Pulse or digital communications – Receivers – Particular pulse demodulator or detector
Reexamination Certificate
2001-06-13
2002-10-29
Tse, Young T. (Department: 2634)
Pulse or digital communications
Receivers
Particular pulse demodulator or detector
C375S262000, C375S346000, C714S795000
Reexamination Certificate
active
06473472
ABSTRACT:
TECHNICAL FIELD
The present invention in general relates to an adaptive array communication system that removes an unnecessary signal by using an adaptive array technique. More particularly, this invention relates to an adaptive array communication system and a receiver capable of realizing an improvement in demodulation characteristic by efficiently removing interference in a fading environment of a land mobile communication or the like.
BACKGROUND ART
A conventional receiver will be described below. For example, a conventional receiver using an adaptive array antenna technique which is one of techniques for improving bit error rate performance in a receiver will be described below.
As a technique related to a conventional receiver, for example, “Adaptive Array for Mobile Radio” (Ohgane, Ogawa, November, 1998 to March, 1999) described in Journal of The Institute of Electronics, Information and Communication Engineers is known. The adaptive algorithm itself is described in a reference “Introduction to Adaptive Filters” (S. Haykin, translated by Tsuyoshi TAKEBE, Gendai Kogaku sha, Third Edition, on Mar. 10, 1994).
For example, in land mobile communication such as a portable telephone system, a base station is installed corresponding to each one of a plurality of areas. These areas are generally called as cells. Mobile stations existing in a cell communicate with the base station of that cell. In this case, for a mobile station in the cell, a radio wave from the base station in the same cell is a desired wave. Similarly, for the base station in the cell, a radio wave from the mobile station in the same cell is a desired wave. However, a mobile station existing near a boundary of another cell receives interference from a mobile station existing in another cell using the same frequency and a base station of another cell which communicates with the mobile station. Since the transmission power of a base station is generally higher than the transmission power of a mobile station, the base stations receive the interference the most.
In such a case, in an adaptive array communication system, a plurality of antennas (array) are used, and the directivity of the array is adaptively controlled, so that the process of removing an interference wave except for a desired wave and the process of combining a plurality of desired waves reflected from buildings or the like and having different arrival times are performed. More specifically, a directivity (beam) is turned in a desired wave direction, and a point where the beam is 0 (null) is turned in an interference direction to remove an interference wave. A plurality of beams are turned toward a plurality of desired waves to equalize delays and to combine desired waves, so that a preferable characteristic is realized.
FIG. 15
is a diagram showing the configuration of a conventional receiver. In
FIG. 15
, as one example of the conventional receiver, an adaptive array communication system using mean square error (MSE) criteria is shown. In this receiver, received signals from a plurality of antennas (branches) are weighted by coefficients (complex weights) which are different from each other for the respective branches, and a signal is combined. At this time, in the adaptive array communication system, on the basis of the received signals from the branches and the combined signal (array output), a such optimum complex weight that a desired wave is increased and an unnecessary wave is decreased in the array output is determined.
The details of the operation of the conventional receiver will be described below. In the adaptive array communication system of N branches (N: a natural number of
2
or more) using the MSE criteria shown in
FIG. 15
, an optimum complex weight w
j
is determined by using a known reference signal d
i
included in the received signals. First, inputs X
j,i
(j is a branch number: j=1 to N and i is an integer representing a symbol timing) of branches are multiplied by the complex weights w
j
output from an adaptive control unit
181
in a multiplier. Signals multiplied by the complex weights w
j
are input into an adder
182
and added (combined) to each other to calculate an array output y
i
. More specifically,
y
i
=
∑
j
=
1
N
w
j
x
j
,
i
(
1
)
is satisfied.
Thereafter, the array output y
i
is input into a demodulation unit (not shown) and input into an adder
183
to be compared with the known reference signal d
i
. As a result, an error signal &egr;
i
is output from the adder
183
. More specifically,
&egr;
i
=d
i
−Y
i
(2)
is satisfied.
The error signal &egr;
i
output from the adder
183
is, thereafter, input into the adaptive control unit
181
. In the adaptive control unit
181
, by using the error signal &egr;
i
, the complex weight w
j
is controlled on the basis of an adaptive algorithm. For example, when an LMS (Least Mean Square) as the adaptive algorithm, the complex weight is changed (controlled) by the following equation:
w
j,i+1
=w
j,i
+2&mgr;x
j,1
*&egr;
i
(3)
Note that w
j,i
represents the complex weight w
j
including a symbol timing. In the following, * represents a complex conjugate.
According to Equations (1) , (2), and (3), the complex weight w
j
is controlled to such an optimum value that a desired wave is maximum and an interference wave is minimum in the array output y
i
. In this manner, since the signal controlled to the optimum value is demodulated, the adaptive array communication system can improve bit error rate performance in the array output y
i
. Therefore, for example, when the adaptive array communication system shown in
FIG. 15
is applied to a system using convolutional coding/viterbi decoding as shown in
FIG. 16
, the bit error rate performance in the array output y
i
is improved. For this reason, bit error rate performance in a decoded output of a viterbi decoder can be improved.
In addition, as a conventional receiver different from the above receiver, for example, an adaptive array communication system using a decision feedback loop is known.
FIG. 17
is a diagram showing the configuration of a conventional receiver using a decision feedback loop. In this case, the complex weight w
j
is updated by an adaptive algorithm using data except for the known reference signal d
i
. The same reference numerals as in the configuration in
FIG. 15
denote the same parts in the configuration in
FIG. 17
, and a description thereof will be omitted. In this receiver, for example, a method of deciding an array output y
i
by a decision unit
191
and selecting an output d
i
′ from the decision unit
191
as a reference signal by a selection unit
192
to calculate an error signal ∈
i
′ is used. More specifically, when an LMS algorithm is applied, by the following equation:
W
j,i+1
=W
j,i
+2&mgr;x
j,i
*∈
i
′ ∈
i
′=d
i
′−y
i
(4)
the complex weight w
j
is calculated. In this manner, since a signal controlled to an optimum value, the adaptive array communication system can improve a bit error rate performance in an array output y
i
. In addition,an error signal ∈
i
′ can be calculated even in a period having no reference signal d
i
, and the LMS algorithm can be operated at a high accuracy.
Therefore, for example, when an adaptive array communication system using the decision feedback loop shown in
FIG. 17
is applied to a system using convolutional coding/viterbi decoding as shown in
FIG. 18
, as shown in
FIG. 18
, re-encoded data of viterbi decoder output is assumed as a reference signal d
i
′. The data is fedback to the adaptive array, so that the error signal &egr;
i
′ can be calculated. As a result, bit error rate performance in a decoded output of a viterbi decoder can be further improved.
However, in the conventional receiver as shown in
FIG. 15
, since calculation represented by Equation (3) is repeated until the complex weight w
j
converges to an optimum value by the adaptive algorithm, the
Kojima Toshiharu
Uchiki Tatsuya
Mitsubishi Denki & Kabushiki Kaisha
Tse Young T.
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