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Gaussian-Middleton Classification of Cyclostationary Correlated Noise in Hybrid MIMO-OFDM WiNPLC

Moaveninejad S.
•
Kumar A.
•
Elgenedy M.
altro
Tonello A. M.
2019
  • conference object

Abstract
An effective approach to enhance the data rate in narrowband power line communication (NBPLC) system is multicarrier modulation based on orthogonal frequency-division multiplexing (OFDM) and multiple-input multiple-output (MIMO) transmission over multiple power line phases. A key challenge for achieving reliable communication over MIMO-OFDM NBPLC is to mitigate the effects of the correlated non-stationary additive noise. In fact, substantial components of the noise in NBPLC systems exhibit a cyclostationary behavior with a period of half the AC cycle. Moreover, when MIMO transmission is adopted, an important issue that must be considered is the cross-correlation between the different phases. In this work, we propose to classify the cyclostationary noise into three classes, based on the evaluation of second order statistics. In addition, we derive estimates of the probability density functions for each of the three classes and show that while two of them exhibit a Gaussian behavior, the third one has an impulsive behaviour similar to the Middleton class-A noise. Simulation results show that the bit error rate (BER) of MIMO-OFDM NBPLC significantly changes between different classes of noise. Hence, we develop an algorithm for switching data delivery between MIMO-OFDM NBPLC and MIMO-OFDM wireless transmission in unlicensed frequency band that takes into account knowledge of the periodicity of the three classes of noises. The result is a hybrid MIMO-OFDM wireless/NBPLC system, which we refer to as, hybrid MIMO-OFDM WiNPLC. Our simulation results demonstrate BER improvement of the proposed hybrid system over individual MIMO-OFDM NBPLC or MIMO-OFDM wireless systems.
DOI
10.1109/ICC.2019.8761152
Archivio
https://hdl.handle.net/11390/1267780
info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85070216542
https://ricerca.unityfvg.it/handle/11390/1267780
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google-scholar
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