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.
Highlights
Aging power grids are being increasingly replaced with infrastructure that includes advanced communication technologies, called “smart grids,” thereby bringing attention to the use of power line communication (PLC) as an appropriate network technology [1]
There is no doubt that smart grids will exploit multiple communication technologies to guarantee reliability, and PLC is very attractive as a viable home area networking (HAN) solution, because of its inherent features such as inexpensive installations, the use of existing electrical wiring, broad coverage, and easy scalability
We focus on home area networking technology, which covers LV distribution networks
Summary
Aging power grids are being increasingly replaced with infrastructure that includes advanced communication technologies, called “smart grids,” thereby bringing attention to the use of power line communication (PLC) as an appropriate network technology [1]. Yoon et al [15] proposed a heuristic throughput optimization method that can run without knowing the exact number of contending stations These investigations assume that the communication channel is ideal (i.e., no transmission error due to the bad channel). We propose using the improved adaptive p-persistent CSMA-based dynamic game theory to optimize the saturation performance of the improved LVPLC artificial cobweb and address the problem of how to guarantee the saturation performance under unknown numbers of active stations. This paper is organized as follows: Section 2 presents the low-voltage distribution network topology; Section 3 describes the LVPLC network scheme; Section 4 describes the incompletely cooperative dynamic game model that optimizes the saturation throughput and the access delay performance; Section 5 presents some numerical simulation results not previously reported; and Section 6 draws the conclusions
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
