Pulse or digital communications – Equalizers – Automatic
Reexamination Certificate
1998-04-29
2001-02-13
Pham, Chi H. (Department: 2731)
Pulse or digital communications
Equalizers
Automatic
C333S02800T
Reexamination Certificate
active
06188722
ABSTRACT:
FIELD OF THE INVENTION
This invention relates to the field of blind convergence processes in an adaptive decision feedback equalizer such as may be used in modems.
BACKGROUND OF THE INVENTION
In communication systems a modem is used to convert (modulate) digital signals generated by a computer into analog signals suitable for transmission over telephone lines. Another modem, located at the receiving end of the transmission, converts (demodulates) the analog signals back into digital form. In a particular modulation transmission scheme, the phase and amplitude of a signal are shifted to various combinations of values, each combination indicating a different set of transmitted bits. At the receiver, proper decoding includes detecting the various phase and amplitude combinations. In a two dimensional modulation scheme, the signal can be represented mathematically with an I (in-phase) component and a Q (quadrature-phase) component of the signal, each of which is &pgr;/2 out of phase with respect to the other. The plot of these two components on a two dimensional graph for a set of received symbols results in a pattern referred to as a constellation.
Proper detection of the I and Q components of the signal is hampered by various sources of signal degradation. One such source is intersymbol interference where consecutive transmitted symbols interfere with each other. Other sources of signal degradation include the transmission media (i.e. wire) and analog filters. These factors produce large amplitude and group delay distortion in the signal that needs compensation.
To compensate for intersymbol interference (ISI) and other sources of signal degradation and distortion, best performance is achieved by implementing an equalizer as a fractionally spaced adaptive filter. An adaptive filter can modify from time instant to time instant, the coefficients, also referred to as tap weights, used in the filter to remove ISI and to compensate for amplitude and group delay distortions. The update of the tap weights is done to minimize the error at the output of the filter. This error is effectively a measure of the difference between the actual output of the filter and the expected output. The adaptive process continues until the error is at a minimum (i.e. the filter converges).
The convergence of an equalizer depends on many factors including initial tap weights, desired convergence rate, signal to noise ratio (SNR) at the input and phase changes caused by a clock recovery circuit at the receiver, and can be accomplished with various adaptive algorithms.
The adaptation of the tap weights in adaptive equalizers is based on an assumed correct decision about which symbol was received. This assumption is valid for equalizers with a training sequence for which the received symbol is in fact known in advance. Equalizers, however, are also used without the benefit of a training sequence, in which case the decision is not necessarily correct. These equalizers are referred to as blind equalizers. The term blind refers to trying to find the correct equalizer coefficients without a reference training sequence, therefore during convergence the decisions may be incorrect and the coefficients (weights) erroneously updated. Although the possibility of a mistake exists, if the blind equalizer makes correct decisions for a sufficiently large set of received symbols, the equalizer will converge correctly.
If many erroneous decisions occur, the algorithm may converge to a local minimum (false convergence) or may not converge at all. Two common types of convergence failures for two dimensional modulation schemes such as Quadrature Amplitude Modulation (QAM), where the information is transmitted by modulating both the amplitude and phase of the carrier signal, are summarized below:
1. Both the I and Q parts of the equalizer converge to tap weight settings such that both parts decode the same symbols, either the I symbols or the Q symbols. When the I and Q equalizers converge to similar tap weight settings, the resulting constellation appears as a diagonal line.
2. The I and Q parts converge to tap weight settings such that the I equalizer decodes a symbol transmitted at time t while the Q equalizer decodes a symbol transmitted at time t-1. This failure is difficult to detect since the I and Q parts of the equalizer are passing their respective correct I and Q components, albeit not from the same time instant (i.e. instead of having a &pgr;/2 difference, the I and Q components have a 5&pgr;/2 difference).
Prior art solutions for dealing with the convergence failures discussed above continue to present potential problems in their use since filter convergence is not guaranteed in theory, depending on the distortion and noise. Further, convergence without the need of training sequences based on random QAM input is very difficult for higher than 4 QAM since there is no reference signal available.
SUMMARY OF THE INVENTION
An object of the present invention is to provide a blind convergence process for an adaptive decision feedback equalizer to limit convergence failure.
In accordance with an aspect of the present invention there is provided a blind convergence process for an adaptive decision feedback equalizer having a quadrature amplitude modulation (QAM) slicer, a forward filter defined by a plurality of forward tap coefficients and a feedback filter defined by a plurality of feedback tap coefficients, said blind convergence process comprising the steps of: (a) performing a clustering process comprising: (al) updating the forward tap coefficients of the forward filter; (b) performing a decision directed process for a predefined set of QAM indexes having values n
1
, n
2
, . . . , n
m
, comprising: (b1) updating the forward tap coefficients of the forward filter for a QAM index n
i
selected from the predefined set of QAM indexes, and (b2) updating the feedback tap coefficients of the feedback filter for the QAM index n
i
selected from the predefined set of QAM indexes; (c) determining if the equalizer has converged, and (d) repeating steps (b1), (b2) and (c) for a next QAM index n
i+1
of the predefined set of QAM indexes until the equalizer has converged to the highest operable QAM index.
In accordance with another aspect of the present invention there is provided a blind convergence process for an adaptive decision feedback equalizer having a quadrature amplitude modulation (QAM) slicer, a forward filter defined by a plurality of forward tap coefficients and a feedback filter defined by a plurality of feedback tap coefficients, said blind convergence process comprising the steps of: (a) initializing the forward tap coefficients of the forward filter and the feedback tap coefficients of the feedback filters with predetermined values; (b) updating the forward tap coefficients of the forward filter with a 4 QAM signal; (c) performing a decision directed process for a predefined set of QAM indexes having values n
1
, n
2
, . . . , n
m
, comprising: (c1) updating the forward tap coefficients of the forward filter for a QAM index n
i
selected from the predefined set of QAM indexes, and (c2) updating the feedback tap coefficients of the feedback filter for the QAM index n
i
selected from the predefined set of QAM indexes; (d) determining if the equalizer has converged, and (e) repeating steps (c1), (c2) and (d) for a next QAM index n
i+1
of the predefined set of QAM indexes until the equalizer has converged to its highest available QAM index.
REFERENCES:
patent: 5119401 (1992-06-01), Tsujimoto
patent: 5293401 (1994-03-01), Serfaty
patent: 5541956 (1996-07-01), Ueda
patent: 5642382 (1997-06-01), Juan
patent: 5689528 (1997-11-01), Tsujimoto
patent: 5694423 (1997-12-01), Larsson et al.
patent: 6069917 (2000-05-01), Werner et al.
Buz Richard
Dublin Ian
Velez Edgar
Yirga Sisay
Nortel Networks Limited
Pearne & Gordon LLP
Pham Chi H.
Tran Khai
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