts
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A Survey on Machine Learning for Optical Communication [Machine Learning View]
A survey of machine learning for optical communication that classifies many references by algorithm type, but contains factual inaccuracies and an unsupported first-time claim.