Data processing: measuring – calibrating – or testing – Measurement system – Measured signal processing
Reexamination Certificate
1998-11-30
2001-11-06
Shah, Kamini (Department: 2857)
Data processing: measuring, calibrating, or testing
Measurement system
Measured signal processing
C375S341000, C714S786000
Reexamination Certificate
active
06314387
ABSTRACT:
TECHNICAL FIELD
The present invention relates to transmission of digital data as conducted in mobile telephone service and, more particularly, to a method of estimating the sequence of a transmitted signal from a received signal and from the characteristics of the channel by the receiver.
BACKGROUND ART
The background art for the present invention is described.
Prior Art Technique 1
In digital data transmission, it is normally impossible for a receiver to receive a signal transmitted by a sender as it is due to the state of the channel and noises. The transmitted signal is converted into other form by the state of the channel and by noises. This converted form is received.
A model describing conversion of a signal on a channel is illustrated in FIG.
16
. As shown in this figure, the signal is delayed on the channel. In addition, noises are adeed. Let X
k
be transmitted signal. The received signal r
k
is given by
r
k
=
∑
i
=
0
L
c
i
x
k
-
i
+
w
k
(
1
)
where L is the length of memory of the channel that delays the transmitted signal, C
i
is a tap coefficient, and w
k
is a noise sequence. The tap coefficient and the noise sequence are values determined by the characterstics of the channel.
The receiver receiving the signal r
k
estimates the transmitted signal from the received signal r
k
and from the tap coefficient c
i
.
The receiver, or a sequence estimator, convolves a candidate for the transmitted signal with known tap coefficients and calculates an estimated value (hereinafter referred to as a replica) of the received signal as follows:
r
^
k
=
∑
i
=
0
L
c
i
x
^
k
-
i
(
2
)
Furthermore, the receiver, or the sequence estimator, calculates the error power between the estimated value (replica) of the received signal calculated according to Eq. 2) and the actual received signal, using the following Eq. (3):
∑
k
|
e
k
|
2
=
∑
k
|
r
k
-
r
^
k
|
2
(
3
)
The receiver searches for a transmitted signal candidate minimizing the error power given by Eq. (3) and estimates that this candidate is the transmitted signal.
Processing for estimating a sequence where the memory length of the channel is set to L=2 is described in detail.
FIG. 17
illustrates a model of a sequence estimator that is best adapted for the case where the memory length of the channel is L=2. The sequence estimator is so constructed as to reproduce a model similar to a model of a channel. However, in the sequence estimator shown in
FIG. 17
, means for adding noises are omitted from the model of the channel.
Specifically, the sequence estimator comprises: a memory having the same memory length as the memory length for the channel and receiving estimated values of the transmitted signal; a multiplication means for multiplying an estimated value of the transmitted signal produced from the memory by given tap coefficients; an adder means for calculating an estimated value (replica) of the received signal by summing up the products produced by the multiplication means; an error-calculating means for producing the differences between the estimated value (replica) of the received signal produced from the adder means and the actual received signal; and a squares sum-calculating means for taking the sum of squares of values produced from the error-calculating means. The given tap coefficients at which the multiplication means is set are the same as the tap coefficients obtained from the characteristics of the channel.
A method of making maximum likelihood estimates by the sequence estimator described thus far is described.
First, candidates for a transmitted signal of sequence length N are obtained.
Then, the candidates for the transmitted signal are entered into the memory of the sequence estimator. The multiplication means multiplies signals produced from the memory by tap coefficients c
1
and c
2
. The signal produced without via the memory is multiplied by a tap coefficient c
0
.
The adder means sums up the values produced by the multiplication means to obtain an estimated value (replica) of the received signal.
The difference-calculating means takes the difference between the estimated value (replica) of the received signal obtained by the adder means and the actually received signal.
The squares sum-calculating means takes the sum of squares of difference values produced from the difference-calculating means. The squares sum-calculating means sums up the squares of the differences between the estimated signal (replica) of the received signal and the received signal for every signal sequence.
Let N be the length of the transmission sequence length. There are 2
N
candidates for this transmitted signal. The above-described processing is performed for every candidate.
The maximum likelihood estimator estimates that the candidate for the transmitted signal minimizing the squares sum produced from the squares sum-calculating means is the transmitted signal.
Prior Art Technique 2
In the case of the maximum likelihood estimates as described above, the amount of calculation increases in proportion to a power of the transmission sequence length N. Accordingly, maximum likelihood estimates adopting a procedure known as the Viterbi algorithm are used. The Viterbi algorithm is described in detail by G. D. Forney, Jr. in “The Viterbi algorithm”, Proc. IEEE, Vol.61, No.3, pp. 268-278 (March 1973).
In the case of the channel model of
FIG. 18
, the error electric power at instant k can be calculated if data about transmission at the present instant k and data about transmission performed two instants earlier are known.
In the maximum likelihood estimates using the Viterbi algorithm, a diagram (hereinafter referred to as a trellis diagram) describing information about transitions of data arising from combinations of data at two instants as shown in
FIG. 19
is used.
In this trellis diagram of
FIG. 19
, combinations of data at two instants of time are connected by lines, taking account of the following characteristics.
It is assumed, for example, that a signal stored in memory at some instant is in state
00
. At the next instant, the state makes a transition either to state
10
or to state
00
. However, transition to state
01
or state
11
does not occur, because if a shift register in
000
is shifted one clock time, only
000
or
100
is obtained.
Accordingly, when combinations of data at two instants are connected by lines, states
00
and
10
are connected by a line. Also, states
00
and
00
are connected by a line. No line is drawn between states
00
and
01
. No line is drawn between states
00
and
11
.
The trellis diagram is created by taking account of the characteristics of transitions in this way. In
FIG. 19
, a line connecting states indicates a possibility of a transition. No connecting line means that transition cannot occur. A line indicating a transition of state is hereinafter referred to as a branch. In the trellis diagram of
FIG. 19
, both solid lines and dotted lines are put. The solid lines indicate that signal
0
is applied, producing a transition of a state. The dotted lines indicate that signal
1
is applied, resulting in a transition of a state.
If combinations of data at two instants are connected by a line as in the trellis diagram of
FIG. 19
, a combination of data at three instants can be determined. By making use of this, the error electric power can be found.
Then, the contents of processing of the Viterbi algorithm using the trellis diagram of
FIG. 19
are described in detail.
The number of states is determined by the Viterbi algorithm in the manner described now. Let L be the transmission memory length. The number is 2
L
. That is, the number of states increases in proportion to a power of the transmission memory length L. The amount of calculation increases according to the number of states.
In the prior art technique
1
, every candidate for the transmitted signal must be searched for. In contrast, application of the Viterbi algorithm can reduce the numbe
Kubo Hiroshi
Murakami Keishi
Mitsubishi Denki Kabushiki
Shah Kamini
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