NDLI: Performance evaluation of PLC with log-normal channel gain over Nakagami-m additive background noise

Content Provider	IEEE Xplore Digital Library
Author	Mathur, A. Bhatnagar, M.R. Panigrahi, B.K.
Copyright Year	2015
Description	Author affiliation: Dept. of Electr. Eng., Indian Inst. of Technol. - Delhi, New Delhi, India (Mathur, A.; Bhatnagar, M.R.; Panigrahi, B.K.)
Abstract	Power line communication (PLC) utilizes power lines for the purpose of electronic data transmission. The performance of a PLC system is significantly affected by the additive and multiplicative power line noises; the additive noises are of two types, namely background noise and impulsive noise. Whereas, the multiplicative PLC noise leads to fading in the received signal strength. In this paper, we evaluate the performance of a PLC system over log-normal fading channel under Nakagami-m distributed additive background noise assuming binary phase shift keying modulation scheme. The analysis involving log-normal fading is very complicated. Hence we use a novel gamma approximation to log-normal distribution for our analysis. We evaluate the probability density function of the decision variable. A closed-form expression of the analytical average bit error rate of the considered system is derived. We also compute the diversity order of the considered PLC system. The validity of the derived analytical expressions is closely verified by the simulation results.
Starting Page	824
Ending Page	829
File Size	175973
Page Count	6
File Format	PDF
e-ISBN	9781467367820
DOI	10.1109/PIMRC.2015.7343411
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-30
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Noise measurement Fading Bit error rate Additives Binary phase shift keying Detectors Additive noise power line communications Background noise bit error rate log-normal fading Nakagami-m distribution
Content Type	Text
Resource Type	Article

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