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Extreme Learning Machine-Based Receiver for MIMO LED Communications

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arxiv 1903.01551 v1 pith:KHGXH7IS submitted 2019-02-27 eess.SP cs.LG

Dawei Gao , Qinghua Guo This is my paper · ORCID
classification eess.SPcs.LG
keywords receivercommunicationsnonlinearitycross-ledextremehandleinputinterference
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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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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Survey on Machine Learning for Optical Communication [Machine Learning View]

    eess.SP 2019-08 reject novelty 2.0 of 10

    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.

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