Abstract

Power-line communication (PLC) networks have been increasingly used for constructing industrial IoT (internet of things) and home networking systems due to their low-cost installation and broad coverage feature. To guarantee the transmission reliability, ARQ (automatic repeat request) scheme is introduced into the link layer of reliable PLC networks, which allows the retransmission of a data frame several times so that it has a higher probability to be correctly received. However, current studies of performance analysis for PLC MAC (medium access control) protocol (i.e., IEEE 1901) do not take into account of the impact of ARQ scheme. To resolve this problem, we propose an analytical model to investigate the MAC performance of IEEE 1901 protocol for reliable PLC networks with ARQ scheme. In the modeling process, we first establish a PLC channel model to reflect the impacts of PLC channel types (containing Rayleigh fading and Log-normal fading), additive non-Gaussian noise feature and ARQ scheme on data transmission at link layer. Next, we employ Renewal theory and Queueing dynamics to capture the transmission attempt behavior of executing IEEE 1901 protocol in the unsaturated environment with finite transit buffer size. On the basis of combining these two models, we derive the closed-form expressions of 1901 MAC metrics considering the influence of the ARQ scheme. Furthermore, we prove that the proposed analytical model has the convergence property. Finally, we evaluate the MAC performance of 1901 protocol for reliable PLC networks with ARQ scheme and verify the proposed analytical model.

Highlights

  • Received: 3 December 2020Accepted: 25 December 2020Published: 30 December 2020Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.This article is an open access articleThe past decade has witnessed a rapid development of IoT (Internet of things), which provides convenience for consumers, manufacturers, and public welfare [1,2,3,4]

  • The modeling process is divided into four steps: (1) Establishing the power-line communication (PLC) channel model to reflect the impacts of channel fading types, additive non-Gaussian noise feature and ARQ scheme on data transmission at link layer; (2) Providing a Renewal theory and Queueing dynamics-based model to depict the transmission attempt behavior of executing 1901 protocol in unsaturated environment with finite transit buffer; (3) Deriving the closed-form expressions of 1901’s MAC metrics for reliable PLC networks with ARQ

  • We put forward a MAC performance analysis model of IEEE 1901 protocol for reliable PLC networks with ARQ scheme

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Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Because of the introduction of the ARQ scheme, the performance of MAC (medium access control) protocol for reliable PLC networks i.e., IEEE 1901 [16,17,18,19,20], would be necessarily affected. These works fail to investigate whether their established models have the convergence property It is still a great challenge for us to propose an analytical model with insightful understanding, which can accurately evaluate the MAC performance of IEEE 1901 under the influence of ARQ scheme, channel fading type and additive non-Gaussian noise feature, and prove this model has the convergence property. Making thorough analysis of IEEE 1901 protocol for reliable PLC networks with ARQ scheme has practical value for guiding IoT system construction

Contributions
Paper Outline
IEEE 1901 Protocol
ARQ Scheme
Related Work
System Model
Rayleigh Fading Channel
Log-Normal Fading Channel
The Model of IEEE 1901 Protocol
The Derivation of Pn Based on Queueing Dynamics
The MAC Metrics of 1901 Considering ARQ Scheme
Convergence Analysis of the Proposed Model
Performance Evaluation
The Impact of Network Size N
The Impact of Packet Arrival Rate λ
The Impact of Threshold SNR ζ
The Impact of the Probability of Impulsive Noise Component PI
Conclusions
FPI Method
Full Text
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