CN108848044A - A kind of extracting method of channel fine feature - Google Patents

A kind of extracting method of channel fine feature Download PDF

Info

Publication number
CN108848044A
CN108848044A CN201810658989.3A CN201810658989A CN108848044A CN 108848044 A CN108848044 A CN 108848044A CN 201810658989 A CN201810658989 A CN 201810658989A CN 108848044 A CN108848044 A CN 108848044A
Authority
CN
China
Prior art keywords
peak
signal
channel
blind equalization
fine feature
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN201810658989.3A
Other languages
Chinese (zh)
Inventor
魏平
饶烔恺
廖红舒
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
University of Electronic Science and Technology of China
Original Assignee
University of Electronic Science and Technology of China
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by University of Electronic Science and Technology of China filed Critical University of Electronic Science and Technology of China
Priority to CN201810658989.3A priority Critical patent/CN108848044A/en
Publication of CN108848044A publication Critical patent/CN108848044A/en
Pending legal-status Critical Current

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/0238—Channel estimation using blind estimation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/024—Channel estimation channel estimation algorithms

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Power Engineering (AREA)
  • Quality & Reliability (AREA)
  • Physics & Mathematics (AREA)
  • Electromagnetism (AREA)
  • Cable Transmission Systems, Equalization Of Radio And Reduction Of Echo (AREA)

Abstract

The invention belongs to signal sorting technical fields, particularly relate to a kind of extracting method of channel fine feature.Emit signal in transmission process by the effect of channel, then receives the implicit fine feature that can characterize channel information of signal.In the present invention, signal is emitted from recovery in signal is received using blind equalization algorithm, and peak extraction, as the fine feature of channel, description and differentiation for channel is carried out to blind equalization weight vector.Pass through emulation experiment, the feasibility of 1 authenticated channel fine feature extracting method of embodiment.

Description

A kind of extracting method of channel fine feature
Technical field
The invention belongs to signal sorting technical fields, particularly relate to a kind of extracting method of channel fine feature.
Background technique
In mobile communications, the electromagnetic path that signal is propagated is referred to as wireless channel.Wireless channel and the environment of surrounding are close Cut phase is closed, and the wireless channel under varying environment has the feature of some differentiation.How to find and extracts the letter in wireless channel Number correlated characteristic has great researching value, and a current research hotspot.Common radio channel characteristic parameter has Multidiameter delay, Doppler frequency shift etc..Currently, the feature extracting method of wireless channel mainly has three classes：Power estimation is based on parameter The estimation method in space, deterministic parameter Estimation.
Power estimation, it can be common that multiple signal classification method (Multiple Signal Classification, MUSIC). It can provide progressive nothing to incidence wave wavefront number, arrival direction or the direction of the launch, the intensity of incident waveform and cross-correlation Estimation partially.But the calculation amount and amount of storage that the algorithm searches for parameter space are larger, and when incoming signal is coherent signal, MUSIC algorithm is invalid.
The Parameter Subspace estimation technique, the representative of this kind of algorithm are rotation invariant technology estimation signal parameter algorithms (Estimating Signal Parameters via Rotational Invariance Techniques, ESPRIT).It Using the invariable rotary characteristic of signal subspace, the accurate parameter that can be used for horizontal angle is extracted.ESPRIT avoids space Search process, calculation amount is few, but requiring the correspondence array element of subarray must be identical dipole pair.
Deterministic parameter Estimation, such as desired maximum calculated method (Expectation Maximization, EM), which is It is generated based on maximum- likelihood estimation (Maximum Likelihood Estimation, MLE).It may be implemented pair The Combined estimator of time delay, horizontal angle, amplitude.The popularization of EM algorithm is space-alternating generalized expectation-maximization algorithm (Space- Alternating Generalized Expectation Maximization, SAGE).SAGE algorithm can simultaneously clock synchronization The multidigits parameter such as prolong, leave angle, angle of arrival, Doppler frequency shift, amplitude and carries out Combined estimator.
Channel is the necessary link between transmitting terminal and receiving end, emits signal in transmission process by channel background environment Effect, then it includes channel information that receiving end, which receives signal,.When channel background environment has differences, this species diversity is receiving signal In also embodied.Therefore, the fine feature for being able to reflect channel information can be extracted in signal from receiving.If x (t) is transmitting letter Number, h (t) is equivalent base band impulse response, that is, contain transmitting terminal, channel and the radio frequency of receiving end, intermediate-frequency section it is total Transmission characteristic is represented by then receiving signal：
Wherein,Represent convolution.
By the balanced thinking for restoring x (t), the information of h (t) not only can be obtained, but also can avoid directly asking using from y (t) Take h (t).If the impulse response of balanced device is ω (t), the output of balanced device is：
G (t) is total equivalent impulse response of transmitting terminal, channel, the radio frequency of receiving end, intermediate-frequency section and balanced device in formula.
The idea output (i.e. desired output) of balanced device is transmitting signal x (t), must be expired to make z (t)=x (t) then Foot：
The purpose of balanced device is exactly to realize above formula, and frequency-domain expression is：
H (f) Ω (f)=1
Above formula shows that balanced device is actually the inverse filter of transmission channel.Accordingly, it is considered to balanced device weight vector table The information in reference road.
Summary of the invention
The present invention extracts problem for channel characteristics, provides a kind of extracting method of channel fine feature, can be from reception Channel information is extracted in signal.Compared to existing channel characteristics extracting method, this method calculation amount is small, realizes simple.
For achieving the above object, the extraction process of channel fine feature of the present invention, includes the following steps：
S1：It is if receiving signalN is data length, is normalized, and μ is the mean value for receiving signal, and σ is to connect The standard deviation of the collection of letters number, normalization formula are：
S2：Iteration step length μ and blind equalization weight vector f is seti(k) initial value.
S3：Blind equalization is carried out to data using MCMA algorithm is improved, is generated error signal e (k), formula is：
E (k)=eR(k)+jeI(k)
Wherein,
S4：Blind equalization weight vector the number of iterations takes k=N, and iteration more new formula is：
fi(k+1)=fi(k)+μe(k)x(k-i)
Wherein, e (k) is error signal, and μ is iteration step length, and i is tap number, and k is the number of iterations.
S5：Using blind equalization tap coefficient excess mean-square error rmskAs the evaluation index of equalization performance, formula is：
Wherein, fk,iFor blind equalization weight vector fi(k) i-th of element.
S6：To blind equalization weight vector fi(k) modulus value extracts local maximum, and all local maximums are formed peak set
Wherein | | F | | indicate the number of the included element of set F.For sequential peak point, meet：The neighbor distance sequence of definition of order peak point：
S7：Based on peak set F, following fine feature is extracted：
Peak-to-average force ratio：Fa=max (F)/Fharmmean, wherein
Compare in peak：Fb=max (F)/median (F)
Peak point distribution consistency degree：Wherein | | F | | indicate peak value number.
Beneficial effects of the present invention are that it is poor that channel background environment is utilized in the extracting method of channel fine feature of the present invention It is different to receive the embodiment in signal；In fact, in the present invention, transmitting signal passes through the effect of different channels, receives signal and deposit In difference.Therefore, the fine feature for characterizing channel information can be used for the description and differentiation of channel.
Detailed description of the invention
Fig. 1 is simulation block diagram of the present invention；
Fig. 2 is the flow chart of realization process of the present invention；
Fig. 3 is different reception signal blind equalization weight vectors in the embodiment of the present invention 1.
Specific embodiment
A specific embodiment of the invention is described with reference to the accompanying drawing, preferably so as to those skilled in the art Understand the present invention.Requiring particular attention is that in the following description, when known function and the detailed description of design perhaps When can desalinate main contents of the invention, these descriptions will be ignored herein.
With the feasibility of emulation experiment data verification channel fine feature extracting method, simulation block diagram is as shown in Figure 1.
Experimental Hardware platform includes that a processor is Pentium (R) Dual-Core 3.2G, the interior desk-top meter for saving as 6G Calculation machine, software platform are WIN7 operating system, Matlab2015b.
Fig. 2 is execution step of the present invention when application is extracted with channel fine feature.As shown in Fig. 2, the subtle spy of channel The extracting method of sign includes the following steps：
S1：It is if receiving signalN is data length, is normalized, and μ is the mean value for receiving signal, and σ is to connect The standard deviation of the collection of letters number, normalization formula are：
S2：Iteration step length μ=0.001 and blind equalization weight vector f are seti(k) initial value centre cap is 1, remaining is complete It is 0.S3：Blind equalization is carried out to data using MCMA algorithm is improved, is generated error signal e (k), formula is：
E (k)=eR(k)+jeI(k)
Wherein,
S4：Blind equalization weight vector the number of iterations takes k=N, and iteration more new formula is：
fi(k+1)=fi(k)+μe(k)x(k-i)
Wherein, e (k) is error signal, and μ is iteration step length, and i is tap number, and k is the number of iterations.
S5：Using blind equalization tap coefficient excess mean-square error rmskAs the evaluation index of equalization performance, formula is：
Wherein, fk,iFor blind equalization weight vector fi(k) i-th of element.
S6：To blind equalization weight vector fi(k) modulus value extracts local maximum, and all local maximums are formed peak set
Wherein | | F | | indicate the number of the included element of set F.For sequential peak point, meet：The neighbor distance sequence of definition of order peak point：
S7：Based on peak set F, following fine feature is extracted：
Peak-to-average force ratio：Fa=max (F)/Fharmmean, wherein
Compare in peak：Fb=max (F)/median (F)
Peak point distribution consistency degree：Wherein | | F | | indicate peak value number.
Embodiment 1
The present embodiment is with the feasibility of emulation experiment data verification channel fine feature extracting method.Signal is sent to use QPSK modulation system, 100000 points of data length.In emulation, channel is emulation Rayleigh channel, and h1, h2, h3 order are different.Emulation Parameter setting is as shown in table 1：
Simulation parameter facilities in 1 embodiment 1 of table
As shown in Figure 3.It is improved that h1, h2, h3 respectively correspond three channels, 100000 points of normalization reception data in table 1 The modulus value of the last one weight vector obtained by the iterative processing of MCMA algorithm blind equalization, and h1, h2, h3 are the equilibrium of 44 rank balanced devices As a result.As seen from Figure 3, the blind equalization result that different channels receive signal has differences.
As shown in table 2：
The fine feature of different channels extracts result in 2 embodiment 1 of table
Fine feature Fa Fb Fp
h1 11.6350 7.6917 16.7225
h2 3.7839 2.5320 19.0195
h3 4.8850 3.5450 17.8421
Tri- channels of h1, h2, h3 are compared, in peak-to-average force ratio Fa, compare F in peakb, peak point distribution consistency degree FpThree subtle spies Sign numerically has differences.So channel is different, the fine feature of channel also can be variant.Therefore, fine feature：Peak is equal Compare Fa, compare F in peakb, peak point distribution consistency degree Fp, can be used for the description and differentiation of different channels.

Claims (1)

1. a kind of extracting method of channel fine feature, which is characterized in that include the following steps：
S1, set receive signal asN is data length, is normalized：
Wherein, μ is the mean value for receiving signal, and σ is the standard deviation for receiving signal；
S2, setting iteration step length μ and blind equalization weight vector fi(k) initial value；
S3, blind equalization is carried out to data using improvement MCMA algorithm, generated error signal e (k)：
E (k)=eR(k)+jeI(k)
Wherein,
S4, blind equalization weight vector the number of iterations take k=N, and iteration more new formula is：
fi(k+1)=fi(k)+μe(k)x(k-i)
Wherein, e (k) is error signal, and i is tap number, and k is the number of iterations；
S5：Using blind equalization tap coefficient excess mean-square error rmskEvaluation index as equalization performance：
Wherein, fk,iFor blind equalization weight vector fi(k) i-th of element；
S6：To blind equalization weight vector fi(k) modulus value extracts local maximum, and all local maximums are formed peak set：
Wherein | | F | | indicate the number of the included element of set F,For sequential peak point, meet：The neighbor distance sequence of definition of order peak point：
S7, it is based on peak set F, extracts following fine feature：
Peak-to-average force ratio：Fa=max (F)/Fharmmean, wherein
Compare in peak：Fb=max (F)/median (F)；
Peak point distribution consistency degree：Wherein | | F | | indicate peak value number.
CN201810658989.3A 2018-06-25 2018-06-25 A kind of extracting method of channel fine feature Pending CN108848044A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810658989.3A CN108848044A (en) 2018-06-25 2018-06-25 A kind of extracting method of channel fine feature

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810658989.3A CN108848044A (en) 2018-06-25 2018-06-25 A kind of extracting method of channel fine feature

Publications (1)

Publication Number Publication Date
CN108848044A true CN108848044A (en) 2018-11-20

Family

ID=64203215

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810658989.3A Pending CN108848044A (en) 2018-06-25 2018-06-25 A kind of extracting method of channel fine feature

Country Status (1)

Country Link
CN (1) CN108848044A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20220078050A1 (en) * 2018-12-17 2022-03-10 U-Blox Ag Estimating one or more characteristics of a communications channel

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1585318A (en) * 2003-08-20 2005-02-23 大唐移动通信设备有限公司 Space characteristic extracting and signal reaching direction estimating method for CDMA wireless telecommunication
CN101533642A (en) * 2009-02-25 2009-09-16 北京中星微电子有限公司 Method for processing voice signal and device
WO2011063471A1 (en) * 2009-11-27 2011-06-03 Cohda Wireless Pty Ltd Extracting parameters from a communications channel

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1585318A (en) * 2003-08-20 2005-02-23 大唐移动通信设备有限公司 Space characteristic extracting and signal reaching direction estimating method for CDMA wireless telecommunication
CN101533642A (en) * 2009-02-25 2009-09-16 北京中星微电子有限公司 Method for processing voice signal and device
WO2011063471A1 (en) * 2009-11-27 2011-06-03 Cohda Wireless Pty Ltd Extracting parameters from a communications channel

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
KIL YAM OH等: "MODIFIED CONSTANT MODULUS ALGORITHM: BLIND EQUALIZATION AND CARRIER PHASE RECOVERY ALGORITHM", 《PROCEEDINGS IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS ICC "95》 *
陈勇等: "无线通信信道中的指纹特征提取与建模", 《无线通信技术》 *
黄焱等: "基于星座软信息的猝发信号盲均衡算法", 《电子与信息学报》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20220078050A1 (en) * 2018-12-17 2022-03-10 U-Blox Ag Estimating one or more characteristics of a communications channel
US11601307B2 (en) * 2018-12-17 2023-03-07 U-Blox Ag Estimating one or more characteristics of a communications channel

Similar Documents

Publication Publication Date Title
Wei et al. An AMP-based network with deep residual learning for mmWave beamspace channel estimation
Van Der Veen et al. Joint angle and delay estimation using shift-invariance properties
Wang et al. Far field EM side-channel attack on AES using deep learning
Peken et al. Blind channel estimation for massive MIMO
Wang et al. A MIMO-OFDM channel estimation approach using time of arrivals
Yang et al. Channel estimation for practical IRS-assisted OFDM systems
Chen et al. Joint AoD, AoA, and channel estimation for MIMO-OFDM systems
Kosasih et al. Graph neural network aided MU-MIMO detectors
Ritcher et al. Maximum likelihood channel parameter estimation from multidimensional channel sounding measurements
Park et al. Sparse covariance‐based high resolution time delay estimation for spread spectrum signals
Hao et al. Importance sampling based direct maximum likelihood position determination of multiple emitters using finite measurements
Xie et al. Multi-task learning-based channel estimation for RIS assisted multi-user communication systems
Fan et al. MuSpel-Fi: Multipath subspace projection and ELM-based fingerprint localization
He et al. Joint space-time parameter estimation for underwater communication channels with velocity vector sensor arrays
Guo et al. Robust deep learning for uplink channel estimation in cellular network under inter-cell interference
CN108848044A (en) A kind of extracting method of channel fine feature
Vieeralingaam et al. Parametric study of rf energy harvesting in swipt enabled wireless networks under downlink scenario
Fesl et al. Diffusion-based Generative Prior for Low-Complexity MIMO Channel Estimation
Mota et al. Estimation of the radio channel parameters using the SAGE algorithm
Li et al. Fast Estimation Method of Space‐Time Two‐Dimensional Positioning Parameters Based on Hadamard Product
Lian et al. Configuration optimization and channel estimation in hybrid beamforming mmWave systems with channel support side information
CN113422627B (en) Hybrid precoding method and device based on adaptive gradient back propagation
Chen et al. Joint AoA and channel estimation for SIMO-OFDM systems: A compressive-sensing approach
Ba et al. Maximum likelihood time delay estimation based on Monte Carlo importance sampling in multipath environment
Lu et al. Spatial characteristics of the massive MIMO channel based on indoor measurement at 1.4725 GHz

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20181120