Abstract
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.
Highlights
Optical wireless communication (OWC) has recently attracted much attention and emerged as a promising solution to indoor and outdoor optical communication systems
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)
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
Summary
Optical wireless communication (OWC) has recently attracted much attention and emerged as a promising solution to indoor and outdoor optical communication systems. One of the critical issues in efficient recovery of the information sent over a time-varying optical channel is the design of a detection algorithm, where the transmitted information bit is estimated from the noisy and corrupted signal received at. An indoor OWC transmitter must obey power constraints imposed to adhere to the illumination control. Another important challenge is the optical shot noise generated by random characteristics of the optical transmitter [2]. To address such non-trivial challenges, this paper presents an ANN-based data detection algorithm under time-varying channel conditions. We present an algorithm to estimate the shape parameters of the system response
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