NDLI: Artificial neural network-based nonlinear channel equalization: A soft-output perspective

Content Provider	IEEE Xplore Digital Library
Author	Xuantao Lyu Wei Feng Rui Shi Yukui Pei Ning Ge
Copyright Year	2015
Description	Author affiliation: Tsinghua Nat. Lab. for Inf. Sci. & Technol., Tsinghua Univ., Beijing, China (Rui Shi) \|\| Dept. of Electron. Eng., Tsinghua Univ., Beijing, China (Xuantao Lyu; Wei Feng; Yukui Pei; Ning Ge)
Abstract	The artificial neural network (ANN) has been shown that, is an effect technique used to gain insight into channel equalizer design, to combat nonlinear distortion in wireless communication systems. Also, the joint design of channel equalizer and decoder can provides great advantages for system performance. However, research on the soft output of an ANN-based equalizer still remains largely open. Towards this end, this paper proposes an accurate soft information characterization for an ANN-based channel equalizer, which is crucial for the joint development of equalization and decoding. Particularly, we focus on the functional link ANN (FLANN)-based channel equalizer. By adopting the Kolmogorov-Smirnov test, we find that the error signal of a FLANN-based equalizer is not Gauss, which would pose a challenge to the calculation of the soft information. We use the mix-Gauss distribution to model the error signal, and accordingly the log-likelihood ratio (LLR) from a FLANN-based equalizer is derived. We also give insight into the mix-Gauss model that one component stands for the channel noise and another component stands for the noise caused by the equalizer, which may shed some lights on the optimization of a FLANN-based equalizer.
Starting Page	243
Ending Page	248
File Size	150952
Page Count	6
File Format	PDF
ISBN	9781479980789
DOI	10.1109/ICT.2015.7124690
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-27
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Analytical models Nonlinear Equalizers LLR Noise Artificial neural networks Chebyshev approximation Decoding Telecommunications FLANN-based equalizer mix-Gauss distribution
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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