Investigation of the Lower Bound of Outage Threshold in Indoor Power-Line Networks
- Authors
- Shin, Jaeyoung; Jeong, Jichai
- Issue Date
- 6월-2016
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Channel capacity; lower bound; outage; power-line communication (PLC)
- Citation
- IEEE TRANSACTIONS ON POWER DELIVERY, v.31, no.3, pp.913 - 920
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE TRANSACTIONS ON POWER DELIVERY
- Volume
- 31
- Number
- 3
- Start Page
- 913
- End Page
- 920
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/88399
- DOI
- 10.1109/TPWRD.2014.2388217
- ISSN
- 0885-8977
- Abstract
- We theoretically investigate lower bound of the outage threshold based on channel capacity degradation in indoor power-line networks and its practical applicability to a realistic indoor power-line network existing in practice. We estimate the channel capacity degradations of arbitrary channels in networks with uniformly distributed branches with regular length. The channel capacity degradations are estimated under various network configurations, such as network size, branch characteristics (including the number and length), and loading conditions (including the number, type, impedance, and location). Based on the channel capacity degradation, the lower bounds of the outage threshold of arbitrary channels, which can evaluate the quality of the channels, are given. To evaluate the practical applicability of the simulated results, we compare the lower bounds of the outage threshold between a realistic network and simple networks. It is shown that the Pearson's correlation coefficients between the realistic network and the simple networks are predominantly greater than 0.85 (-value 0.01) in almost all cases, which shows a strong correlation in terms of the outage behavior. Therefore, our investigation enables estimating the outage behavior of a realistic network, without sufficient prior information of the network configuration using a simple network with a similar network configuration.
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Collections - Graduate School > Department of Brain and Cognitive Engineering > 1. Journal Articles
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