MGF-Based Mutual Approximation of Hybrid Fading: Performance of Wireless/Power Line Relaying Communication for IoT

Abstract

Wireless and power line communications (PLC) are important components of distribution network communication, and have a broad application prospect in the fields of intelligent power consumption and home Internet of Things (IoT). This study mainly analyzes the performance of a dual-hop wireless/power line hybrid fading system employing an amplify-and-forward (AF) relay in terms of outage probability and average bit error rate (BER). The Nakagami-m distribution captures the wireless channel fading; whereas the PLC channel gain is characterized by the Log-normal (LogN) distribution. Moreover, the Bernoulli-Gaussian noise model is used on the noise attached to the PLC channel. Owing to the similarity between LogN and Gamma distributions, the key parameters of probability density function (PDF) with approximate distribution are determined by using moment generating function (MGF) equations, joint optimization of s vectors, and approximation of LogN variable sum. The MGF of the harmonic mean of the dual Gamma distribution variables is derived to evaluate the system performance suitable for any fading parameter m value. Finally, Monte Carlo simulation is used to verify the versatility and accuracy of the proposed method, and the influence of the hybrid fading channel and multidimensional impulse noise parameters on system performance is analyzed.

Keywords

• hybrid fading
• harmonic mean
• Internet of Things (IoT)
• moment generating function (MGF)
• mutual approximation