WO2000059168A1 - Techniques d'egalisation adaptative de filtres - Google Patents

Techniques d'egalisation adaptative de filtres Download PDF

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Publication number
WO2000059168A1
WO2000059168A1 PCT/GB2000/001214 GB0001214W WO0059168A1 WO 2000059168 A1 WO2000059168 A1 WO 2000059168A1 GB 0001214 W GB0001214 W GB 0001214W WO 0059168 A1 WO0059168 A1 WO 0059168A1
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WO
WIPO (PCT)
Prior art keywords
filter
coefficients
channel
equaliser
training
Prior art date
Application number
PCT/GB2000/001214
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English (en)
Inventor
David Roger Bull
Andrew Robert Nix
Ismail Kaya
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University Of Bristol
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 Bristol filed Critical University Of Bristol
Priority to JP2000608555A priority Critical patent/JP2002540728A/ja
Priority to EP00918993A priority patent/EP1166512A1/fr
Priority to AU39757/00A priority patent/AU3975700A/en
Publication of WO2000059168A1 publication Critical patent/WO2000059168A1/fr

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B3/00—Line transmission systems
    • H04B3/02—Details
    • H04B3/04—Control of transmission; Equalising
    • 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
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03006—Arrangements for removing intersymbol interference
    • H04L25/03012—Arrangements for removing intersymbol interference operating in the time domain
    • H04L25/03019—Arrangements for removing intersymbol interference operating in the time domain adaptive, i.e. capable of adjustment during data reception
    • H04L25/03031—Arrangements for removing intersymbol interference operating in the time domain adaptive, i.e. capable of adjustment during data reception using only passive components
    • 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
    • H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03006—Arrangements for removing intersymbol interference
    • H04L25/03012—Arrangements for removing intersymbol interference operating in the time domain
    • H04L25/03019—Arrangements for removing intersymbol interference operating in the time domain adaptive, i.e. capable of adjustment during data reception
    • H04L25/03038—Arrangements for removing intersymbol interference operating in the time domain adaptive, i.e. capable of adjustment during data reception with a non-recursive structure

Definitions

  • the present invention relates to adaptive filter equalisation techniques, and in particular to initialisation of coefficients of equalisation filters.
  • ISI inter symbol interference
  • the base station 1 transmits an RF signal to a mobile station 2. If the communication is over a direct path, 3, there is no ISI.
  • reflections occur, from buildings etc. (illustrated by the reflective paths 4 in the figure), causing multiple signals to be received by the receiver. Since direct and reflected path lengths are different, a signal representing a single transmitted symbol (a single bit of data) can arrive at different times. This results in a spreading of the signal for that symbol, referred to as inter-symbol-interference (ISI), which potentially causes difficulty in detecting the symbol.
  • ISI inter-symbol-interference
  • Figure 2 illustrates an equivalent effect in a wired or fixed communication system where two nodes 5 and 6 are linked. If an intermediate node 7 exists (which connects the path to another node 8) multiple paths 10 and 11 can result.
  • An example of this is communication through cable networks that involve the mains local area network (M-LAN), which can be used in a home-based application.
  • M-LAN mains local area network
  • the mains power cables are used to implement a local network in a frequency-isolated area, for example a home or office department.
  • the observed multipath propagation is similar to that shown figure 2, but the interference level could be much higher for high-speed data communications.
  • This type of communication system is new, but has a very wide application area.
  • Multipath propagation or time dispersion can also occur, as illustrated in figure 3, in an optical cable 12 with reflection paths 14 causing inter-symbol-interference on the centre trace beam 13.
  • the multipath channel can be modelled by a tapped delay line filter, as shown in figure 4, where T s is the symbol period.
  • the resulting signal v t is made up of the combination of the input symbols x k , x k _ ⁇ k _ 2 ,--- ⁇ k _ L multiplied by filter coefficients A Q , A,,— h, and a noise component ⁇ k , as shown in Eq. 1 where L + 1 is the number of taps in the channel model (or the symbol storage capacity of the channel).
  • the power delay profile of the channel is shown in figure 5. It can be seen that the channel signal can be modelled as a series of time spaced samples. Since the received signal is spread over a number of symbol periods, then symbols can interfere with one another, giving the phenomenon known as inter-symbol- interference (ISI). This results in the received signals badly defining the original transmitted symbol stream. In the past, various methods have been employed to remove this ISI.
  • ISI inter-symbol- interference
  • a channel matched filter such as that shown in figure 6, can be used to convert the channel signals into a waveform, such as that shown in figure 7.
  • the channel- matched filter CMF receives the channel waveform from the channel model, and combines the received signals with the complex conjugates of the tapped delay line filter model coefficients. The output of the CMF is calculated as,
  • the output ISI profile of the CMF is calculated according to the following equation:
  • the profile ( Figure 7)from the CMF has the advantage of being symmetrical and exhibiting a large real centre tap. It has been shown in several publications that the CMF filter provides the optimum symbol-synchronisation point and multipath diversity at the centre tap, ⁇ .
  • the output of the CMF can then be fed as an input to a decision feedback equaliser (DFE) filter to remove side lobes calculated by equations (3) and (5).
  • DFE decision feedback equaliser
  • the method which is called CMF- DFE, calculates the equaliser coefficients directly from the channel profile and produces the best performance for an equaliser filter.
  • the required extra unit for running the CMF filter the feedback data gain adjustment problem and, more importantly the need for a DSP to execute the required Teoplitz Matrix inversion, make the method too expensive and complex for most low-cost, low- power applications.
  • MMSE-DFE minimum mean square decision feedback equaliser
  • the MMSE-DFE does not require the CMF filter but suffers from a feedback data gain adjustment problem.
  • the method has a higher bit-error-rate and greater complexity than the CMF-DFE.
  • the training method is not adaptive and the training is not suitable for a hardware filter implementation due to the difficulty of matrix inversion.
  • the least mean square (LMS) algorithm is preferred for most applications that require simple implementation and is also more stable than any other technique.
  • the LMS algorithm suffers when the channel has a null point close to the centre of the unit circle in the z-domain. This results in a slow convergence speed and thus a long training sequence is required.
  • the training step size is large then the LMS training becomes unstable. If the step size is small then the algorithm may not converge completely by the end of the training sequence.
  • the initialisation technique presented here is explained assuming the LMS training algorithm, the initialisation process is valid for all training algorithms.
  • the accuracy of the initialisation may well allow the feedback filter (FBF) coefficients to be left out of the training process for some applications, with training restricted to the feedforward filter (FFF) coefficients.
  • FFF feedforward filter
  • Figure 8 shows a decision feedback equaliser incorporating a feedforward filter (FFF) (16) and a feedback filter (FBF) (17).
  • FFF feedforward filter
  • BPF feedback filter
  • ff + ⁇ is the number of feedforward coefficients (18)
  • L p is the number of feedback filter coefficients (19).
  • the variables x k (24) and x k (25) represent the estimated data of the DFE and the detected data from the estimated data respectively
  • the feedforward filter (16) comprises a delay line (15), from which signals are tapped, the tapped signals represent the figure 4 samples.
  • the feedback filter (17) also comprises a delay line (20), which has as its input the output of the equaliser x k (25) or the reference fraining symbols x k (21). This output is the estimate from the filter of the symbol concerned.
  • the feedback filter also has a number of taps, which represent the precursor samples.
  • the tapped signals from both the feedforward and feedback filters are scaled by respective coefficients c L to c 0 (18) and c, to c L (19), and are then added together to provide the output (24).
  • the expression for the DFE operation is given by
  • the output of the equaliser is then detected to determine its level (25).
  • the coefficients of the filter can be adjusted so that the output of the equaliser is the expected value.
  • the error, ⁇ k is calculated for use in the training algorithm as
  • the coefficients can be obtained in various manners using so-called adaptive training techniques. These adaptive training techniques use a known training sequence of symbols, which allow the receiver to compare the detected symbol sequence with the expected sequence, as shown in Eq.(8). The equaliser coefficients can then be adjusted until the detected symbols have an error rate within the required tolerance (or until the end of the training sequence is reached).
  • the coefficients can be calculated in several ways.
  • the various methods can be grouped into linear (such as least mean squares) and non- linear (such as recursive least squares RLS) techniques.
  • the least mean squares (LMS) algorithm is the simplest method for equaliser training.
  • LMS least mean squares
  • the recursive least squares algorithm provides very high performance but is complex to implement It is also possible to directly calculate the equaliser coefficients. Examples of direct calculation methods include the min-n-mm mean square error and channel matched filter equaliser.
  • a method of estimating coefficients of an equaliser filter which receives an input channel signal, the method including initialising preselected coefficients of the equaliser filter in accordance with a calculated channel matched filter model, and estimating the coefficients of the filter using an estimation technique and a known input channel signal to the equaliser.
  • FIGS. 1 and 3 illustrate multi-path signal transmission in communication systems
  • Figure 4 is a block diagram illustrating a model of the multi-path signal transmission channel of figures 1, 2 and 3;
  • FIG 5 illustrates the power delay profile of the channel and sampling points for the channel coefficients modelled in the figure 4
  • Figure 6 illustrates a channel matched filter (CMF)
  • FIG 7 illustrates the output power delay profile or ISI profile of the CMF
  • Figure 8 illustrates the decision feedback equaliser (DFE)
  • Figures 9 and 10 illustrate relative performance characteristics of known equalisation techniques and an equalisation technique incorporating a method embodying the present invention.
  • the present invention is concerned with the calculation of the coefficient values in the feedforward and feedback filters of the decision feedback equaliser such that the approximation of the coefficients of the equaliser can be made more efficient.
  • the method can also be applied to the FFF in a Linear Transversal Equaliser (LTE).
  • the technique used for actually estimating the coefficients is the least means squares algorithm, however any appropriate estimation algorithm can be used with the initialisation method embodied in the present invention.
  • the decision feedback equaliser is typically trained using the training data at the start of a communications packet.
  • the training data is known at the receiver allowing the actual detected symbol stream to be compared with the required symbol stream.
  • the equaliser can then adapt to the particular communication path concerned.
  • the strongest path (A, illustrated in figure 4) of the channel is chosen for the symbol synchronisation.
  • Pre-cursor path (only AQ in fig. 4) symbols are cancelled by the feedforward filter (FFF) and post-cursor path (/i 2 ,A 3 ,A 4 ) symbols are cancelled by the feedback filter (FBF).
  • the FFF is an anti-causal, finite impulse response (FIR) filter.
  • FIR finite impulse response
  • a filter size equal to or bigger than the length of the channel model is chosen.
  • the FFF works in order to combine the power delay profile in its targeted window.
  • the FBF is causal and normally implemented using L-1 taps, it cancels the received signal energy for it's targeted window. Therefore, energy in the vicinity of the synchronisation symbol should be reserved in the feedforward filter ISI cancellation window to obtain more multipath diversity.
  • the least delayed transmit symbol (x k ) at the centre tap data (v k in figure 8) should be used for symbol synchronisation. All received power for symbol x k is represented in the feedforward filter and other interfering symbols are shared by the feedforward and feedback filters.
  • Figure 8 is arranged to obtain the symbol synchronisation at symbol x k , which is received through the first tap of the channel h as it appears in the centre-tap data v k . Then, subsequent interference symbols x k+l to x k+L +L _ X and previous interference symbols x k _ ⁇ to x k _ L are targeted as interference components by the FFF and FBF respectively. Since the FBF is causal, there is no need to employ more than L-1 taps.
  • the CMF extends the interference profile as shown in Figure 7 and presents all the multipath energy for the desired symbol at the centre ISI component d .
  • the above initialisation secures all the received signal energy about the target symbol x k in the feedforward filter. This is a unique starting point for a linear transversal equaliser (LTE) and is not dependent on any particular adaptive training algorithm.
  • LTE linear transversal equaliser
  • the feedback coefficients can be initialised according to the ISI profile of the CMF.
  • the FBF works as a substructure of the target interference component and the previous symbols ISI components should be subtracted from the estimate of the DFE by the FBF. According to this phenomena, the FBF coefficients would be given as shown in equation (11)
  • the training would start with a proper ISI cancellation on the previous symbols side of the cursor symbol and the rem-tining ISI components would be on the left side (subsequent symbols) of the CMFs' power delay profile as shown in Figure 7.
  • the FBF coefficients can keep these values and the FFF coefficients are forced to implement the ISI cancellation according to this pre-determined FBF setting.
  • including the FBF in the training process does increase performance since updated FFF filter coefficients require a different set of FBF filter coefficients. Simulations have shown that the FBF coefficients do not significantly change, because the error function does not have a large effect on the FBF since the FBF operation is initially correct.
  • An adaptive training algorithm with a short training sequence is more desirable than one with a long training sequence and non-adaptive equalisation.
  • the presented invention reduces the required training sequence length dramatically, since the initialised coefficients are close to the final values achieved after the iterative training.
  • LMS algorithm which is a member of the stochastic gradient-based algorithms, acting as an example. It uses an estimate of the gradient of the error function and as such does not require a measurement of the pertinent correlation functions (nor does it require matrix inversion).
  • the LMS algorithm is simple and robust and performs well under a wide range of channel conditions and input signal powers.
  • a training sequence of data bits is supplied to the receiver. In a typical LMS system this can be anything up to 800 bits of information. Since the receiver knows the training sequence, the output from the DFE can be compared with the expected output and the coefficients adjusted accordingly.
  • the update equation for the ordinary LMS algorithm is given as
  • step sizes for the FFF and FBF respectively A ff and ⁇ ft differ from the original update equation.
  • the same DFE filter is used.
  • Simulation studies have shown that the FFF step size ⁇ can be increased to 0.2 without causing any instability.
  • the feedback filter step size A ⁇ should be within the range 0 to 0.1, a typical value is 0.01.
  • the FBF step size A ⁇ is equal to 0 it means the FBF filter is removed from the training process. This does not cause any major performance degradation, in the Supervised LMS, and the error performance is still better than the ordinary LMS algorithm, as shown in the performance curves of figures 9 and 10.
  • the bigger step size for the FFF, of 0.2 dramatically reduces the required training iterations to 100-150 for a reasonable low mean square error value as shown in Figure 9.
  • Figure 10 show the bit-error-rate analysis of the LMS technique with the proposed Supervised LMS (SLMS) initialisation.
  • SLMS Supervised LMS
  • the speed improvement would be expected to be similar to the LMS, i.e. three times faster than normal.
  • the performance improvement in terms of bit-error-rate, should also be observed.
  • ignoring the training of the FBF would dramatically reduce the complexity and speed problems of the RLS algorithm thus allowing it to be implemented with reasonable processing power.
  • embodiments of the present invention can present a low cost, high speed, high performance technique for equalisation.
  • the equations (4) and (5) can be executed by the same autocorrelation filter used for bit synchronisation and the division operation in equation (14) is a data normalisation process that can be implemented by data shifting operations. Therefore the initialisation method is suitable for either DSP or hardware filter implementations.

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Power Engineering (AREA)
  • Filters That Use Time-Delay Elements (AREA)
  • Digital Transmission Methods That Use Modulated Carrier Waves (AREA)
  • Cable Transmission Systems, Equalization Of Radio And Reduction Of Echo (AREA)

Abstract

Des canaux de communication multivoies peuvent présenter un brouillage intersymboles nécessitant l'emploi de techniques d'égalisation pour les éliminer. Un procédé d'estimation des coefficients d'un filtre égaliseur recevant un signal de canal d'entrée consiste à initialiser les coefficients présélectionnés du filtre égaliseur selon un modèle calculé de filtre adapté aux canaux, et à estimer les coefficients de filtre à l'aide d'une technique d'estimation et d'un signal de canaux d'entrée connu dans l'égaliseur. L'initialisation des coefficients de cette façon permet de réduire la durée d'apprentissage du filtre.
PCT/GB2000/001214 1999-03-30 2000-03-30 Techniques d'egalisation adaptative de filtres WO2000059168A1 (fr)

Priority Applications (3)

Application Number Priority Date Filing Date Title
JP2000608555A JP2002540728A (ja) 1999-03-30 2000-03-30 適応フィルタ等化技術
EP00918993A EP1166512A1 (fr) 1999-03-30 2000-03-30 Techniques d'egalisation adaptative de filtres
AU39757/00A AU3975700A (en) 1999-03-30 2000-03-30 Adaptive filter equalisation techniques

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GB9907354.6 1999-03-30
GBGB9907354.6A GB9907354D0 (en) 1999-03-30 1999-03-30 Adaptive filter equalisation techniques

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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1542416A2 (fr) 2003-12-10 2005-06-15 NEC Corporation Egalisateur et procédé de mise d'une valeur initiale pour l'égalisateur
KR101068639B1 (ko) * 2002-10-25 2011-09-28 지씨티 세미컨덕터 인코포레이티드 고속 무선 랜을 위한 양방향 터보 ｉｓｉ 소거기 기반의ｄｓｓｓ 수신기
US8165192B2 (en) 2006-11-22 2012-04-24 Panasonic Corporation Waveform equalizer
EP2050241A4 (fr) * 2006-08-07 2017-09-13 SK Telecom Co., Ltd. Égaliseur au niveau chip et procédé d'égalisation
CN107508618A (zh) * 2017-08-29 2017-12-22 苏州裕太车通电子科技有限公司 一种基于有线通信的抗信号衰减的方法及通信设备
US10218428B2 (en) 2016-05-25 2019-02-26 Samsung Electronics Co., Ltd. Receiver and detecting method thereof

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090070396A1 (en) * 2006-09-29 2009-03-12 Haruka Takano Waveform equalizing device

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1991017607A1 (fr) * 1990-05-01 1991-11-14 Dancall Radio A/S Procede d'egalisation dans un recepteur de signaux ayant passe un canal de transmission
US5732112A (en) * 1995-12-28 1998-03-24 Globespan Technologies, Inc. Channel training of multi-channel receiver system
EP0880253A2 (fr) * 1997-05-23 1998-11-25 Lucent Technologies Inc. Adaptation aveugle d'un égaliseur à décision récursive

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1991017607A1 (fr) * 1990-05-01 1991-11-14 Dancall Radio A/S Procede d'egalisation dans un recepteur de signaux ayant passe un canal de transmission
US5732112A (en) * 1995-12-28 1998-03-24 Globespan Technologies, Inc. Channel training of multi-channel receiver system
EP0880253A2 (fr) * 1997-05-23 1998-11-25 Lucent Technologies Inc. Adaptation aveugle d'un égaliseur à décision récursive

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
KAYA I ET AL: "Exploiting multipath activity using low complexity equalisation techniques for high speed wireless LANs", VTC '98. 48TH IEEE VEHICULAR TECHNOLOGY CONFERENCE.18-21 MAY 1998, 1998, New York, NY, USA, IEEE, USA, pages 1593 - 1597 vol.2, XP002141419, ISBN: 0-7803-4320-4 *

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101068639B1 (ko) * 2002-10-25 2011-09-28 지씨티 세미컨덕터 인코포레이티드 고속 무선 랜을 위한 양방향 터보 ｉｓｉ 소거기 기반의ｄｓｓｓ 수신기
EP1542416A2 (fr) 2003-12-10 2005-06-15 NEC Corporation Egalisateur et procédé de mise d'une valeur initiale pour l'égalisateur
EP1542416A3 (fr) * 2003-12-10 2006-09-27 NEC Corporation Egalisateur et procédé de mise d'une valeur initiale pour l'égalisateur
US7406122B2 (en) 2003-12-10 2008-07-29 Nec Corporation Equalizer and method of setting initial value for same
EP2050241A4 (fr) * 2006-08-07 2017-09-13 SK Telecom Co., Ltd. Égaliseur au niveau chip et procédé d'égalisation
US8165192B2 (en) 2006-11-22 2012-04-24 Panasonic Corporation Waveform equalizer
US10218428B2 (en) 2016-05-25 2019-02-26 Samsung Electronics Co., Ltd. Receiver and detecting method thereof
CN107508618A (zh) * 2017-08-29 2017-12-22 苏州裕太车通电子科技有限公司 一种基于有线通信的抗信号衰减的方法及通信设备

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GB9907354D0 (en) 1999-05-26
JP2002540728A (ja) 2002-11-26
EP1166512A1 (fr) 2002-01-02
AU3975700A (en) 2000-10-16

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