ANN-Assisted Real-Time Blind Signal Detection Over Time-Varying Doubly-Stochastic Poisson Channel for Indoor Optical Wireless Communications

In this letter, we present a new real-time blind signal detection technique for indoor optical communications using a supervised learning framework in an artificial neural network (ANN). We model the optical channel as a time-varying doubly-stochastic Poisson process. To compare the performance of t...

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Bibliographic Details
Published inIEEE photonics journal Vol. 12; no. 5; pp. 1 - 18
Main Authors Arya, Sudhanshu, Chung, Yeon Ho
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.10.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Adaptive optics
Artificial neural network
Artificial neural networks
Bit error rate
Channel estimation
indoor communications
Iterative methods
Learning theory
Optical communication
optical communications
Optical fiber communication
Optical transmitters
Poisson density functions
Real time
Receivers
Signal detection
Viterbi algorithm
Viterbi decoding
Wireless communications
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Summary:In this letter, we present a new real-time blind signal detection technique for indoor optical communications using a supervised learning framework in an artificial neural network (ANN). We model the optical channel as a time-varying doubly-stochastic Poisson process. To compare the performance of the proposed ANN network, we present an iterative method for joint channel estimation and symbol detection using expectation-maximization (EM) and Viterbi decoding and demonstrate that the bit error rate (BER) performance of the proposed technique is better than that of the Viterbi decoding. The proposed scheme is found to be resilient to indoor channel dynamics and can achieve a good BER performance over a wide range of channel variations.
ISSN:1943-0655
1943-0655
1943-0647
DOI:10.1109/JPHOT.2020.3019824