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## Channel models for an indoor power line communication system

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The role of power line communication (PLC) in smart grids becomes more significant as the understanding of the channel improves. Since PLC reduces the additional infrastructure required to link regular devices onto the network, devices can hence be more easily linked to the Internet of Things (IoT) network to create smart buildings, vehicles and grids. The challenge with the harsh PLC channel has been how to mitigate noise in the channel, which is commonly man-made, hence difficult to predict. In this chapter, we evaluate the different indoor PLC channel models and discuss the memoryless channel and the channel with memory. The statistical representations that best describe the channel models are derived which can be used for offline analysis as well as inform appropriate modulation and coding techniques for error mitigation in the channel.

Chapter Contents:

• 4.1 Introduction
• 4.2 Memoryless PLC channel
• 4.2.1 Multipath channel model
• 4.2.2 Middleton class A noise model
• 4.2.3 Single-carrier modulation for PLC
• 4.2.4 Multicarrier modulation for PLC
• 4.3 PLC channel with memory
• 4.4 Hidden Markov models
• 4.4.1 Model notations
• 4.4.2 Model architecture
• 4.4.3 HMM problems
• 4.4.4 Generalized N-state and three-state HMMs
• 4.5 Semi-hidden Fritchman Markov models
• 4.5.1 Generalized SHFMM basics
• 4.5.2 A three-state SHFMM
• 4.6 Machine learning estimation algorithm for SHFMMs
• 4.6.1 The Baum—Welch algorithm
• 4.6.2 First-order Baum—Welch algorithm for SHFMM
• 4.7 SHFMM for indoor PLC system
• 4.7.1 The software-defined NB-PLC transceiver model
• 4.7.2 Modeling methodology
• 4.8 Estimated models—state crossover probabilities
• 4.8.1 Estimated state crossover probabilities (mildly disturbed)
• 4.8.2 Estimated state crossover probabilities (heavily disturbed)
• 4.9 Model validation and analysis
• 4.9.1 Log-likelihood ratio plots for the estimated models
• 4.9.2 Measured versus model error-free run distribution plots
• 4.9.3 Measured versus model error probabilities
• 4.9.4 The chi-square test and the mean square error
• 4.10 Conclusions
• References

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