Dual path ghost eliminating equalizer with optimum noise...

Details Dual path ghost eliminating equalizer with optimum noise... Dual path ghost eliminating equalizer with optimum noise...

1999-10-22

2003-11-18

Pham, Chi (Department: 2631)

Pulse or digital communications

Equalizers

Automatic

C375S349000, C375S235000

Reexamination Certificate

active

06650700

ABSTRACT:

TECHNICAL FIELD OF THE INVENTION
The present invention is directed to an equalizer that substantially eliminates signal ghosts of up to and including 100% ghosts and, more particularly, to a dual path equalizer with optimum noise enhancement.
BACKGROUND OF THE INVENTION
Ghosts are produced in a receiver usually because a signal arrives at the receiver through different transmission paths. For example, in a system having a single transmitter, the multipath transmission of a signal may occur because of signal reflection. That is, the receiver receives a transmitted signal and one or more reflections of the transmitted signal. As another example, the multipath transmission of a signal may occur in a system having plural transmitters that transmit the same signal to a receiver using the same carrier frequency. A network which supports this type of transmission is typically referred to as a single frequency network.
When a signal reaches a receiver through two or more different transmission paths, an interference pattern results. In the frequency domain, this interference pattern is manifested by a variable signal amplitude along the frequency axis. An interference pattern which results when the ghost is 100% is shown in FIG.
1
. This interference pattern has amplitude nulls or near amplitude nulls at certain frequencies. Therefore, any information contained in the received main signal in the neighborhood of these frequencies is likely lost because the signal to noise ratio near these frequencies is below a usable threshold.
A variety of systems have been devised to deal with the problems caused by ghosts. For example, spread spectrum systems deal very adequately with the problem of a 100% ghost by spreading the transmitted data over a substantial bandwidth. Accordingly, even though a 100% ghost means that some information may be lost in the neighborhood of frequencies corresponding to the amplitude nulls, a data element can still be recovered because of the high probability that it was spread over frequencies which do not correspond to the amplitude nulls. Unfortunately, the data rate R associated with spread spectrum systems is typically too low for many applications. (The data rate R is defined as the number of data bits per Hertz of channel bandwidth.)
It is also known to use a matched filter in a receiver in order to deal with the problem of a ghost. In this approach, data is transmitted as a data vector. The matched filter correlates the received data with reference vectors corresponding to the possible data vectors that can be transmitted. Correlation of the received main signal to the reference vector corresponding to the transmitted data vector produces a large peak, and correlation of the received main signal to the other possible reference vectors produces small peaks. Accordingly, the transmitted data vector can be easily determined in the receiver. Unfortunately, the data rate R typically associated with the use of matched filters is still too low for many applications.
When high data rates are required, equalizers are often used in a receiver in order to reduce ghosts of a main signal. A classic example of a time domain equalizer is an FIR filter. An FIR filter convolves its response h(t), shown generally in
FIG. 2
, with a received signal. The received signal contains the main signal and the ghost of the main signal. The FIR filter produces an output having a large peak representative of the main signal. Ghosts of the main signal have small components in the output of the FIR filter. However, as shown in
FIG. 2
, the values a
1
, a
2
, a
3
, . . . of the taps of an FIR filter depend on the value of a and, in order to perfectly cancel a 100% ghost using an FIR filter, the value a of the FIR filter response must approach 1. As the value a approaches 1, the values of the taps of the FIR filter do not asymptotically decrease toward zero. Therefore, the FIR filter becomes infinitely long if a 100% ghost is to be eliminated, making the FIR filter impractical to eliminate a 100% ghost.
An example of a frequency domain equalizer
10
is shown in FIG.
3
. The frequency domain equalizer
10
includes a Fast Fourier Transform (FFT) module
12
which performs a Fast Fourier Transform on the received signal in order to transform the received signal to the frequency domain. A multiplier
14
multiplies the frequency domain output of the FFT module
12
by a compensation vector which includes a row of coefficients A
i
. An inverse FFT module
16
performs an inverse FFT on the multiplication results from the multiplier
14
in order to transform the multiplication results to the time domain.
FIG. 4
illustrates an exemplary set of coefficients A
i
which may be used by the frequency domain equalizer
10
. The coefficients A
i
are chosen so that, when they and the FFT of the received signal are multiplied by the multiplier
14
, the coefficients A
i
cancel the ghost in the received signal leaving only the main signal. It should be noted that the coefficients A
i
should have infinite amplitudes at the frequencies where the interference pattern has a zero amplitude. However, the coefficients A
i
cannot be made infinite as a practical matter. Accordingly, the coefficients A
i
are cut off at these frequencies, which means that information in the received main signal is lost at the cut off frequencies so that the output of the inverse FFT module
16
becomes only an approximation of the transmitted data.
Moreover, it is known to use empty guard intervals between the vectors employed in the frequency domain equalizer
10
of FIG.
3
. The guard intervals are shown in FIG.
5
and are provided so that received vectors and ghosts of the received vectors do not overlap because such an overlap could otherwise cause intersymbol interference. Thus, the guard intervals should be at least as long as the expected ghosts. It is also known to use cyclic extensions of the vectors in order to give the received main signal an appearance of periodicity. Accordingly, a Fast Fourier Transform of the received signal and a Fourier Transform of the received signal appear identical.
U.S. application Ser. No. 09/158,730 filed Sep. 22, 1998now U.S. Pat. No. 6,442,221 discloses a vector domain equalizer which effectively eliminates ghosts up to 100% by distributing the transmitted data in both time and frequency so that the vectors are essentially random in the time and frequency domains. Accordingly, in a heavily ghosted channel, all data can be recovered with small noise enhancement, and any enhanced noise that does exist is near white.
As shown in
FIG. 6
, the vector domain equalizer
20
disclosed in this application includes an inverse vector domain transform
22
and a vector domain transform
24
which are separated by a channel
26
. The inverse vector domain transform
22
performs a matrix multiplication between an input data block and a transform matrix so as to distribute each data element in the input data block to each element in an output data block. The vector domain transform
24
performs a matrix multiplication between the received signal and a plurality of receiver vectors. The vectors of the vector domain transform
24
are adjusted according to channel distortion such that, in the presence of channel distortion, the data of the original input data block is recovered.
The invention of U.S. application Ser. No. 09/158,730 now U.S. Pat. No. 6,442,221 works quite well. However, the number of calculations performed by the transforms shown in
FIG. 6
increases in accordance with n
2
as n increases, where n is the number of data elements in a data block. U.S. application Ser. No. 09/283,877 filed Apr. 1, 1999 discloses an equalizer which effectively eliminates ghosts up to 100% but which uses fewer calculations. This equalizer
30
is shown in FIG.
7
and includes a pre-processor
32
, a finite filter
34
, and a post-processor
36
. The finite filter
34
may be implemented as a Fast Fourier Transform, a multiplier, and an inverse Fast Fourier Transform. The pre-processor
32
of th

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