Pulse or digital communications – Receivers – Interference or noise reduction
Reexamination Certificate
1998-07-31
2004-08-03
Tse, Young T. (Department: 2634)
Pulse or digital communications
Receivers
Interference or noise reduction
C375S232000, C375S340000
Reexamination Certificate
active
06771722
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates generally to channel estimators for use in radio communication devices, such as radiotelephones and, more particularly, relates to a method and apparatus for initializing a channel estimator. In a preferred embodiment described herein, the channel estimator is a predictive least mean squares channel estimator.
2. Description of the Related Art
Adaptive channel estimators track the channel impulse response, represented by H(n), using received channel samples and symbols decoded by a detector such as an Ungerboeck Maximum Likelihood Sequence Estimator (MLSE). In Time Division Multiplex (TDM) systems such as the North American Digital Cellular (NADC) system, there is a synchronous codeword at the beginning of each frame of data. Typically the synchronous codeword is used to initialize an adaptive channel estimator. Two conventional approaches used to initialize the adaptive channel estimator are channel sounding, described with reference to
FIG. 1
, and training using the synchronous codeword, described with reference to FIG.
2
.
FIG. 1
illustrates a block diagram of an equalizer
10
and a corresponding data stream
11
using channel sounding followed by training to perform channel initialization, in accordance with the prior art. The equalizer
10
generally includes a channel sounding block
12
, a matched filter
13
, a channel estimator
14
and a maximum likelihood sequence estimator (MLSE)
15
. The data stream
11
represents the samples received by the equalizer
10
and generally includes a synchronous codeword
16
followed by data
17
, as is well known in the art. The operation of the equalizer
10
responsive to receiving the data stream
11
is well known in the art. The channel sounding approach requires multiple complex correlations of received in-phase (I) and quadrature-phase (Q) samples with the synchronous codeword to produce an initial channel estimate H (0), as is well known in the art.
FIG. 2
illustrates a block diagram of an equalizer
20
and a corresponding data stream
21
using training to perform channel initialization, in accordance with the prior art. The equalizer
20
generally includes a matched filter
22
, a channel estimator
23
and a maximum likelihood sequence estimator (MLSE)
24
. The data stream
21
represents the samples received by the equalizer
20
and generally includes a synchronous codeword
25
followed by data
26
, as is well known in the art. The operation of the equalizer
20
responsive to receiving the data stream
21
is well known in the art. To perform channel initialization via the training approach, as illustrated in
FIG. 2
, the channel pulse response, Ĥ (−14), is set to an arbitrary constant (e.g. the all-zero vector) and the channel estimator is operated using the known symbols of the synchronous codeword
25
. The goal is to have the channel estimator
23
converge to the actual channel response by the time data
26
is input to the channel estimator
23
at time n=0.
Each of these approaches has its drawbacks, especially when the channel estimator
14
in
FIG. 1
or the channel estimator
23
in
FIG. 2
is a predictive Least Mean Squares (LMS) adaptive filter. The LMS adaptive filter has essentially two estimators: one estimator for the channel response (i.e. the LMS estimator), and one estimator for the rate of change of the channel response (i.e. the predictor estimator). Each of these estimators must be initialized at the beginning of each frame.
In light of these two conventional approaches, conventional channel sounding alone is a sub-optimal technique of initialization because it initializes the LMS estimator but not the predictor estimator. Training alone, as described with
FIG. 2
, is a sub-optimal technique because training the LMS estimator (from a constant zero) tends to incorrectly train the predictor estimator, and there are not enough symbols in the synchronous codeword to compensate for this with conventional training. Further, channel sounding followed by training, as described with
FIG. 1
, helps somewhat, but this solution requires excessive hardware and current drain. For example, even if channel sounding followed by training was accomplished with a significant amount of hardware reuse, channel sounding would still require about 25,000 gates. Accordingly, there is a need for a method for initializing a predictive least mean squares channel estimator that solves the problem of initializing the LMS estimator and the predictor estimator to improve performance while minimizing hardware and current drain.
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Diehl John W.
Kazecki Henry L.
Chen Sylvia Y.
Loppnow Matthew C.
Motorola Inc.
Tse Young T.
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