Algorithms for Adaptive Equalization in Wireless Applications

Since the introduction of adaptive equalizers in digital communication systems by Lucky [1], much progress has been made. Due to their particular constraints many new and different concepts in the wireless domain have been proposed. The wireless channel is typically time and frequency dispersive, making it difficult to use standard equalizer techniques. Also, due to its time varying nature, long transmission bursts may get corrupted and require a continuous tracking operation. Thus, transmission is often performed in short bursts, allowing only a limited amount of training data. Furthermore, quite recently, advantages of the multiple-input multiple-output character of wireless channels have been recognized. This chapter presents an overview of equalization techniques in use and emphasizes the particularities of wireless applications.

[1]  P. Fuhrmann.  A Polynomial Approach to Linear Algebra , 1996 .

[2]  C. R. Johnson,et al.  Blind fractionally-spaced equalization of digital cable TV , 1996, Proceedings of 8th Workshop on Statistical Signal and Array Processing.

[3]  Reinaldo A. Valenzuela,et al.  V-BLAST: an architecture for realizing very high data rates over the rich-scattering wireless channel , 1998, 1998 URSI International Symposium on Signals, Systems, and Electronics. Conference Proceedings (Cat. No.98EX167).

[4]  Lang Tong,et al.  A new approach to blind identification and equalization of multipath channels , 1991, [1991] Conference Record of the Twenty-Fifth Asilomar Conference on Signals, Systems & Computers.

[5]  John M. Cioffi,et al.  MMSE decision-feedback equalizers and coding. I. Equalization results , 1995, IEEE Trans. Commun..

[6]  Markus Rupp,et al.  Efficient implementation of parallel decision feedback decoders for broadband applications , 1999, ICECS'99. Proceedings of ICECS '99. 6th IEEE International Conference on Electronics, Circuits and Systems (Cat. No.99EX357).

[7]  Ali H. Sayed,et al.  A feedback approach to the steady-state performance of fractionally spaced blind adaptive equalizers , 2000, IEEE Trans. Signal Process..

[8]  W. C. Jakes,et al.  Microwave Mobile Communications , 1974 .

[9]  R. V. Nee,et al.  Maximum likelihood decoding in a space division multiplexing system , 2000, VTC2000-Spring. 2000 IEEE 51st Vehicular Technology Conference Proceedings (Cat. No.00CH37026).

[10]  Gerald Matz,et al.  Efficient POCS algorithms for deterministic blind equalization of time-varying channels , 2000, Globecom '00 - IEEE. Global Telecommunications Conference. Conference Record (Cat. No.00CH37137).

[11]  F. K. Becker,et al.  Automatic equalization for digital communication , 1965 .

[12]  Ernst Bonek,et al.  Semi-blind space-time estimation of co-channel signals using least squares projections , 1999, Gateway to 21st Century Communications Village. VTC 1999-Fall. IEEE VTS 50th Vehicular Technology Conference (Cat. No.99CH36324).

[13]  Anders Ahlén,et al.  Tracking of time-varying mobile radio channels .1. The Wiener LMS algorithm , 2001, IEEE Trans. Commun..

[14]  G. J. Foschini,et al.  Equalizing without altering or detecting data , 1985, AT&T Technical Journal.

[15]  Mounir Ghogho,et al.  Adaptive MLSE receiver over rapidly fading channels , 2000, Signal Process..

[16]  Andrew J. Viterbi,et al.  Error bounds for convolutional codes and an asymptotically optimum decoding algorithm , 1967, IEEE Trans. Inf. Theory.

[17]  Franz Hlawatsch,et al.  Blind multiuser equalization for time-varying channels , 2001, 2001 IEEE Third Workshop on Signal Processing Advances in Wireless Communications (SPAWC'01). Workshop Proceedings (Cat. No.01EX471).

[18]  John M. Cioffi,et al.  MMSE decision-feedback equalizers: finite-length results , 1995, IEEE Trans. Inf. Theory.

[19]  Ehud Weinstein,et al.  New criteria for blind deconvolution of nonminimum phase systems (channels) , 1990, IEEE Trans. Inf. Theory.

[20]  D. Slock,et al.  Linear receivers for the DS-CDMA downlink exploiting orthogonality of spreading sequences , 1998, Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284).

[21]  D. Godard,et al.  Self-Recovering Equalization and Carrier Tracking in Two-Dimensional Data Communication Systems , 1980, IEEE Trans. Commun..

[22]  G. David Forney,et al.  Maximum-likelihood sequence estimation of digital sequences in the presence of intersymbol interference , 1972, IEEE Trans. Inf. Theory.

[23]  Hans D. Hallen,et al.  Long-range prediction of fading signals , 2000, IEEE Signal Process. Mag..

[24]  Georgios B. Giannakis,et al.  Signal processing advances in wireless and mobile communications , 2000, IEEE Signal Process. Mag..

[25]  Markus Rupp.  Fast implementation of the LMS algorithm , 2000, 2000 10th European Signal Processing Conference.

[26]  M. Rupp,et al.  Robustness and convergence of adaptive schemes in blind equalization , 1996, Conference Record of The Thirtieth Asilomar Conference on Signals, Systems and Computers.

[27]  John G. Proakis,et al.  Equalization and Decoding for Multiple-Input Multiple-Output Wireless Channels , 2002, EURASIP J. Adv. Signal Process..

[28]  Inbar Fijalkow,et al.  Fractionally spaced equalizers , 1996, IEEE Signal Process. Mag..

[29]  Franz Hlawatsch,et al.  Blind equalization of MIMO channels using deterministic precoding , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[30]  M. Rupp.  On the learning behavior of decision feedback equalizers , 1999, Conference Record of the Thirty-Third Asilomar Conference on Signals, Systems, and Computers (Cat. No.CH37020).

[31]  L. Tong,et al.  Multichannel blind identification: from subspace to maximum likelihood methods , 1998, Proc. IEEE.

[32]  Phillip A. Regalia,et al.  On the numerical stability and accuracy of the conventional recursive least squares algorithm , 1999, IEEE Trans. Signal Process..

[33]  Anders Ahlén,et al.  Tracking of time-varying mobile radio channels. II. A case study , 2002, IEEE Trans. Commun..

[34]  Andrew J. Blanksby,et al.  Reduced-state sequence estimation with tap-selectable decision-feedback , 2000, 2000 IEEE International Conference on Communications. ICC 2000. Global Convergence Through Communications. Conference Record.

[35]  M. Rupp,et al.  Performance analysis of unbiased finite-length DFE receivers , 1998, Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284).

[36]  Y. Sato,et al.  A Method of Self-Recovering Equalization for Multilevel Amplitude-Modulation Systems , 1975, IEEE Trans. Commun..

[37]  Mao-Ching Chiu,et al.  Analysis of LMS-adaptive MLSE equalization on multipath fading channels , 1996, IEEE Trans. Commun..

[38]  Harish Viswanathan,et al.  Optimal channel training for multiple antenna systems , 2000 .

[39]  J. Salz.  Optimum mean-square decision feedback equalization , 1973 .

[40]  Shahid U. H. Qureshi,et al.  Reduced-state sequence estimation for coded modulation of intersymbol interference channels , 1989, IEEE J. Sel. Areas Commun..

[41]  John G. Proakis,et al.  Digital Communications , 1983 .

[42]  Constantinos B. Papadias,et al.  Unbiased decision feedback equalization , 1998, Proceedings. 1998 IEEE International Symposium on Information Theory (Cat. No.98CH36252).

[43]  Hui Liu,et al.  Deterministic approaches for blind equalization of time-varying channels with antenna arrays , 1998, IEEE Trans. Signal Process..

[44]  Georgios B. Giannakis,et al.  Modelling and equalization of rapidly fading channels , 1996 .

[45]  Markus Rupp,et al.  Normalization and convergence of gradient-based algorithms for adaptive IIR filters , 1995, Signal Process..

[46]  Ali H. Sayed,et al.  On the convergence of blind adaptive equalizers for constant modulus signals , 2000, IEEE Trans. Commun..

[47]  H.-L. Lou,et al.  Implementing the Viterbi algorithm , 1995, IEEE Signal Process. Mag..

[48]  Lang Tong,et al.  Single-user channel estimation and equalization , 2000, IEEE Signal Process. Mag..

[49]  Ronald A. Iltis,et al.  A digital DS spread-spectrum receiver with joint channel and Doppler shift estimation , 1991, IEEE Trans. Commun..

[50]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[51]  D. Falconer,et al.  Least sum of squared errors (LSSE) channel estimation , 1991 .

[52]  Markus Rupp,et al.  Adaptive DFE algorithms for IS-136 based TDMA cellular phones , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[53]  L. J. Greenstein,et al.  Modeling multlipath fading responses using multitone probing signals and polynomial approximation , 1981, The Bell System Technical Journal.

[54]  Lajos Hanzo,et al.  Fast adaptation of fractionally spaced equalisers , 2002 .

[55]  Thomas Kailath,et al.  Linear Systems , 1980 .

[56]  Markus Rupp,et al.  Robustness conditions of the LMS algorithm with time-variant matrix step-size , 2000, Signal Process..