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New Structure of CCR with an AOANN Threshold

Abstract : In this paper, artificial neural network-based adaptive optimal threshold estimation for a two-dimensional optical code division multiple access conventional correlation receiver is proposed. A multilayer perceptron neural network with back-propagation learning algorithm is considered. This estimator uses the weight ( w ) and the length ( F ) of the code word, the number of active users ( Ν ) and the signal to noise ratio as inputs to estimate the required optimal threshold. We have evaluated the proposed approach on a data set of 46,200 samples. We have found that it gives accurate results: 0.029 for the root mean square error, 0.37% for the relative root mean square error and 99.984% for the correlation coefficient (R), which reflects the efficiency of the proposed optimal threshold estimator.
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https://hal-uphf.archives-ouvertes.fr/hal-03718064
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Submitted on : Friday, July 8, 2022 - 2:43:09 PM
Last modification on : Saturday, July 9, 2022 - 3:57:51 AM

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Abdelhalim Rabehi, Ali Djebbari, Ahmed Hafaifa, Abdelkerim Souahlia, Abdelmalik Taleb-Ahmed. New Structure of CCR with an AOANN Threshold. Journal of Optical Communications and Networking, Piscataway, NJ ; Washington, DC : IEEE : Optical Society of America, 2021, 42 (1), pp.103-109. ⟨10.1515/joc-2018-0028⟩. ⟨hal-03718064⟩

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