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Balancing method based on probability nerve network and applied to visible light communication system

A probabilistic neural network and visible light communication technology, which is applied in the field of visible light communication and can solve problems such as affecting the performance of visible light systems and deteriorating communication performance.

Active Publication Date: 2015-12-30
SYSU CMU SHUNDE INT JOINT RES INST +1
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Problems solved by technology

Due to the characteristics of visible light reflection and refraction and indoor complex channel conditions, the intersymbol interference caused by the multipath effect seriously affects the performance of the visible light system and greatly reduces its communication performance.

Method used

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  • Balancing method based on probability nerve network and applied to visible light communication system
  • Balancing method based on probability nerve network and applied to visible light communication system
  • Balancing method based on probability nerve network and applied to visible light communication system

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Embodiment Construction

[0057] The drawings are for illustrative purposes only, and should not be construed as limitations on this patent; in order to better illustrate this embodiment, some parts in the drawings will be omitted, enlarged or reduced, and do not represent the size of the actual product;

[0058] For those skilled in the art, it is understandable that some well-known structures and descriptions thereof may be omitted in the drawings. The technical solutions of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0059] The invention transforms the equilibrium problem into a classification problem. Under the optimal Bayesian criterion, a probability neural network is introduced as an equalizer. Furthermore, the present invention combines a decision feedback method and proposes a classification-based probability neural network equalization method using decision feedback.

[0060] Equalization issues in visible light commu...

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Abstract

The invention discloses a balancing method based on the probability nerve network and applied to a visible light communication system. The balancing method in a visible light system is analyzed, and a balancing problem is converted into a classification problem. Under the Bayes optimum criterion, the probability nerve network is introduced as a balancer, through mode unit substitution and probability density function estimation, channel output state estimation is avoided, system channel state evaluation complexity is reduced, interference among symbols can be better inhibited, and bit error rate performance of the visible light communication system is improved. Considering the channel complexity, decision feedback is introduced to an originally-proposed new balancer scheme, performance improvement for the original balancer is verified through simulation.

Description

technical field [0001] The present invention relates to the field of visible light communication, and more specifically, relates to proposing an equalization method based on a probabilistic neural network applied in a visible light communication system, which can suppress inter-symbol interference. Background technique [0002] In recent years, with the development of LED technology and technology, visible light communication (VisibleLightCommunications, VLC) is regarded as a powerful supplement to traditional short-distance wireless communication. Compared with the traditional wireless communication technology, visible light communication uses the visible light frequency band with higher frequency and wider spectrum, and has the advantages of reliability, safety, wider frequency band and wider application range. [0003] However, in the indoor visible light communication system, due to the arbitrary distribution of objects, the situation of the visible light channel becomes...

Claims

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Application Information

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IPC IPC(8): H04L25/03H04B10/116
Inventor 张琳庄博渊江明
Owner SYSU CMU SHUNDE INT JOINT RES INST
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