Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Blind equalization method for wavelet neural network based on space diversity

A wavelet neural network, space diversity technology, applied to shaping networks in transmitter/receiver, error prevention/detection through diversity reception, baseband system components, etc., can solve problems such as wasting bandwidth resources

Inactive Publication Date: 2010-02-17
NANJING UNIV OF INFORMATION SCI & TECH
View PDF0 Cites 8 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Traditional equalization techniques need to periodically send training sequences, which wastes limited bandwidth resources. Blind equalization techniques that do not need to send known training sequences can save bandwidth and improve communication efficiency.
Through equalization technology, an equalizer that is completely opposite to the channel characteristics can be designed to offset the influence of channel distortion (see literature: [3] E.G.Larsson, On the combination of spatial diversity and multi-user diversity [J]. IEEECommunications Letters, 2004, 8: 517-519), but the traditional blind equalization method is researched on a single channel, and the new generation of high-speed underwater communication system will adopt the method based on multipath equalization, so the emergence of diversity technology is a blind The design of the equalizer proposes a new idea (see literature: [4] Sung-Hoon Moon, Ju-Yeun Kim and Dong-Seog. Han. Spatial diversity technique for Improvement of DTV reception performance [J]. IEEE Transactions on consumer electronics , 2003, 49(4): 958~964)

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Blind equalization method for wavelet neural network based on space diversity
  • Blind equalization method for wavelet neural network based on space diversity
  • Blind equalization method for wavelet neural network based on space diversity

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0100] In order to verify the performance of the method SDE-WNN of the present invention, an example analysis is carried out using an underwater acoustic channel.

[0101] [Example 1] In the example, a typical sparse two-path underwater acoustic channel H is used 1 (z)=1+0.4z -12 and the homogeneous medium two-path underwater acoustic channel H 2 (z)=1+0.59997z -20 The transmitted signal is 4QAM, the signal-to-noise ratio is 20dB, D=2 is adopted in the experiment, WNN1 and WNN2 are used to represent the wavelet neural network blind equalizer of channel 1 and channel 2, and the length of the wavelet neural network blind equalizer is 11.

[0102] Figure 4 Simulation result shows, the convergence speed of the inventive method SDE-WNN will be faster than WNN1 and WNN2, as can be seen from figure (a), the inventive method SDE-WNN mean square error is smaller than WNN1 1dB, and obviously smaller than WNN2 4dB, Figure 4 Comparing the three figures (b), (c) and (d), it can be se...

Embodiment 2

[0103] [Embodiment 2] still adopt the channel of Embodiment 1, the transmission signal is 2PAM, and the signal-to-noise ratio is 20dB, adopts D=2 in the experiment, represents the wavelet neural network blind equalizer of channel 1 and channel 2 with WNN1 and WNN2, wavelet The length of the neural network blind equalizer is 11.

[0104] Figure 5 Show, the convergence speed of the inventive method SDE-WNN will be faster than WNN1 and WNN2, and mean square error is obviously smaller than WNN1 and WNN2 2dB and 5dB, Figure 5 The comparison of (b), (c) and (d) shows that the constellation diagram of the SDE-WNN method of the present invention is clearer, more compact, and the equalization effect is more obvious.

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a blind equalization method for a wavelet neural network based on space diversity. On the basis of analysis of a space diversity technology and the equalization performance of a wavelet neural network, the method reduces the influence of fading by utilizing the space diversity and overcomes intersymobl interferences by using the stronger approximation capacity of a blind equalizer of the wavelet neural network. The invention overcomes the intersymobl interferences caused by the multipath propagation and the fading characteristic of a channel at a receiving end, improves the communication quality and has high convergence speed and small mean square error. The effectiveness of the method is verified by an acoustic channel simulation result. The method can effectivelyrealize the separation of signals and noise and the real-time restoration of the signals.

Description

technical field [0001] The invention relates to a wavelet neural network blind equalization method based on space diversity, and belongs to the technical field of blind equalization methods for overcoming inter-symbol interference (Inter-Symbol Interference, ISI) caused by multipath fading of underwater acoustic channels. Background technique [0002] In the underwater communication system, inter-symbol interference (ISI) caused by multipath fading and channel distortion distorts the transmitted signal and generates bit errors at the receiving end, which seriously affects the communication quality. An effective means to reduce intersymbol interference is to use equalization technology. Since the equalization itself can be regarded as a pattern classification problem, and the neural network has good pattern classification characteristics, it is worth studying to design a blind equalizer with a neural network (see literature [1] CHENG Hai-qing, ZHANG Li-yi. Blind Equalization...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): H04L25/03H04L1/06H04B13/02
Inventor 郭业才高敏
Owner NANJING UNIV OF INFORMATION SCI & TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com