Pulse or digital communications – Equalizers – Automatic
Reexamination Certificate
1999-12-09
2003-12-23
Chin, Stephen (Department: 2634)
Pulse or digital communications
Equalizers
Automatic
C375S341000, C375S287000, C375S224000, C375S265000, C375S229000, C375S346000, C375S350000, C375S233000, C375S340000, C714S796000, C714S786000, C714S792000, C714S794000, C714S795000, C708S322000, C708S323000
Reexamination Certificate
active
06668014
ABSTRACT:
FIELD OF INVENTION
The present invention relates to blind equalization techniques to compensate for channel transmission distortion in digital communication systems. In particular, the present invention relates to an equalization technique for use with trellis-encoded data such as that adopted by the U.S. for broadcast transmission of high definition television (HDTV) signals.
BACKGROUND OF THE INVENTION
Digital transmission of information typically involves the modulation of pulses on the amplitude and/or phase of an RF carrier. A propagation medium such as terrestrial broadcast introduces signal distortion caused by noise (static), strength variations (fading), phase shift variations, multiple path delays, and the like.
In addition, multiple different paths between the transmitter and receiver through the propagation medium cause multiple path delays. The different paths have different delays that cause replicas of the same signal to arrive at different times at the receiver (like an echo). Multi-path distortion results in inter-symbol interference (ISI) in which weighted contributions of other symbols are added to the current symbol.
In addition to distortion and noise from the propagation medium, front-end portions of the receiver and transmitter also introduce distortion and noise. The presence of distortion, noise, fading and multi-path introduced by the overall communication channel (transmitter, receiver and propagation medium), can cause digital systems to degrade or fail completely when the bit error rate exceeds some threshold and overcomes the error tolerance of the system.
Equalization
Digital systems transmit data as symbols having discrete levels of amplitude and/or phase. The digital receiver uses a slicer to make hard decisions as to the value of the received symbol. A slicer is a decision device responsive to the received signals at its input, which outputs the nearest symbol value from the constellation of allowed discrete levels. A slicer is also known as a nearest element decision device. To the extent that a symbol is received at a level that differs from one of the allowed discrete levels, a measure of communication channel error can be detected.
At the receiver, it is known to use an equalizer responsive to the detected error to mitigate the signal corruption introduced by the communications channel. It is not uncommon for the equalizer portion of a receiver integrated circuit to consume half of the integrated circuit area.
An equalizer is a filter that has the inverse characteristics of the communication channel. If the transmission characteristics of the communication channel are known or measured, then the equalization filter parameters can be determined. After adjustment of the equalization filter parameters, the received signal is passed through the equalizer, which compensates for the non-ideal communication channel by introducing compensating “distortions” into the received signal which tend to cancel the distortions introduced by the communication channel.
However, in most situations such as in HDTV broadcasting, each receiver is in a unique location with respect to the transmitter. Accordingly, the characteristics of the communication channel are not known in advance, and may even change with time. In those situations where the communication channel is not characterized in advance, or changes with time, an adaptive equalizer is used. An adaptive equalizer has variable parameters that are calculated at the receiver. The problem to be solved in an adaptive equalizer is how to adjust the equalizer filter parameters in order to restore signal quality to a performance level that is acceptable by subsequent error correction decoding.
In some adaptive equalization systems, the parameters of the equalization filter are adjusted using a predetermined reference signal (a training sequence), which is periodically re-sent from the transmitter to the receiver. The received training sequence is compared with the known training sequence to derive the parameters of the equalization filter. After several iterations of parameter settings derived from adaptation over successive training sequences, the equalization filter converges to a setting that tends to compensate for the distortion characteristics of the communications channel.
The U.S. standard for broadcast transmission of high definition television (HDTV) signals embeds a recurring training sequence every 24 milliseconds. Unfortunately, for terrestrial broadcast the propagation medium often undergoes time-varying inter-symbol interference characteristics, for example due to such subtle changes as foliage waiving in the wind, which prevent the successful convergence of an equalizer that relies solely on the training sequence for convergence. Therefore, a blind equalization technique is highly desirable.
In blind equalization systems, the equalizer filter parameters are derived from the received signal itself without using a training sequence. In the prior art, it is known to adjust the equalizer parameters blindly using the Least Mean Squares (LMS) algorithm, in which the training symbols are replaced with hard decisions, or best estimates of the original input symbols. Blind equalization systems using LMS in this manner are referred to as decision directed LMS (DD-LMS).
However, the DD-LMS algorithm requires a good initial estimate of the equalizer parameters. For most realistic communication channel conditions, the lack of an initial signal estimate of the equalizer parameters results in high decision error rates, which cause the successively calculated equalizer filter parameters to continue to fluctuate, (diverge or go to +/−infinity), rather than converge to a desired solution.
It is also known to use another algorithm, called the Constant Modulus Algorithm (CMA), in combination with the DD-LMS algorithm from a cold start. The CMA algorithm is used first to calculate the equalizer filter parameters, which is regarded as an initial estimate. Thereafter, the equalizer filter parameters (as calculated by the CMA algorithm) are used in an acquisition mode to find the initial equalizer filter parameters to start the DD-LMS algorithm.
The Constant Modulus Algorithm (CMA) was originally proposed by Godard for QAM signals. See D. N. Godard, “Self-recovering equalization and carrier tracking in two-dimensional data communication systems,” IEEE Transactions on Communications, vol 28, no. 11, pp. 1867-1875, November 1980. A similar technique was independently developed by Treichler and Agee for constant envelope FM signals. See J. R. Treicher, B. G. Agee, “A new approach to multipath correction of constant modulus signals,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. ASSP-31, no. 2, pp. 459-472, April, 1983. Godard's original intention was to develop an algorithm that was insensitive to carrier synchronization in order to decouple equalization and carrier tracking, so that carrier tracking could be done in a decision directed (DD) mode. The satisfaction of the latter goal is the single-most attractive feature of CMA, which accounts for its widespread deployment today. See J. R. Treichler, M. G. Larimore, J. C. Harp, “Practical Blind Demodulators for High-Order QAM signals,” Proceedings of the IEEE, Vol. 86, No. 10, pp. 1907-1926, October 1998.
The CMA algorithm (as well as the DD-LMS algorithm) is usually implemented with a gradient descent strategy in which the equalizer parameters are adapted by replacing the present equalizer parameter settings with their current values plus an error (or correction) term. See C. R. Johnson, Jr., P. Schniter, T. J. Endres, J. D. Behm, D. R. Brown, R. A. Casas, “Blind equalization using the constant modulus criterion: a review,” Proceedings of the IEEE, vol. 86, no. 10, pp. 1927-1950, October, 1998. The CMA error term itself is a cubic function of the equalizer output.
The CMA algorithm may be modified for use with an equalizer for a high definition television (HDTV) receiver. In the U.S. HDTV standard, a carrier signal is amplitude modu
Biracree Stephen L
Casas Raul A
Endres Thomas J
Hulyalkar Samir N
Schaffer Troy A
ATI Technologies Inc.
Chin Stephen
Jacobson Allan
Munoz Guillermo
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