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

Method of training sequence time change step length least mean square

A technology of training sequence and time-varying step size, which is applied in the field of channel estimation and training sequence time-varying step size minimum mean square, which can solve the problem of slowing down the convergence speed of the time-domain LMS algorithm and achieve the effect of improving system performance

Inactive Publication Date: 2008-08-27
UNIV OF ELECTRONICS SCI & TECH OF CHINA ZHONGSHAN INST
View PDF1 Cites 10 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] ②Transform domain LMS algorithm When the input signal itself has a strong correlation, the time domain LMS algorithm will slow down the convergence speed

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
  • Method of training sequence time change step length least mean square
  • Method of training sequence time change step length least mean square
  • Method of training sequence time change step length least mean square

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0032] The technical solution of the present invention will be further described through specific implementation below.

[0033] The specific steps are:

[0034] 1. The transmitting end sends in the OFDM modulated baseband signal and training sequence u(n) to generate a guard interval, and generates a transmit signal through the D / A and shaping filter.

[0035] 2. At the receiving end, after the received signal passes through the A / D and low-pass filter, the guard interval is deleted, and the received signal matrix Y is obtained. Among them, v is noise.

[0036] Y=Uh+r

[0037] 3. Select the training sequence u(n) and set the parameter β 0 The value of the error matrix e(n) is calculated. in:

[0038] e ( n ) = Y ( n ) - u ( n ) T ...

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 provides a training sequence variable step-size least-mean-square algorithm, in particular to a training sequence quick channel estimation method. Employing factors which are capable of adaptively tracking and controlling the changes of characteristic parameters through error estimation, the quick channel estimation method improves the convergence speed of the algorithm and acquires the weight coefficient of an optimum filter speedily. The invention discloses a training sequence least-mean-square characteristic parameter estimation method which is quicker in convergence speed and more precise than prior art. The method is easy to realize and is applicable in communication systems for modulated orthogonal frequency division multiplexing for channel estimation. Meanwhile, the idea of the invention can be applied to CDMA channel estimation devices and TDMA channel estimation devices, and can also be applied to LMS methods and the derivative methods thereof. The channel estimation method relates to the fields of communication, oil and seismic exploration, sonar, image processing, computer vision, biomedical engineering, vibration engineering, radar, remote control and telemetry, as well as aerospace field.

Description

Technical field: [0001] The invention relates to a minimum mean square method with time-varying step size of a training sequence. In particular, it relates to channel estimation technology in digital terrestrial television, single-carrier OFDM communication system, multi-carrier OFDM communication system, wireless local area network (WLAN) and other digital communication systems using OFDM modulation. [0002] At the same time, the present invention relates to a training sequence time-varying step-size least mean square method, which can be used for channel estimation in code division multiple access (CDMA) and time division multiple access (TDMA) systems, and can also be used for petroleum earthquakes Estimation of other characteristic parameters in fields and technologies such as exploration, radar, aerospace, sonar, biomedical engineering, and image processing. Background technique: [0003] The Least Mean Square (LMS) algorithm and the Recursive Least Squares (RLS) algo...

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): H04L27/22H04B7/26
Inventor 罗仁泽马争马云辉师向群杨晓峰罗朗黄岚卢晶琦张华斌王红航高玉梅李亚李井润陈李胜文毅阎林谭朝阳石建国陈永海孟庆元刘咏梅
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA ZHONGSHAN INST
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