Pulse or digital communications – Equalizers – Automatic
Reexamination Certificate
1998-06-23
2004-11-09
Ha, Dac V. (Department: 2634)
Pulse or digital communications
Equalizers
Automatic
C375S350000, C360S065000, C348S726000
Reexamination Certificate
active
06816548
ABSTRACT:
FIELD OF THE INVENTION
This invention concerns adaptive equalization of a video signal transmission channel which may contain high definition television information.
BACKGROUND OF THE INVENTION
The recovery of data from modulated signals conveying digital information in symbol form usually requires three functions at a receiver: timing recovery for symbol synchronization, carrier recovery (frequency demodulation to baseband), and channel equalization. Timing recovery is a process by which a receiver clock (timebase) is synchronized to a transmitter clock. This permits the received signal to be sampled at the optimum point in time to reduce the chance of a slicing error associated with decision-directed processing of received symbol values. Carrier recovery is a process by which a received RF signal, after being frequency down converted to a lower intermediate frequency passband (eg., near baseband), is frequency shifted to baseband to permit recovery of the modulating baseband information.
Many digital data communications systems employ adaptive equalization to compensate for the effects of changing channel conditions and disturbances on the signal transmission channel. The equalization process estimates the transfer function of the transmission channel and applies the inverse of the transfer function to the received signal so as to reduce or eliminate the distortion effects. Channel equalization typically employs filters that remove from a received signal amplitude and phase distortions resulting from a frequency dependent time variant response of the transmission channel, for example, to thereby provide improved symbol decision capability. Equalization removes baseband intersymbol interference (ISI) caused by transmission channel disturbances including the low pass filtering effect of the transmission channel. ISI causes the value of a given symbol to be distorted by the values of preceding and following symbols, and essentially represents symbol “ghosts” since ISI includes advanced and delayed symbols with respect to a reference symbol location in a given decision region.
An adaptive equalizer is essentially an adaptive digital filter. In systems using an adaptive equalizer, it is necessary to provide a method of adapting the filter response so as to adequately compensate for channel distortions. Several algorithms are available for adapting the filter coefficients and thereby the filter response. One widely used method employs the Least Mean Squares (LMS) algorithm. In this algorithm, by varying coefficient values as a function of a representative error signal, the equalizer output signal is forced to approximate a reference data sequence. This error signal is formed by subtracting the equalizer output signal from the reference data sequence. As the error signal approaches zero, the equalizer approaches convergence whereby the equalizer output signal and the reference data sequence are approximately equal.
When the equalizer operation is initiated, the coefficient values (filter tap weights) are usually not set at values which produce adequate compensation of channel distortions. In order to force initial convergence of the equalizer coefficients, a known “training” signal may be used as the reference signal. This signal is programmed at both the transmitter and receiver. The error signal is formed at the receiver by subtracting a locally generated copy of the training signal from the output of the adaptive equalizer. The training signal helps to open the initially occluded “eye” of the received signal, as known. After adaption with the training signal, the “eye” has opened considerably and the equalizer is switched to a decision-directed operating mode. In this mode final convergence of the filter tap weights is achieved by using the actual values of symbols from the output of the equalizer instead of using the training signal. The decision directed equalizing mode is capable of tracking and cancelling time varying channel distortions more rapidly than methods using periodically transmitted training signals. In order for decision directed equalization to provide reliable convergence and stable coefficient values, approximately 90% of the decisions must be correct. The training signal helps the equalizer achieve this 90% correct decision level.
In some systems, however, a training signal is not available. In such case “blind” equalization is often used to provide initial convergence of the equalizer coefficient values and to force the eye to open. In the blind mode, filter coefficients are coarsely adjusted in response to an error signal which is calculated by employing a known function, or algorithm. Among the most popular blind equalization algorithms are the Constant Modulus Algorithm (CMA) and the Reduced Constellation Algorithm (RCA). These algorithms are discussed, for example, in Proakis,
Digital Communications
, McGraw-Hill: New York, 1989 and in Godard, Self-Recovering Equalization and Carrier Tracking in Two Dimensional Data Communication Systems,”
IEEE Transactions on Communications
, November 1980. Briefly, the CMA relies on the fact that, at the decision instants, the modulus of the detected data symbols should lie on a locus of points defining one of several (constellation) circles of different diameters. The RCA relies on forming “super constellations” within the main transmitted constellation. The data signal is first forced to fit into a super constellation, then the super constellation is subdivided to include the entire constellation.
In a conventional system using a feed forward filter (FFF) and a decision feedback filter (DFF) as equalizers, the FFF typically performs adaptive blind equalization (not decision-directed) during the initial signal acquisition interval. The DFF does not provide equalization at this time. At the end of the blind equalization interval, the DFF is activated for decision-directed equalization. In the decision-directed mode, filter coefficients are updated to finer values by using a decision error signal which is calculated by using a known decision function. At this time both the FFF and the DFF have their coefficients adapted (updated) in response to locally generated control signals in a decision-directed mode, eg., based on differences between symbol samples appearing at the input and the output of a slicer network. This approach has disadvantages. If significant ISI and ghost effects are present, it will be difficult for the FFF to achieve equalization since the filter center tap will be contaminated by symbol “ghosts.” To equalize pre- and post-ghosts, the FFF employs both pre-cursor and post-cursor taps. The post-cursor taps of the FFF overlap with the post-cursor taps of the DFF, which is not an efficient use of filter taps. This limitation is avoided by a system of the type described in U.S. Pat. No. 5,712,873-Shiue et al. In that system, a digital signal processor includes a decision feedback filter (DFF) which exhibits different operating modes before and during decision-directed equalization. Specifically, the DFF operates as a linear feedback filter during blind equalization, and as a non-linear filter in the decision-directed mode after blind equalization.
SUMMARY OF THE INVENTION
In accordance with the principles of the present invention, a digital channel equalizer for processing a demodulated VSB signal containing high definition video information comprises a feed forward filter (FFF) and a decision feedback filter (DFF). Both the FFF and DFF operate adaptively in both blind and decision-directed modes.
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U. E. Kraus et al., Bergische University of Wuppertal, Germany,An Experimental 8-VSB Demodulator,International Broadcasting Convention,
Knutson Paul Gothard
Ramaswamy Kumar
Shiue Dong-Chang
Ha Dac V.
Kurdyla Ronald H.
Laks Joseph J.
Thomson Licensing S.A.
Tripoli Joseph S.
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