Pulse or digital communications – Receivers – Particular pulse demodulator or detector
Reexamination Certificate
1999-04-30
2003-05-20
Phu, Phuong (Department: 2631)
Pulse or digital communications
Receivers
Particular pulse demodulator or detector
C375S262000, C714S794000
Reexamination Certificate
active
06567481
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates to the field of communications and more particularly to communications systems including equalizers used to detect coherent symbols and related methods.
BACKGROUND OF THE INVENTION
In current D-AMPS (IS-136) cellular communications systems, maximum likelihood sequence estimation (MLSE) equalizers detect coherent symbol values using forward or backward detection at the mobile terminal depending on the quality of the current and next slot synchronization words. At the base-station, backward MLSE detection used for symbols to the left of the synchronization sequence and forward detection is used for symbols to the right of the synchronization sequence. For example, MLSE equalization in the forward direction can be performed using the Viterbi algorithm as discussed in the reference by Ungerboeck entitled “Adaptive Maximum-Likelihood Receiver for Carrier-Modulated Data-Transmission Systems”, IEEE Trans. Comm., COM-22: pages 624-636, May 1974. The disclosure of this reference is hereby incorporated herein in its entirety by reference.
Overall log-likelihoods used to detect a sequence of symbols [a
0
, a
1
, . . . , a
N
] can take the form:
l
{
a
;
y
}
=
∑
n
=
0
N
[
2
ℜ
{
a
n
*
z
n
}
-
&LeftBracketingBar;
a
n
&RightBracketingBar;
2
s
0
,
n
-
∑
l
-
1
min
(
n
,
N
)
2
ℜ
{
a
n
*
s
l
,
n
a
n
-
1
}
]
.
(
1
)
In this equation, the equalization parameters z
n
and s
l,n
are described in the Ungerboeck reference, and these equalization parameters are extended to fractionally-spaced receivers in the reference by Molnar et al. entitled “A Novel Fractionally-Spaced MLSE Receiver And Channel Tracking With Side Information”, Proc. 48
th
IEEE Veh. Tech. Conf., May 1998. The disclosure of this reference is hereby incorporated herein in its entirety by reference.
The term inside the square brackets of Equation (1) forms the metric for the symbol a
n
. This approach takes advantage of the symmetry of the s
l,n
terms, and the summation of the s-terms is shown in
FIG. 1
for an example with N=2. The circles correspond to the s
0,n
terms and for each n, the s-terms to the left and to the top of the circled s
0,n
terms correspond to the terms s
l,n
for a specific value of n.
When backward equalization is used, the following log-likelihood is used to determine the metric for the Viterbi algorithm:
l
{
a
;
y
}
=
∑
n
=
0
N
[
2
ℜ
{
a
n
*
z
n
}
-
&LeftBracketingBar;
a
n
&RightBracketingBar;
2
s
0
,
n
-
∑
l
=
1
min
(
N
-
n
,
N
)
2
ℜ
{
a
n
*
s
l
,
n
+
l
*
a
n
+
l
}
]
(
2
)
Note that the first two terms in the metric are the same for either forward or backward equalization, while the summation of the s-terms is different. The summation of the s-terms for this case is shown in
FIG. 2
, again for the case of N=2. The circled terms remain the same as for the forward metric, but now the s-terms for a specific value of n are chosen as those terms to the right and below the n'th diagonal element.
For differential-QPSK modulation, the coherent symbols can be estimated using the above MLSE equalization approach. Estimates of the differential symbols and bits can then be obtained from the detected coherent symbols, while soft differential bit information can be obtained from the saved equalization metrics as discussed in the reference by Bottomley entitled “Soft Information in ADC”, Technical Report, Ericsson-GE, 1993. The disclosure of this reference is hereby incorporated herein in its entirety by reference.
For digital communications, maximum a-posterori (MAP) detection has been used as discussed in the reference by Bahl et al. entitled “Optimal Decoding of Linear Codes for Minimizing Symbol Error Rate”, IEEE Trans. Inf. Theory, IT-20: pages 284-287, March 1974. This approach is also similar to approaches discussed in the following references: Baum et al., “A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains”, Ann. Math. Statist., 41: pages 164-171, 1970; Sundberg, “An Iterative Method for Solution of the Likelihood Equations for Incomplete Data from Exponential Families”, Comm. Statist. Simulation. Comput., B5: pages 55-64, 1976; Erkurt et al., “Joint Detection and Channel Estimation for Rapidly Fading Channels”, Globecom 1992, pages 910-914, December 1992; Kaleh et al., “Joint Parameter Estimation and Symbol Detection for Linear or Nonlinear Unknown Channels”, IEEE Trans. Comm. 42(7): pages 2406-2413, July 1994; Krishnamurthy, “Adaptive Estimation of Hidden Nearly Completely Decomposable Markov Chains With Applications in Blind Equalization”, “International Journal of Adaptive Control and Signal Processing”, 8: pages 237-260,1994; Cirpan et al., “Stochastic Maximum Likelihood Methods for Semi-Blind Channel Estimation”, IEEE Signal Processing Letters, 5(1): pages 21-24, January 1998; and Baccarelli et al., “Combined Channel Fast-Fading Digital Links”, IEEE Trans. Comm., 46(4): pages 424-427, April 1998. The disclosures of each of these references are hereby incorporated herein in their entirety by reference.
The metric for MAP detection of the coherent symbols can be derived from the following overall log-likelihood:
l
{
a
;
y
}
=
∑
n
=
0
N
2
ℜ
{
a
n
*
z
n
}
-
∑
n
=
0
N
∑
m
=
0
N
a
n
*
h
n
,
m
a
m
.
(
3
)
The MAP metric is found by collecting all terms containing a
n
. For example, in
FIG. 3A
, the symbol a
1
contribution to the double sum in Equation (3) is shown by the enclosed h
n,m
terms. The h
n,m
terms are related to the s-parameters in the following manner:
h
n
,
m
=
{
s
n
-
m
,
n
n
≥
m
;
s
n
-
m
,
m
=
s
m
-
n
,
m
*
n
<
m
.
(
4
)
The corresponding terms are shown in FIG.
3
B. Using the s-parameters from
FIG. 3B
, the contribution to the MAP metric for symbol an can be written as:
l
{
a
n
;
y
}
=
2
ℜ
{
a
n
*
z
n
}
-
&LeftBracketingBar;
a
n
&RightBracketingBar;
2
s
0
,
n
-
∑
l
=
1
min
(
n
,
N
)
2
ℜ
{
a
n
*
s
l
,
n
a
n
-
l
}
-
∑
l
=
1
min
(
N
-
n
,
N
)
2
ℜ
y
{
a
n
*
s
l
,
n
*
a
n
+
l
}
.
(
5
)
The terms contributing to the double summation in Equation (3) use the same folding as in the forward and backward MLSE equalizers previously discussed.
Notwithstanding the equalizer systems and methods discussed above, there continues to exist a need in the art for improved equalizer systems and methods.
SUMMARY OF THE INVENTION
It is therefore an object of the present invention to provide improved methods and receivers for receiving data transmitted over radio communications channels.
This and other objects are provided according to the present invention by methods for receiving data transmitted over a radio communications channel wherein the data is transmitted as a plurality of sequential symbols wherein each sequential symbol is determined as a function of a previous symbol and a respective differential symbol corresponding to a portion of the data being transmitted. A plurality of received segments are sampled wherein the received segments correspond to respective ones of the transmitted symbols, and an initial differential MAP symbol estimation is performed for estimated received symbols corresponding to the sampled received segments to provide initial estimates of the differential symbols. New received symbol estimates are calculated using the initial estimates of the differential symbols, and a subsequent differential MAP symbol estimation is performed using the new received symbol estimates to provide improved estimates of the differential symbols. Bit probability calculations are performed on the improved estimates of the differential symbols. Improved data reception can thus be provided over radio
Ericsson Inc.
Myers Bigel & Sibley & Sajovec
Phu Phuong
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