NEXT & FEXT Simulation Parameters.
1. Introduction
The reliable delivery of information over severe fading wireless or wired channels is a major challenge in communication systems. At the heart of every communication system is the physical layer, consisting of a transmitter, a channel and a receiver. A transmitter maps the input digital information into a waveform suitable for transmission over the channel. The communication channel distorts the transmitted waveform. One of the many sources of signal distortion is the presence of multipath in the communication channel. Due to the effect of the multipath signal propagation, intersymbol interference (ISI) occurs in the received waveform. Moreover, the transmitted signal gets distorted due to the effect of various kinds of interference and noise, as it propagates through the channel. ISI and the channel noise distort the amplitude and phase of the transmitted signal, which lead to erroneous bit detection at the receiver. It is desirable for a good communication system that its receiver is able to retrieve the digital information from the received waveform, even in the presence of channel impairments such as, multipath eﬀect and noise.
Orthogonal Frequency Division Multiplexing (OFDM) is a MultiCarrier Modulation (MCM) technique that enables high data rate transmission and is robust against ISI (Saltzberg, 1967), (Weinstein and Ebert, 1971), (Hirosaki, 1981). It is a form of frequency division multiplexing (FDM), where data is transmitted in several narrowband streams at various carrier frequencies. The subcarriers in an OFDM system are orthogonal under ideal propagation conditions. By dividing the input bitstream into multiple and parallel bitstreams, the objective is to lower the data rate in each subchannel as compared to the total data rate and also to make subchannel bandwidth lower than the coherence bandwidth of the communication channel. Therefore, each subchannel will experience ﬂatfading and will have small ISI. Hence an OFDM system requires simpliﬁed equalization techniques, to mitigate the intersymbol interference. The ISI can be completely eliminated in OFDM transceivers by utilizing the principle of cyclic preﬁxing (CP). Therefore, high data rate communication systems prefer to apply multicarrier modulation techniques. OFDM has been standardized for many digital communication systems, including ADSL, the 802.11a and 802.11g Wireless LAN standards, Digital audio broadcasting including EUREKA 147 and Digital Radio Mondiale, Digital Video Broadcasting (DVB), some Ultra Wide Band (UWB) systems, WiMax, and Power Line Communication (PLC) (Sari, et al., 1995) (Frederiksen and Prasad, 2002), (Baig and Gohar, 2003).
Over the years, OFDM has evolved into variants, such as Discrete Multitone (DMT), and hybrid modulation techniques, such as multicarrier code division multiple access (MCCDMA), Wavelet OFDM and Discrete Wavelet Multitone (DWMT). Several factors are responsible for the development of these variants, especially Wavelet based OFDM techniques, which target several disadvantages associated with Multicarrier modulation (MCM) techniques. Some of these drawbacks are:
the spectral inefficiency associated with the guard interval insertion, which includes the cyclic prefix
the high degree of spectral leakage due to high magnitude side lobes of pulse shape of sinusoidal carriers
OFDM based communication system’s sensitivity to intercarrier interference (ICI) and narrowband interference (NBI)
Therefore, a Discrete Wavelet Transform (DWT) based MCM system was developed as an alternative to DFT based MCM scheme (Lindsey, 1995).DWT based MCM techniques came to be known as WaveletOFDM in wireless communications and as Discrete Wavelet Multitone (DWMT) for harsh and noisy wireline communication channels such as Digital Subscriber Line (DSL) or Power Line Communications (PLC) (Baig and Mughal, 2009).
This chapter describes the application of DWT in Discrete Multitone (DMT) transceivers and its performance analysis in Digital Subscriber Line (DSL) channel, in the presence of background noise, crosstalk etc. Time domain equalization techniques proposed for DWT based multitone that is DWMT are discussed, along with the simulation results. The pros and cons of adopting DWT instead of DFT in DMT transceivers will also be discussed, highlighting the open areas of research.
2. Basics of wavelet filter banks &multirate signal processing systems
Wavelets and filter banks play an important role in signal decompositioninto various subbands, signal analysis, modeling and reconstruction. Some areas of DSP,such as audio and video compression, signal denoising, digital audio processing and adaptivefiltering are based on wavelets and multirate DSP systems. Digital communication is a relativelynew area for multirate DSP applications. The wavelets are implemented by utilizing multirate filter banks (Fliege, 1994). The discovery of Quadrature Mirror Filter banks (QMF) led to the idea of Perfect Reconstruction (PR), and thus to subband decomposition. Mallatcame up with the idea of implementing wavelets by filter banks for subband coding and multiresolution decomposition (Mallat, 1999). DWT gives timescale representation of a digital signal using digital filtering techniques. The DWT analyzes the signal at different frequency bands with different resolutions by decomposing the signal into approximation and detail coefficients. The decomposition of the signal into different frequency bands is obtained simply by successive highpass and lowpass filtering of the time domain signal.
2.1. Analysis and synthesis filter banks
Analysis filter banks decomposes input signal into frequency subbands. A two channel analysis filter bank, as shown in Fig. 1, splits the input signal
Consequently, with the sampling frequency,
The two signal spectra overlap. The downsampling will produce aliased components of the signals, that are functions of
2.2. Quadrature mirror filter bank
The analysis and the synthesis filter banks combine to form astructure commonly known as the twochannel quadrature mirror filter (QMF) bank. QMFbank serves as the basic building block in many multirate systems. A twochannel QMF bank is shown in Fig. 4. The constituent analysis and synthesis filter banks have power complementaryfrequency responses.The lowpass and highpass filters in the analysis filter bank decompose the input signal intosubbands, and the decimation introduces a certain amount of aliasing, due to the nonideal frequency response of the analysis filters. However, the synthesis filters characteristicsare chosen with such frequency response, that the aliasing introduced by the analysis filter bankis canceled out in the reconstruction process. The output signal
The reconstructed signal
This condition may be satisfied by choosing
The filter banks, which are able to perfectly reconstruct the input signal are the perfect reconstructionfilter banks, that satisfy the perfect reconstruction condition.The desired QMF output includes the function
2.3. Transmultiplexer
Transmultiplexers form an integral part of modems and transceivers based on filter banks that work on the principle of perfect reconstruction. A simple twochannel filter bank can be utilized to illustrate the perfect reconstruction condition. A transmultiplexer is the dual of Subband coder (SBC) in structure. Fig. 6 shows a twochannel transmultiplexer filter bank, which converts a timeinterleaved signal at its input to a FDM signal, having separate bands of spectrum multiplexed together and then converts it back into TDM signal at its output. Transmultiplexers find application in modems and transceivers for digital communication (Vaidyanthan, 1993).
3. Discrete multitone modulation technique
Discrete Multitone (DMT) modulation is a variant of OFDM associated with various loadingalgorithms, so as to optimize a transceiver’s performance in wireline channels like AsymmetricalDigital Subscriber Line (ADSL) and power line (Chow, et al., 1991). In literature, several loadingalgorithms have been developed; allocating resources such as data bits, or power in order tooptimize high data rate, low average transmitting power, or low bit error rate. Typically twoof these parameters are kept constant and third is the goal of optimization.
A conventional DFT based DMT transceiver block diagram is shown in Fig. 7. Thechannel bandwidth is divided into
A guard band consisting of a few samples of the DMT symbol is preappended to the symbol.This is the cyclic prefix, which consists of the last
3.1. Evolution of discrete wavelet multitone modulation
A major drawback of DFTDMT is that the rectangular lowpass prototype filter results in
Many contributions in literature have emphasized the need for DWMT in specific channel conditions. Tzannes and Proakis have proposed DWMT in (Tzannes, et al., 1994), and shown it to be superior to DFTDMT. Authors suggest implementing DWMT in DSL channel for improved performance (Doux et al., 2003). Studies have compared DMT and DWMT performance in DSL channel (Akansu and Xueming 1998).
DWT exhibits better spectral shaping compared to the rectangular shaped subcarriers of OFDM. Therefore, it offers much lower side lobes in transmitted signal, which reduces its sensitivity to narrowband interference (NBI) and intercarrier interference (ICI). However, it cannot utilize CP to mitigate ISI created by the frequencyselective channel, as various DWT symbols overlap in time domain (Vaidyanathan, 1993). Nevertheless, such MCM systems based on DWT require an efficient equalization technique to counter the ISI created by the channel.
4. Discrete Wavelet Multitone (DWMT) in Digital Subscriber Line (DSL)
A system based on Discrete Wavelet Multitone (DWMT) for modulating and demodulating the required signal using Discrete Wavelet Transform as a basis function has been suggested in wireless applications (Jamin andMähönen, 2005). The importance of DWMT in wireless communication is a recognized area of research and on similar lines a DWMT system can be implemented in wireline communication. It can be used as a maximally decimated filter bank with its overlapping symbols in timedomain. Therefore, this structure does not require the addition of CP which is an overhead in DMT and DWMT based wireline systems (Vaidyanathan, 1993). On the other hand, the wavelet filters also possess the advantages of having greater sidelobe attenuation and requires no CP (Bingham, 1990). Therefore, the DWMT systems are bandwidth efficient by not using the CP which creates the problem of bandwidth containment in DMT based systems. However, application of the DWMT systems in a dispersive channel like ADSL necessitates a robust channel equalization technique (Sandberg and Tzannes, 1995). In literature some equalization techniques for DMT based multicarrier systems have been suggested by many authors (Pollet and Peeters, 2000); (Acker et al., 2001); (Acker et al., 2004); (Karp et al.,2003) and DWMT based multicarrier systems (Viholainen et al., 1999). Equalization is a key factor in the design of modems based on DWMT modulation technique and till date, it remains an open research area. When using the Discrete Wavelet Packet Transform (DWPT) as a basis function in DWMT systems, it is difficult to equalize the overlapped symbols in time domain. We emphasize on the design of equalizer for DWPT based DWMT multicarrier systems. The proposed system is based on DWPT for DWMT wireline systems and timedomain equalization is suggested for the equalization process of overlapped symbols.
In this chapter, the timedomain equalization through a linear transversal filter is applied. The equalization algorithms are based on ZeroForcing (ZF) and minimum mean squared error (MMSE) criterion to a discrete waveletpacket transform based DWMT transceiver for a wireline ADSL channel. It is then compared with the system’s performance of a DMT based ADSL system. For a fair comparison between the two systems, the DMT system also utilizes the same timedomain equalization. The performance of the proposed waveletpacket based transceiver is also evaluated in the presence of nearend crosstalk (NEXT) and farend crosstalk (FEXT) for downstream ADSL. It is shown that the DWMT system conserves precious bandwidth by not utilizing any CP, and gives improvement in bit error rate (BER) performance over the DMT system with timedomain equalization (TEQ).
4.1. System model of DWMT
The DWMT system model’s block diagram is shown in Fig. 8. It divides the input data bitstream into multiple and parallel bitstreams. The proposed DWMTtransceiver is based on discrete wavelet packet transform (DWPT). The DWPT is implemented througha reverse order perfect reconstruction filter bank transmultiplexer. Wavelet packets can be implementedas a set of FIR filters, which leads to the filter bank realization of wavelet transform,according to Mallat’s algorithm (Mallat, 1998). The blocked version of the input signal
4.1.1 Water filling bit loading
Bit loading is usually applied to DMT modulated systems applied to wireline channels, by firstestimating the signaltonoise ratio (SNR) of each subchannel through channel estimation techniques, which is followed by the distribution of bits to these subchannels according to their respective SNR. WaterFilling bit loading algorithm applied in the proposed system is rate adaptive and it is suitable for achieving maximum bitrate and also useful when considering the large number of subchannels and variable QAM constellation (Leke and Cioffi, 1997);(Yu and Cioffi, 2001). A discrete version of this algorithmis applied, in which the bitloading procedure initiates by determining the subchannels that shouldbe turned off, due to very low SNR. The bits are assigned to channels according to their capacity, expressed mathematically as (Thomas et al., 2002),
where
where
4.2. ADSL channel
Digital Subscriber Line, commonly known as DSL is the most popularand ubiquitously available wireline medium which provides highspeed Internet access over thetwisted pair telephone network. Fig. 10 shows a typical DSL network, which consists of copperlines extending all the way from the central office (CO) to the customer’s premises. Currentand future applications such as Interactive Personalized TV, high definition TV (HDTV) andvideoondemand through highspeed Internet access, will require more bandwidth. Researchersare exploring costeffective ways to exploit the existing copper infrastructure to deliver greaterbandwidth.
Although the DSL channel offers the advantage of utilizing the already in place telephonelines to carry digital data, however there are different channel impairments that pose difficultiesin achieving the objective of highspeed and reliable communication (Cook, et al.,1999). These channel impairments include different types of noise and interference. The noise sourcesinclude crosstalk, impulse noise and narrow band noise (Thomas Starr, et al., 2002). Also,interference in the communication signal may occur due to the electromagnetic conduction(EMC) in the unshielded twisted pair (UTP) and DSL operating in the vicinity of transmittersmay pick up radio frequency interference (RFI) (Cook, et al.,1999). Moreover, signal reflectionmay be induced due to bridge tabs, unterminated lines and load mismatching in the telephonenetwork. This leads to multipath signal propagation, due to ISI occurs (Bingham, 2000). BER deterioration, due to ISI is a significantproblem in the communication systems utilizing the DSL channel. Atypical telephone line frequency response and its impulse response are shown in Fig. 11 and Fig. 12 respectively. Multicarrier modulation is a possible solution to the ISI problem in DSL, which is already standardized inAsymmetric digital subscriber line (ADSL), in the form of DMT modulation, as G.DMT andG.lite ADSL.
4.2.1. Crosstalk
In a telephone network, each subscriber is connected to the CO through a twisted pair, however, hundreds of such pairs are bound together in a cable. The twisting in the wires keeps theelectromagnetic coupling between them to a minimum, however, when the pairs are numerous,all crosstalk between the pairs cannot be completely removed. Therefore, this crosstalk constitutesa dominant impairment, where DSL channel is concerned. The DSL crosstalk types,namely near end crosstalk (NEXT) and farend crosstalk (FEXT) are illustrated in Fig. 13 (Thomas Starr, et al., 2002). NEXT is the crosstalk due to the neighboring transmitter on adifferent twisted pair line and its power increases with increase in frequency. FEXT is the noisedetected by the receiver located at the far end of the cable from the transmitter. FEXT istypically less severe than NEXT, because FEXT is attenuated as the cable length increases.
In this chapter, the performance of DWMT transceiver is evaluated for the downstream ADSL channel. For this purpose, the NEXT and FEXTare modeled using the ADSL standard G.992.1/G.992.2(ITUT, 2003).
The PSD of the ADSL transceiver disturbers for downstream is given by (ITUT, 2003),
Where
where
where
PSD of disturbers and NEXT is shown in Fig. 14(a)and Fig. 14 (b) displaysthe FEXT PSD for downstream ADSL (ITUT, 2003). The NEXT and FEXT for upstream can be computed in a similar manner (ITUT, 2003).



Number of disturbers  24  24 
f_{LP3dB}  fs/2  fs/2 
f_{HP3dB}  138 kHz  138 kHz 
K_{ADSL}  0.1104 watts  0.1104 watts 
f_{NXT}  160 kHz  160 kHz 
NPSL  47.0 dB  47.0 dB 
f_{FXT}  160 kHz  160 kHz 
d_{FXT}  1.0 km  1.0 km 
FPSL  45.0 dB  45.0 dB 
The wavelet packet transform (WPT) transmultiplexer in the proposed DWMT transceiver gives perfect reconstructionof the transmitted signal, if ideal channel conditions are assumed. However, an actualchannellike ADSL is far from ideal, and therefore requires some form of equalization to reliably retrievethe transmitted signal. Time domain equalization is proposed here for DWMT basedtransceiver for ADSL. There are some equalization techniques for ADSL proposed in literature (Acker et al., 2004);(SMÉKAL et al., 2003);(Trautmann and Fliege, 2002); (Yap and McCanny, 2002).
4.3. Time domain equalization
In order to equalize the signal after it has been dispersed by the ADSL channel, timedomain equalization is proposed, and it is implemented through a linear transversal filter. The equalizerfilteris a linear function of the channel length
4.3.1. ZF finite length equalizer
In ZF algorithm it cancels out the channel effect completely by multiplying the received signal with the inverse of the channel impulse response, as shown in Fig. 15. With an infinite length equalizer filter, it is possible to force the system impulse response to zero at all sampling points (Proakis, 1995). However, since an infinite length filter is unrealizable. Therefore, a finite length filter is considered that approximates the infinite length filter (Proakis, 1995). The received signal y is the distorted version of the transmitted signal x after convolution with the channel c_{h} plus the channel noise r. The received signal can be expressed in vector notation as,
The equalizer output vector zcan be found by convolving a set of a training sequence input samples h and equalizer tap weights c (Sklar, 2001),
However, we continue with the assumption that channel state information is entirely known at the receiver. Therefore, a square matrix h, consisting of channel coefficients is formulated with the help of ZF criterion. The ZF algorithm defines that in order to minimize the peak ISI distortion by selecting the equalizer filter weights csuch that the equalizer output is enforced to zero at sample points other than at the desired pulse. The weights are chosen such that (Sklar, 2001)
The equalizing filter has
The job of equalizing filter is to recover the transmitted signal
where
4.3.2. MMSE criterion
The MMSE criterion represents a more robust solution compared to the ZF since it considers the effect of additive channel noise (Proakis, 1995);(Sklar, 2001). The MMSE criterion of transversal equalizer filter coefficients optimizes the mean squared error of all the ISI terms plus the noise at the equalizer output. A set of over determined equations is formed, in order to derive a minimum MSE solution of the equalizer filter (Sklar, 2001). Therefore, for a
where
For the MMSE solution of the equalizing filter, an over sampled nonsquare matrix h is formed which is transformed to a square autocorrelation matrix R_{hh,} yielding the optimizedfilter coefficients.
4.4. Simulation results
An ADSL system is investigated which is based on DWPT transmultiplexer. The system utilizes



Data rate  1 Mbps  1 Mbps 
Sampling Frequency  2.208MHz  2.208 MHz 
Modulation 


Cyclic Prefix  None  20% 
FFT size (N)    512 
Waveletlevel  2   
Number of bits/subchannel  1 to 6  1 to 6 
This corresponds to a system bandwidth of 2 MHz with data rate of 1 Mbps with discrete wavelet packet filter which is used for transmitter and receiver end. The channel equalization is performed by applying a linear equalizing filter in timedomain. The filter coefficients for equalization are optimized by ZF algorithm and MMSE criterion. The ADSL channel is simulated by an FIR filter of 100 taps.
The prototype filter for the synthesis and analysis part of the transmultiplexer is a discrete wavelet filter using 2level wavelet packet. The input symbols
The equalizer frequency response of ZF equalizer FIR filter is shown in Fig. 16. Initially DWMT transceiver and DMT systems are compared regarding the bit error rate (BER) performance in AWGN channel, having identical timedomain zeroforcing channel equalization. Although, the conventional DMT system equalization is a combination of timedomain equalization (TEQ) and frequencydomain equalization (FEQ) techniques, in this case DMT is equalized with a timedomain ZeroForcing for fair comparison between the two systems. The DWPT transform is applied utilizing Haar wavelet. Fig. 17 shows the comparative performance of two systems in the presence of AWGN without crosstalk. The BER curve, shown in Fig. 17, presents the fact that the two systems give almost identical performance for lower SNR, and at higher SNR, the DWMT system exhibits an improvement of 1 dB in
Fig. 18 shows the performance of DWMT and DMT systems in ADSL channel with AWGN, NEXT and FEXT (crosstalk), utilizing timedomain equalization (TEQ) techniques. The NEXT & FEXT represent the downstream crosstalk in ADSL channel according to the G.992.1/G.992.2 standard (ITUT, 2003), with the simulation parameters as described in Table 1. DMT system is still equalized by ZFTEQ, while the DWMT transceiver is equalized by ZFTEQ, timedomain MMSE (MMSETEQ). The BER curves shown in Fig. 18 validate the fact that the wavelet packet transmultiplexer improves the performance of DWMT transceiver, having ZFTEQ by E_{b}/N_{o} margin of 1.0 db for BER of 1E4,over a DMT transceiver, having an identical equalizer. Moreover the MMSETEQ technique for DWMT system shows an improvement of 2 dBs in E_{b}/N_{o} over ZFTEQ technique for DWMT and a 3 dB gain over the ZFTEQ equalized DMT system, at a BER of 1E4.
5. Pros &cons of applying DWT in multicarrier modulation techniques
DWMT modulation based transceiver, appears to be an interesting choice, when utilizing multicarrier modulation techniques in wireline systems. It not only recommends the unique timefrequency localization advantage over the conventional frequency localized DMT systems, but also preserves precious bandwidth, which is wasted in DMT based systems in the form of cyclic prefix. However, when utilized in time dispersive channel like ADSL, DWMT transceiver cannot do without an equalization technique because of the time overlapped symbols. In this chapter DWMT based transceiver is discussed and its performance analyzed for the ADSL channel, in comparison with a conventional DMT modulation with ZF and MMSE algorithms using the timedomain equalization. DWMT system based on WPT performs well in the presence of AWGN and crosstalk in comparison with the DMT system for ADSL. ZF equalization algorithm does not consider noise, while the MMSE criterion of optimizing the equalizer coefficients takes into account the effect of channel noise. Therefore MMSE algorithm based DWMT transceiver gives better BER performance in comparison with ZF criterion, since ZF is known to enhance channel noise. The timedomain equalization is computationally complex in comparison to frequency domain equalization, however it offers improved bit error rate.
6. Conclusion
The multirate digital signal processing techniques, including wavelets and filter banks are part of new emerging technologies, which are finding applications in the field of digital communications. DWT based Multicarrier modulation techniques have opened new avenues for researchers, to avoid the spectral leakage and spectral inefficiency associated with Fourier Transform based MCM techniques. Time domain equalizers based on ZF and MMSE algorithms are utilized for DSL channel equalization in DWMT transceivers. MMSE based equalizers outperform the ZF equalizers in terms of BER. The equalization techniques adopted for DWMT transceiver is a topic of active research. Moreover, simulation results found in literature have shown that DWT based MCM systems exhibit higher immunity to narrowband interference (NBI). Therefore, WOFDM/DWMT can be considered as a viable alternative to spectrally inefficient OFDM/DMT, however at the cost of higher computational complexity of equalization.
References
 1.
Acker K. V. Leus G. Moonen M. van de Wiel O. Pollet T. 2001 Per toneequalization for DMTbased systems,  2.
Akansu A. N. Xueming Lin. (1998 1998 A comparative performance evaluation of DMT (OFDM) and DWMT (DSBMT) based DSL communications systems for single and multitone interference.  3.
Alliance for Telecommunications Industry Solutions, 1995 American National Standardfor Telecommunications Network and Customer Installation Interfaces Asymmetric Digital Subscriber Line (ADSL) Metallic Interface. ANSI T1.413 1995 ANSI. New York.  4.
Jamin A. Mähönen P. 2005 Wavelet packet modulation for wireless communications,  5.
Baig S. Gohar N. D. . April 2003 Discrete MultiTone Transceiver atthe Heart of PHYLayer of an InHome Powerline Communication Local Area Network.  6.
Baig S. Mughal M. J. 2009 Multirate signal processing techniques for highspeed communication over power lines,  7.
Bingham J. C. 2000  8.
Chow P. S. Tu J. C.and Cio. J. M. 1991 Performance Evaluation ofa Multichannel Transceiver System for ADSL and VHDSL Services.  9.
Cook J. W. Kirkby R. H. Booth M. G. Foster K. T. Clarke D. E. A. Young G. 1999 The noise and crosstalk environment for ADSL and VDSL systems  10.
Doux C. V. Lienard J. Conq B. Gallay P. 2003 Efficient implementation of discrete wavelet multitone in DSL communications.  11.
Farrukh F. Baig S. Mughal M. J. 2007 Performance Comparison of DFTOFDM and WaveletOFDM with ZeroForcing Equalizer for FIR Channel Equalization,  12.
Farrukh F. Baig S. Mughal M. J. 2009 MMSE Equalization for Discrete Wavelet Packet Based OFDM,  13.
Frederiksen F. B. Prasad R. 2002 An overview of OFDM and relatedtechniques towards development of future wireless multimedia communications.  14.
Fliege N. J. 1994  15.
June H. S. D. . 1988 The LOT: a link between block transform coding and multirate filter banks.  16.
Hirosaki B. . Jul 1981 An Orthogonally Multiplexed QAM System Using the Discrete Fourier Transform.  17.
Karp T. Wolf M. Trautmann S. Fliege N. J. 2003 ZeroForcing Frequency Domain Equalization for DMT Systems with Insufficient Guard Interval,  18.
Leke A.and. Cioffi I. M. 1997 A Maximum Rate Loading Algorithm for Discrete Multitone Modulation System,  19.
Lindsey A. R.and Dill. J. C. 1995 Wavelet packet modulation: a generalized method for orthogonally multiplexed communications.  20.
Mallat S. 1999  21.
Malvar H. S. 1992 Extended lapped transforms: Properties, applications and fast algorithms.  22.
Pollet, Thierry and Peeters, Miguel. 2000 Equalization for DMTBased Broadband Modems,  23.
Proakis J. G. 1995  24.
Qian Shie. 2002  25.
Saltzberg B. . Dec 1967 Performance of an Efficient Parallel Data Transmission System.  26.
Sandberg S. D.and Tzannes. M. A. (1995 1995 Overlapped discrete multitone modulation for high speed copper wire communications,  27.
Sari,H.Karam,G.and Jeanclaude,I. 1995 Transmission techniquesfor digital terrestrial TV broadcasting.  28.
Sklar B. 2001  29.
Starr T. Sorbara M. Cioffi J. M. Silverman P. J. 2002  30.
Tzannes M. A. Tzannes M. C. Proakis J. Heller P. N. 1994 DMT systems, DWMT systems and digital filter banks.  31.
Van Acker K. Leus G. Moonen M. Pollet T. 2004 Improved initialization for time domain equalization in ADSL, Signal Processing,84 1895 1908  32.
Vaidyanathan P. P. (1993 1993  33.
Viholainen A. Alhava J. Helenius J. Rinne J. Renfors M. (1999 1999 Equalization in filter bank based multicarrier systems, in  34.
Weinstein S. Ebert P. . Oct 1971 Data Transmission by FrequencyDivision Multiplexing Using the Discrete Fourier Transform.  35.
Yap K. S.and J. V. Mc Canny 2002 A mixed costfunction adaptive algorithm for ADSL timedomain equalization,  36.
Yu W.and. Cioffi J. M. 2001 On Constant Power WaterFilling