Abstract
Narrowband Power Line Communication (NB-PLC) enables low-cost, low-power connectivity
for smart grid and Internet of Things (IoT) applications by leveraging existing electrical infrastructure.
However, its performance in indoor settings is hindered by impulsive noise, burst
errors, and temporally correlated interference, which challenge reliable communication and
robust protocol design.
This thesis proposes a statistically grounded, experimentally driven approach to evaluating
NB-PLC performance under bursty channel conditions. First, an uncoded, reconfigurable
software-defined radio (SDR) testbed—drawing on the structure of the International Telecommunication
Union – Telecommunication Standardization Sector (ITU-T) Home Networking
Energy Management (G.hnem) standard—is developed to capture single- and multicarrier
NB-PLC signals in realistic indoor settings, yielding empirical error traces with modulationspecific
temporal characteristics.
Second, empirical error traces are used to train generative models that capture the temporal
correlation and burstiness of NB-PLC channels. Semi-Hidden Fritchman–Markov Models
(SHFMMs) are applied to bit-level data using standard and modified Baum–Welch algorithms
(BWAs), and evaluated via log-likelihood and error-free run distributions. A Block-Diagonal
Semi-Hidden Fritchman-Markov Model (BD-SHFMM) variant is introduced to reduce training
complexity through run-length-based estimation while preserving statistical fidelity.
Third, BD-SHFMMs are integrated into a modular simulation testbed to assess forward error
correction (FEC) schemes under realistic burst-error conditions. Reed–Solomon (RS), convolutional,
and concatenated RS + convolutional codes are evaluated across modulation formats
and noise levels. Only the concatenated scheme consistently achieves low error rates and effective
error dispersion, supporting the G.hnem standard design and revealing the shortcomings
of memoryless error models.
Collectively, the thesis offers a reproducible, statistically grounded framework for NB-PLC performance
analysis that unifies physical-layer testing, stochastic error modeling, and decoder
evaluation. It contributes practical tools for burst-aware design, adaptive coding, and integration
into future hybrid power line communication (PLC)–wireless systems.