Network Performance Optimization for Low-Voltage Power Line Communications

Abstract

Low-voltage power line communication (LVPLC) medium access control protocols significantly affect home area networks performance. This study addresses poor network performance issues caused by asymmetric channels and noise interference by proposing the following: (i) an improved Q learning method for optimizing the improved artificial LVPLC cobweb, wherein the learning-based hybrid time-division-multiple-access (TDMA)/carrier-sense-multiple-access (CSMA) protocol, the asymmetrical network system, is modeled as a discrete Markov decision process, associates the station information using online trial-and-error learning, builds a routing table, periodically studies stations to choose a better forward path, and optimizes the shortest backbone cluster tree between the central coordinator and the stations, guaranteeing network stability; and (ii) an improved adaptive p-persistent CSMA game optimization method is proposed to optimize the improved artificial cobweb saturation throughput and access delay performance. The current state of the game (e.g., the number of competitive stations) for each station is estimated by the hidden Markov model. The station changes its equilibrium strategy based on the estimated number of active stations, which reduces the collision probability of data packets, optimizes channel transmission status, and increases performance by dynamically adjusting the probability p. An optimal saturation performance is achieved by finitely repeating the game. We present numerical results to validate our proposed approach.

An overview of DLMS/COSEM and g3-plc for smart metering applications

“…In Bumiller (2009), Bumiller et al (2010), Ngcobo and Ghayoor (2019), the effects of unforeseen changes of topology on the reliability of PLC networks have been studied, and the concept of single-frequency network transmission is presented for flooding of messages. The recent shift in improving routing in the G3-PLC is towards employing artificial intelligence methods (Cui et al, 2018;Marcuzzi and Tonello, 2019).…”

“…Both wireline and wireless technologies have been used in AMI networks. While wireless technologies are more suitable for geographically dispersed and versatile networks, Power-Line Communication (PLC) technologies are the preferred and cost-effective choice for dense urban areas, as no additional media installation is required (Deblasio and Tom, 2008;Erlinghagen et al, 2015). Power line intelligent metering evolution (PRIME), supported by the PRIME Alliance, and G3-PLC, supported by the G3-PLC Alliance, are the most widely used PLC-based standards in AMI networks.…”

Possibilities of Broadband Power Line Communications for Smart Home and Smart Building Applications

“…The throughput on the transport layer was in the range of 5.8–21 Mbps. The authors of [ 23 ] simulated the topology of a distribution network in which the Carrier-Sense Multiple Access (CSMA) is commonly used. In the simulation, they proposed an improved adaptive p-persistent CSMA protocol based on the dynamic game optimization.…”

Information Technology, Artificial Intelligence and Machine Learning in Smart Grid – Performance Comparison between Topology Identification Methodology and Neural Network Identification Methodology for the Branch Number Approximation of Overhead Low-Voltage Broadband over Power Lines Network Topologies

“…As already been mentioned, the leading philosophy behind the BPL channel characterization literature concentrates on following either a deterministic approach or a statistical approach or a bottom-up approach or a top-down approach or appropriate syntheses of the aforementioned approaches, but artificial intelligence (AI) and machine learning (ML) aspire to derive the input-output relations of the BPL channels by learning and capturing information from big data such as those stored in TIM OV LV BPL topology database [39][40][41]. Among the available AI and ML schemes, neural networks are chosen in this paper due to: (i) their popularity in the communications research field; (ii) their ease of implementation in various architectures; and (iii) their ability to be trained through the backpropagation learning process [42,43].…”