Machine Learning-based Adaptive Modulation and Noise Reduction for Powerline Communication Efficiency

Power Line Communication (PLC) leverages existing electrical wiring to transmit data, offering a scalable and cost-effective solution for Internet of Things (IoT) and smart grid applications. Despite its potential, PLC faces challenges due to inherently noisy channels and dynamic load variations, which degrade communication reliability. This paper proposes a hybrid framework to enhance PLC performance by integrating adaptive modulation, advanced noise mitigation, and machine learning techniques. The framework evaluates multiple modulation schemes, including Binary Phase Shift Keying (BPSK), Frequency Shift Keying (FSK), Orthogonal Frequency Division Multiplexing (OFDM), and Spread Spectrum, to optimize data transmission under varying channel conditions. To improve the Bit Error Rate (BER), noise reduction techniques such as Kalman Filtering and Forward Error Correction (FEC) are employed. Additionally, machine learning classifiers—Random Forest, Decision Tree, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM)—are utilized to dynamically select the optimal modulation scheme based on real-time Signal-to-Noise Ratio (SNR) measurements. The proposed framework significantly enhances PLC efficiency, achieving lower BER and improved adaptability to fluctuating channel conditions. This approach ensures robust and reliable communication, making PLC a viable solution for smart grid and IoT deployments. The integration of adaptive modulation, noise reduction, and machine learning provides a comprehensive strategy to address the challenges of PLC, paving the way for its broader adoption in modern communication systems.