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EP1166512A1 - Verfahren zur entzerrung mittels adaptivem filter - Google Patents

Verfahren zur entzerrung mittels adaptivem filter

Info

Publication number
EP1166512A1
EP1166512A1 EP00918993A EP00918993A EP1166512A1 EP 1166512 A1 EP1166512 A1 EP 1166512A1 EP 00918993 A EP00918993 A EP 00918993A EP 00918993 A EP00918993 A EP 00918993A EP 1166512 A1 EP1166512 A1 EP 1166512A1
Authority
EP
European Patent Office
Prior art keywords
filter
coefficients
channel
equaliser
training
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.)
Withdrawn
Application number
EP00918993A
Other languages
English (en)
French (fr)
Inventor
David Roger Bull
Andrew Robert Nix
Ismail Karadeniz Technical University KAYA
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 Bristol
Original Assignee
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
Publication of EP1166512A1 publication Critical patent/EP1166512A1/de
Withdrawn legal-status Critical Current

Links

Classifications

    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04BTRANSMISSION
    • H04B3/00Line transmission systems
    • H04B3/02Details
    • H04B3/04Control of transmission; Equalising
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00Baseband systems
    • H04L25/02Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/03Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03006Arrangements for removing intersymbol interference
    • H04L25/03012Arrangements for removing intersymbol interference operating in the time domain
    • H04L25/03019Arrangements for removing intersymbol interference operating in the time domain adaptive, i.e. capable of adjustment during data reception
    • H04L25/03031Arrangements for removing intersymbol interference operating in the time domain adaptive, i.e. capable of adjustment during data reception using only passive components
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00Baseband systems
    • H04L25/02Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/03Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
    • H04L25/03006Arrangements for removing intersymbol interference
    • H04L25/03012Arrangements for removing intersymbol interference operating in the time domain
    • H04L25/03019Arrangements for removing intersymbol interference operating in the time domain adaptive, i.e. capable of adjustment during data reception
    • H04L25/03038Arrangements 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)
  • Cable Transmission Systems, Equalization Of Radio And Reduction Of Echo (AREA)
  • Digital Transmission Methods That Use Modulated Carrier Waves (AREA)
EP00918993A 1999-03-30 2000-03-30 Verfahren zur entzerrung mittels adaptivem filter Withdrawn EP1166512A1 (de)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
GB9907354 1999-03-30
GBGB9907354.6A GB9907354D0 (en) 1999-03-30 1999-03-30 Adaptive filter equalisation techniques
PCT/GB2000/001214 WO2000059168A1 (en) 1999-03-30 2000-03-30 Adaptive filter equalisation techniques

Publications (1)

Publication Number Publication Date
EP1166512A1 true EP1166512A1 (de) 2002-01-02

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JP (1) JP2002540728A (de)
AU (1) AU3975700A (de)
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US7606293B2 (en) * 2002-10-25 2009-10-20 Gct Semiconductor, Inc. Bidirectional turbo ISI canceller-based DSSS receiver for high-speed wireless LAN
JP4457657B2 (ja) 2003-12-10 2010-04-28 日本電気株式会社 等化器
CN101502068B (zh) * 2006-08-07 2012-05-30 Sk电信有限公司 码片均衡器和均衡方法
WO2008041612A1 (fr) * 2006-09-29 2008-04-10 Panasonic Corporation Dispositif d'égalisation de formes d'onde
CN101490972A (zh) * 2006-11-22 2009-07-22 松下电器产业株式会社 波形均衡装置
KR20170133550A (ko) 2016-05-25 2017-12-06 삼성전자주식회사 수신기 및 그의 검파 방법
CN107508618B (zh) * 2017-08-29 2021-03-19 苏州裕太微电子有限公司 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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DK168750B1 (da) * 1990-05-01 1994-05-30 Dancall Telecom As Fremgangsmåde til modforvrængning i en modtager af signaler, der har passeret en transmissionskanal
US5732112A (en) * 1995-12-28 1998-03-24 Globespan Technologies, Inc. Channel training of multi-channel receiver system
US6069917A (en) * 1997-05-23 2000-05-30 Lucent Technologies Inc. Blind training of a decision feedback equalizer

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WO2000059168A1 (en) 2000-10-05
AU3975700A (en) 2000-10-16
JP2002540728A (ja) 2002-11-26
GB9907354D0 (en) 1999-05-26

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