KR101585675B1 - Maximum likelihood detecting receiver using iterative algorithm for transmit diversity mode and maximum likelihood detecting method thereof - Google Patents

Maximum likelihood detecting receiver using iterative algorithm for transmit diversity mode and maximum likelihood detecting method thereof Download PDF

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KR101585675B1
KR101585675B1 KR1020140096833A KR20140096833A KR101585675B1 KR 101585675 B1 KR101585675 B1 KR 101585675B1 KR 1020140096833 A KR1020140096833 A KR 1020140096833A KR 20140096833 A KR20140096833 A KR 20140096833A KR 101585675 B1 KR101585675 B1 KR 101585675B1
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channel estimation
rti
estimate
estimation error
transmission
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Korean (ko)
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백종현
최현영
김민수
김주엽
김용규
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한국철도기술연구원
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Abstract

The present invention relates to an ML detection receiver using an iterative algorithm for transmit diversity, and its configuration is characterized in that in a wireless communication system for transmission diversity transmission, a transmission symbol transmitted from two or more transmission antennas at a k-

Figure 112014071990544-pat00525
) Received from the i < th > receiving antenna of the M receiving antennas
Figure 112014071990544-pat00526
), Channel estimation (
Figure 112014071990544-pat00527
) Channel estimation error (
Figure 112014071990544-pat00528
), And outputs the substantial noise signal (
Figure 112014071990544-pat00529
) And the channel estimation error (
Figure 112014071990544-pat00530
), The ML estimate (
Figure 112014071990544-pat00531
), The ML receiver comprising: a reception signal generator
Figure 112014071990544-pat00532
); The channel estimate (H) for the channel < RTI ID = 0.0 >
Figure 112014071990544-pat00533
); The channel estimate (
Figure 112014071990544-pat00534
The channel estimation error < RTI ID = 0.0 >
Figure 112014071990544-pat00535
A channel estimation error part for calculating a channel estimation error; The received signal (
Figure 112014071990544-pat00536
), Channel estimation
Figure 112014071990544-pat00537
), Channel estimation error (
Figure 112014071990544-pat00538
),
Figure 112014071990544-pat00539
The initial value of the real noise signal is obtained, and the covariance matrix (
Figure 112014071990544-pat00540
), And then, using this,
Figure 112014071990544-pat00541
Lt; RTI ID = 0.0 > (ML) < / RTI > estimate
Figure 112014071990544-pat00542
); .
According to the present invention, an ML detection receiver robust against an interference signal such as a channel estimation error and a jamming signal is designed, thereby reducing the influence on the channel estimation error and the interference signal.

Description

[0001] The present invention relates to an ML detection method using an iterative algorithm for transmission diversity transmission and an ML detection method for the receiver. [0002]

More particularly, the present invention relates to an ML detection method using an iterative algorithm for transmission diversity transmission and an ML detection method of the ML detection method.

Transmit diversity is a technique for obtaining a diversity gain by transmitting a signal using a plurality of transmit antennas.

In the case of transmit diversity using two antennas, as shown in FIG. 1,

Figure 112014071990544-pat00001
and
Figure 112014071990544-pat00002
Space-time coding scheme suitable for the mobile station. At this time, two symbols are transmitted for two symbol times, and the average number of transmission symbols per symbol time is one. Therefore, the two antennas have a spectral efficiency of half compared with MIMO in which two symbols are simultaneously transmitted.

In the case of transmit diversity using four antennas, a variety of space-time encoding schemes are possible, and a technique applied to LTE (Long Term Evolution) technology as a mobile communication technology is shown. As shown in FIG. 2, Symbol, and the number of average transmission symbols per symbol time is one. Therefore, it has a frequency efficiency of 1/4 of that of MIMO (Multiple Input Multiple Output) technique in which four symbols are simultaneously transmitted from four antennas.

In addition, although transmit diversity has a lower frequency efficiency performance than MIMO, since one symbol is transmitted through several antennas, it has a higher diversity gain, and therefore, a technique that is mainly used when a stable signal transmission is required in a poor communication environment to be.

Another advantage of transmit antenna diversity is that the complexity of the receiver is low. The space-time coding applied to the transmit antenna diversity has orthogonal characteristics. Accordingly, the ML (Maximum Likelihood) receiver Can be obtained. That is, optimal detection of transmit diversity can be performed with a linear receiver of low complexity.

Here, the maximum likelihood (ML) detection method is a decoding rule that determines that a conditional probability (likelihood) is maximized. As a decoding rule, among all signals that can be transmitted from a noise- Means the detection method of determining the highest signal as the transmission signal.

On the other hand, when there is a high-power interference signal such as a jamming signal, it has a low signal-to-interference-plus-noise ratio (SINR). Accordingly, the use of the transmit diversity transmission mode is more preferable, and the design of the interference cancellation receiver for transmit diversity is particularly important. At this time, there is a need for a technique for mitigating interference while maintaining low reception complexity, which is an advantage of transmit diversity.

Research on interference signals has been mainly carried out on interference cancellation and mitigation methods in general mobile communication systems. However, there have been studies on interference mitigation methods in environments where channel estimation is difficult, such as high-speed mobile environments. However, (S. Shahbazpanahi, et al., Robust Adaptive Beamforming for General-Rank Signal Model, IEEE Trans. On Signal Processing, Vol. 51, pp. 2257-2269, Sep. 2003.).

Recently, research has been actively conducted on interference adjustment methods for mitigating the influence of interference between cells or between users in a wireless communication network (V. Cadambe and S. Jafar, Interference Alignment and Degrees of Freedom of the K-user Interference Channel , IEEE Trans. Inf. Theory, vol. 54, no. 8, pp. 34253441, 2008. S. Jafar and S. Shamai, Degrees of freedom region of the MIMO X channel, IEEE Trans. , No. 1, pp. 151170, 2008.) This is a method which can not be applied to an interference signal which can not process a transmission signal, such as a jamming signal, since it is assumed that an arbitrary signal processing technique is applied when an interference signal is transmitted.

In addition, in the existing researches on the communication method in the existence of the jamming signal, the interference averaging method based on the code division method without beam forming signal processing is mainly performed, and the interference mitigation gain is low and the performance is focused on the analysis. Al-Dweik and A. Shami, Multitone Jamming Rejection of Frequency Hopped OFDM Systems in Wireless Channels, IEEE VTC Fall, 2012.).

However, there are few studies on the design of high-efficiency interference mitigation receiver in the presence of high-power interference signal such as jamming signal. Especially, Since there is no research result, in order to receive a stable signal in the transmission diversity transmission mode, a receiver robust against a channel estimation error due to high power interference and high-speed movement is required.

Korean Patent Registration No. 10-0630108 (transceiver supporting transmit antenna diversity using space-time block code, 2006.09.22) Korean Patent No. 10-0720872 (Transmission / reception apparatus and method for implementing space-time block coding apparatus and method for improving performance, May.05, 2006) Korean Patent No. 10-0605860 (transmission apparatus and method of a wireless communication system using four transmission antennas, 2006.07.20)

S. Shahbazpanahi, et al., Robust Adaptive Beamforming for General-Rank Signal Model, IEEE Trans. on Signal Processing, Vol. 51, pp. 2257-2269, Sep. 2003. V. Cadambe and S. Jafar, Interference alignment and degrees of freedom of the K-user interference channel, IEEE Trans. Inf. Theory, vol. 54, no. 8, pp. 34253441, 2008. S. Jafar and S. Shamai, Degrees of freedom region of the MIMO X channel, IEEE Trans. Inf. Theory, vol. 54, no. 1, pp. 151170, 2008. A. Al-Dweik and A. Shami, Multitone Jamming Rejection of Frequency Hopped OFDM Systems in Wireless Channels, IEEE VTC Fall, 2012.

SUMMARY OF THE INVENTION The present invention has been made in order to solve the above-mentioned problems, and it is an object of the present invention to provide an apparatus and method for transmitting diversity using a repetitive algorithm for transmission diversity transmission capable of estimating ML estimates considering channel estimation errors and interference signals in a wireless communication system environment for transmission diversity transmission ML detector and a method of ML detection of the receiver.

The technical objects of the present invention are not limited to the technical matters mentioned above, and other technical subjects not mentioned can be clearly understood by those skilled in the art from the following description.

The ML detection receiver using the iterative algorithm for transmit diversity transmission in the wireless communication system for transmit diversity transmission is characterized in that a transmit symbol transmitted from two or more transmit antennas at k-

Figure 112014071990544-pat00003
) Received from the i < th > receiving antenna of the M receiving antennas
Figure 112014071990544-pat00004
), Channel estimation (
Figure 112014071990544-pat00005
) Channel estimation error (
Figure 112014071990544-pat00006
), And outputs the substantial noise signal (
Figure 112014071990544-pat00007
) ≪ / RTI >
Figure 112014071990544-pat00008
), The ML estimate (
Figure 112014071990544-pat00009
), Characterized in that the reception signal from the transmission antenna
Figure 112014071990544-pat00010
); The channel estimate (H) for the channel < RTI ID = 0.0 >
Figure 112014071990544-pat00011
); The channel estimate (
Figure 112014071990544-pat00012
The channel estimation error < RTI ID = 0.0 >
Figure 112014071990544-pat00013
A channel estimation error part for calculating a channel estimation error; The received signal (
Figure 112014071990544-pat00014
), Channel estimation
Figure 112014071990544-pat00015
), Channel estimation error (
Figure 112014071990544-pat00016
),
Figure 112014071990544-pat00017
The initial value of the real noise signal is obtained, and the covariance matrix (
Figure 112014071990544-pat00018
), And then, using this,
Figure 112014071990544-pat00019
Lt; RTI ID = 0.0 > (ML) < / RTI > estimate
Figure 112014071990544-pat00020
The ML estimating unit calculates the ML of the received signal.

The ML detection method of an ML detection receiver using an iterative algorithm for transmit diversity transmission according to the present invention is characterized in that in a wireless communication system for transmission diversity transmission, a transmission symbol transmitted from two or more transmission antennas at a k-

Figure 112014071990544-pat00021
) Received from the i < th > receiving antenna of the M receiving antennas
Figure 112014071990544-pat00022
), Channel estimation (
Figure 112014071990544-pat00023
) Channel estimation error (
Figure 112014071990544-pat00024
), And using the received signal including the substantial noise signal, the ML estimate (
Figure 112014071990544-pat00025
(ML) detection method of an ML detection receiver for calculating an ML number
Figure 112014071990544-pat00026
Receiving and storing the input data; Current iteration count (
Figure 112014071990544-pat00027
) Is initialized to 0, and the received signal (
Figure 112014071990544-pat00028
), A transmission channel estimate (
Figure 112014071990544-pat00029
) And channel estimation error (
Figure 112014071990544-pat00030
)
Figure 112014071990544-pat00031
Of ML estimates (
Figure 112014071990544-pat00032
And storing the initial value as an initial value; The initial value (
Figure 112014071990544-pat00033
) Is used to estimate the channel estimation error
Figure 112014071990544-pat00034
) For an instantaneous covariance matrix (
Figure 112014071990544-pat00035
), And using this,
Figure 112014071990544-pat00036
Of
Figure 112014071990544-pat00037
, And
Figure 112014071990544-pat00038
The ML estimate (
Figure 112014071990544-pat00039
); Current iteration count (
Figure 112014071990544-pat00040
) Is set to the number of times of repeated execution (
Figure 112014071990544-pat00041
), ≪ / RTI > As a result of the determination,
Figure 112014071990544-pat00042
), The algorithm is terminated, and the calculated ML estimate value (
Figure 112014071990544-pat00043
) As a final value, using an iterative algorithm including the ML estimate < RTI ID = 0.0 > (
Figure 112014071990544-pat00044
). ≪ / RTI >

According to the ML detection method using the repetitive algorithm for transmission diversity transmission and the ML detection method of the receiver according to the present invention, by designing the ML detection receiver using the iterative algorithm robust to interference signals such as channel estimation error and jamming signal, It has the effect of reducing the influence on the estimation error and the interference signal.

1 is a conceptual diagram illustrating signal transmission when two transmit antennas are used in a conventional transmit diversity transmission mode.
2 is a conceptual diagram illustrating signal transmission when four transmit antennas are used in a conventional transmit diversity transmission mode.
3 is a diagram for explaining a configuration of an ML detection receiver using a repetitive algorithm for transmission diversity transmission according to a preferred embodiment of the present invention.
4 is a flowchart illustrating an ML detection method of an ML detection receiver according to a preferred embodiment of the present invention.
FIG. 5 is a diagram illustrating a simulation result when E = 6.4 of an ML detection receiver for 4 × 4 transmit diversity according to a preferred embodiment of the present invention.
FIG. 6 is a block diagram of an ML detection receiver for 4 × 4 transmit diversity according to a preferred embodiment of the present invention.

Figure 112015109453620-pat00045
= 0.4, the simulation result is shown.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. Prior to this, terms and words used in the present specification and claims should not be construed as limited to ordinary or dictionary terms, and the inventor should appropriately interpret the concepts of the terms appropriately The present invention should be construed in accordance with the meaning and concept consistent with the technical idea of the present invention.

Therefore, the embodiments described in this specification and the configurations shown in the drawings are merely the most preferred embodiments of the present invention and do not represent all the technical ideas of the present invention. Therefore, It is to be understood that equivalents and modifications are possible.

The expression in the following formula has the following meaning.

Figure 112014071990544-pat00046
: Conjugate matrix

Figure 112014071990544-pat00047
: Transpose matrix

Figure 112014071990544-pat00048
: Conjugate Transpose matrix

Figure 112014071990544-pat00049
: Euclidean norm

Figure 112014071990544-pat00050
: Identity matrix

3, the ML detection receiver is provided on the reception side and includes a signal processing unit 210, a channel estimation unit 220, A channel estimation error unit 230, and an ML estimation unit 240.

First, transmit diversity using two antennas is performed by using two symbols

Figure 112014071990544-pat00051
,
Figure 112014071990544-pat00052
From the transmitting-side antenna 100 using a space-time coding scheme. During a given symbol time (when the subcarrier is n on the OFDM system)
Figure 112014071990544-pat00053
And the transmit antenna # 2
Figure 112014071990544-pat00054
Lt; / RTI > During the next symbol time (subcarrier n + 1), the first transmit antenna
Figure 112014071990544-pat00055
, The transmit antenna # 2
Figure 112014071990544-pat00056
To the receiving side.

At this time,

Figure 112014071990544-pat00057
,
Figure 112014071990544-pat00058
And a signal received for two symbol times in the receiving antenna 200 of the receiving end
Figure 112014071990544-pat00059
) Is expressed as follows.

Figure 112015109453620-pat00060

here,

Figure 112014071990544-pat00061
Denotes a noise signal at a receiving end in a k-th symbol time.

As described above, the reception signal ("

Figure 112014071990544-pat00062
May be transmitted to the ML estimator 240 by the signal processor 210 and may be transmitted to the ML estimator 240 by the channel estimator 220,
Figure 112014071990544-pat00063
And may be transmitted to the ML estimator 240 and may be transmitted from the channel estimation error unit 230 to the transmission channel estimation error
Figure 112014071990544-pat00064
) May also be transmitted to the ML estimator 240.

In addition, when an interference signal such as a jamming signal is received from the signal processing unit 210, the channel estimation unit 220 performs channel estimation on the interference signal

Figure 112014071990544-pat00065
) And a channel estimation error unit 230 for the interference signal
Figure 112014071990544-pat00066
) May also be transmitted to the ML estimator 240. [

The ML estimator 240 estimates the received signal

Figure 112014071990544-pat00067
), Channel estimation
Figure 112014071990544-pat00068
), Channel estimation error (
Figure 112014071990544-pat00069
), And calculates a real noise signal using the received signal including the real noise signal
Figure 112014071990544-pat00070
The initial value of the real noise signal is obtained, and the covariance matrix (
Figure 112014071990544-pat00071
), And then, using this,
Figure 112014071990544-pat00072
By an iterative algorithm that computes the value of the ML estimate (
Figure 112014071990544-pat00073
).

A detailed description for calculating the ML estimate value by the iterative algorithm according to the above configuration will be described later.

On the other hand, even when four transmission antennas are used, if the transmission diversity scheme is applied,

Figure 112014071990544-pat00074
, 0,
Figure 112014071990544-pat00075
, And a signal of 0 is transmitted. During the next symbol time
Figure 112014071990544-pat00076
, 0,
Figure 112014071990544-pat00077
, And a signal of 0 is transmitted. Then 0,
Figure 112014071990544-pat00078
, 0,
Figure 112014071990544-pat00079
0 " for the next symbol time,
Figure 112014071990544-pat00080
, 0,
Figure 112014071990544-pat00081
Respectively.

The reception signal of the transmit diversity using the four transmit antennas

Figure 112014071990544-pat00082
) Is expressed as the following equation.

Figure 112015109453620-pat00083

Now, the reception signal of 2 x 1 transmit diversity using the above two antennas

Figure 112014071990544-pat00084
) Is expressed by the following equation. ≪ EMI ID = 1.0 >

Figure 112014071990544-pat00085

Here, the transmission symbol vector

Figure 112014071990544-pat00086
, The channel matrix
Figure 112014071990544-pat00087
, The noise signal
Figure 112014071990544-pat00088
, The following reception signal (
Figure 112014071990544-pat00089
) Can be obtained.

Figure 112014071990544-pat00090

Channel matrix (

Figure 112014071990544-pat00091
) Is the channel matrix estimate < RTI ID = 0.0 >
Figure 112014071990544-pat00092
) To the error matrix (
Figure 112014071990544-pat00093
) Is added, the channel matrix estimate is < RTI ID = 0.0 >
Figure 112014071990544-pat00094
in
Figure 112014071990544-pat00095
And can be represented by the following equation.

Figure 112014071990544-pat00096

If the reception signal equation in the case of one reception antenna expressed by Equation (5) is extended to a case where there are M reception antennas, the following equation can be expressed as a matrix.

Figure 112014071990544-pat00097

Here, the reception signal of the i < th >

Figure 112014071990544-pat00098
, And the channel estimation matrix and the channel estimation error matrix of the i < th >
Figure 112014071990544-pat00099
,
Figure 112014071990544-pat00100
.

The channel estimation matrix and the channel estimation error matrix have orthogonality characteristics as shown in the following equations.

Figure 112014071990544-pat00101

Figure 112014071990544-pat00102

here,

Figure 112014071990544-pat00103
The
Figure 112014071990544-pat00104
Lt; / RTI >
Figure 112014071990544-pat00105
The
Figure 112014071990544-pat00106
, And I denotes an identity matrix.

In Equation (7)

Figure 112014071990544-pat00107
Wow,
Figure 112014071990544-pat00108
Are defined as follows.

Figure 112014071990544-pat00109
,
Figure 112014071990544-pat00110
The entire received signal (
Figure 112014071990544-pat00111
) Can be expressed as the following equation.

Figure 112014071990544-pat00112

Here,

Figure 112014071990544-pat00113
of
Figure 112014071990544-pat00114
, The following expression can be expressed.

Figure 112014071990544-pat00115

Noise signal

Figure 112014071990544-pat00116
The average power of each circle of
Figure 112014071990544-pat00117
, symbol
Figure 112014071990544-pat00118
The average symbol power of each circle of
Figure 112014071990544-pat00119
, And an instantaneous covariance matrix of the channel estimation error is represented by
Figure 112014071990544-pat00120
The covariance matrix of the total real noise signal can be expressed as follows.

Figure 112014071990544-pat00121

At this time, the real noise signal (

Figure 112014071990544-pat00122
) Follows the normal distribution, and the instantaneous covariance matrix of the channel estimation error (
Figure 112014071990544-pat00123
)silver
Figure 112014071990544-pat00124
And that the channel estimation error is approximated to a diagonal matrix by the independence between the orthogonality and the channel estimation error,

Figure 112014071990544-pat00125
The value of the i-th diagonal component of
Figure 112014071990544-pat00126
Can be expressed as follows.

Figure 112014071990544-pat00127

In Equation (11)

Figure 112014071990544-pat00128
Because of,
Figure 112014071990544-pat00129
Can be represented by a diagonal matrix as follows.

Figure 112014071990544-pat00130

Assuming that the sum of the channel estimation error powers has a maximum value limit such as the following equation, the following equation is established.

Figure 112014071990544-pat00131

From the above equation (13), the following equation is established.

Figure 112014071990544-pat00132

Transmit symbol

Figure 112014071990544-pat00133
The ML (maximum likelihood) estimate of
Figure 112014071990544-pat00134
Can be expressed as follows.

Figure 112014071990544-pat00135

Figure 112014071990544-pat00136

here,

Figure 112014071990544-pat00137
to be.

now,

Figure 112014071990544-pat00138
Of the orthogonal characteristic
Figure 112014071990544-pat00139
, It can be expressed as the following expression.

Figure 112014071990544-pat00140

In Equation (16)

Figure 112014071990544-pat00141
, The following equation can be derived.

Figure 112015109453620-pat00142

The above Equation (17)

Figure 112014071990544-pat00143
Estimate of
Figure 112014071990544-pat00144
Can be finally expressed as follows.

Figure 112014071990544-pat00145

Now, in the equation of the above equation (18), the cost function is minimized

Figure 112014071990544-pat00146
.

Figure 112014071990544-pat00147
, It is difficult to optimize
Figure 112014071990544-pat00148
, It is assumed that the optimal
Figure 112014071990544-pat00149
. At this time, the following formula for the power sum of the channel estimation error is used.

Figure 112014071990544-pat00150

In the formula of the above formula (18)

Figure 112014071990544-pat00151
To
Figure 112014071990544-pat00152
In addition,
Figure 112014071990544-pat00153
We can use the following inequality for the cost function.

Figure 112014071990544-pat00154

The above expression (20) can be expressed as follows.

Figure 112014071990544-pat00155

Assuming that the sum of the channel estimation error power sum of Equation (19) is assumed, the right side of the inequality in Equation (21)

Figure 112014071990544-pat00156
, It has the minimum value. Accordingly, the above equation (21) can be expressed as follows.

Figure 112014071990544-pat00157

In Kosi-Schwartz inequality

Figure 112014071990544-pat00158
And
Figure 112014071990544-pat00159
When a proportional relationship is established between terms, using the satisfaction of the equation
Figure 112014071990544-pat00160
Can be obtained from the following equation.

Figure 112014071990544-pat00161

Through the above equation (23)

Figure 112014071990544-pat00162
Can be expressed as follows.

Figure 112014071990544-pat00163

[Equation 24]

Figure 112014071990544-pat00164
, It can be expressed as follows.

Figure 112014071990544-pat00165

Here, the constant C can be expressed as follows.

Figure 112014071990544-pat00166

By substituting the expression for the constant c in the expression (26) into the expression (24) above,

Figure 112014071990544-pat00167
Diagonal
Figure 112014071990544-pat00168
Can be obtained.

Now,

Figure 112014071990544-pat00169
The equation (18) of the signal of the ML detection receiver can be expressed as follows.

Figure 112014071990544-pat00170

In Equation (27), the transmission symbol vector

Figure 112014071990544-pat00171
Each symbol
Figure 112014071990544-pat00172
Detection is performed on the ML estimates separately. Each symbol
Figure 112014071990544-pat00173
By performing detection on a per-symbol basis, the amount of computation for a combination of symbols can be reduced, and the amount of computation can be reduced.

In Equation (27) of the above-described ML detection receiver,

Figure 112014071990544-pat00174
about
Figure 112014071990544-pat00175
, And the calculated
Figure 112014071990544-pat00176
And updates
Figure 112014071990544-pat00177
about
Figure 112014071990544-pat00178
Again, use this to
Figure 112014071990544-pat00179
The more accurate symbol detection result can be obtained.

The amount of calculation is increased in proportion to the number of iterations to be performed by performing repetitive detection in comparison with an ML detection receiver for transmission diversity which does not consider a channel estimation error. However, in the present invention,

Figure 112014071990544-pat00180
Since the detection is performed for each symbol, the computation amount for each iteration is very low, and the computation amount is kept low even after several attempts.

The derivation of the ML detection receiver in the case of two spreading antennas can be derived even when four transmission antennas are used.

Now, an ML detection receiver in an environment affected by an interference signal together with a channel estimation error will be described.

In an environment in which an interference signal exists,

Figure 112014071990544-pat00181
Can be expressed as follows.

Figure 112014071990544-pat00182

here,

Figure 112014071990544-pat00183
Denotes a channel estimation matrix (2M 2) of the interference signal,
Figure 112014071990544-pat00184
Denotes a channel estimation error matrix of an interference signal.

The actual noise signal

Figure 112014071990544-pat00185
, The above expression (28) can be expressed as follows.

Figure 112014071990544-pat00186

For the signal model of Equation (29), the transmission symbol vector

Figure 112014071990544-pat00187
Can be represented as follows.

Figure 112015109453620-pat00188

here,

Figure 112014071990544-pat00189
Is the average power of the interference signal,
Figure 112014071990544-pat00190
Is defined as the power sum of the influence of the channel estimation errors of the desired signal and the interference signal at the i < th >

The real noise signal (

Figure 112014071990544-pat00191
) Includes an interference signal component and a channel estimation error component of the interference signal because an interference signal is added. Therefore, the influence of the channel estimation error of the interference signal should be considered in the operation of the ML detection receiver. The channel estimation error power sum limitation of the desired signal, and the channel estimation error power sum limitation of the interference signal are assumed as follows.

Figure 112014071990544-pat00192

Specific symbol vector

Figure 112014071990544-pat00193
, The expression for the ML detector receiver can be expressed as follows.

Figure 112015109453620-pat00194

In Equation 32,

Figure 112014071990544-pat00195
To
Figure 112014071990544-pat00196
,
Figure 112014071990544-pat00197
To
Figure 112014071990544-pat00198
,
Figure 112014071990544-pat00199
To
Figure 112014071990544-pat00200
,
Figure 112014071990544-pat00201
To
Figure 112014071990544-pat00202
Lt; / RTI >
Figure 112014071990544-pat00203
N,
Figure 112014071990544-pat00204
M,
Figure 112014071990544-pat00205
A,
Figure 112014071990544-pat00206
Is represented by B, the above equation (31) can be simply expressed as follows.

Figure 112014071990544-pat00207

The above equation (32) can be simply expressed as follows.

Figure 112014071990544-pat00208

Using the Koshi-Schwartz inequality in the above equation (34), it can be expressed as follows.

Figure 112014071990544-pat00209

The above expression (35) can be expressed as follows.

Figure 112014071990544-pat00210

Applying Equation (33) to Equation (36) can be simplified as follows.

Figure 112014071990544-pat00211

In order to establish the equation in the above equation (36)

Figure 112014071990544-pat00212
And
Figure 112014071990544-pat00213
The term is when there is a proportional relationship, which can be expressed as

Figure 112014071990544-pat00214

The following M equations can be derived through Equation (38).

Figure 112014071990544-pat00215

From the above equations,

Figure 112014071990544-pat00216
,
Figure 112014071990544-pat00217
Linear combination of
Figure 112014071990544-pat00218
Can be defined and solved. That is, the following equation can be derived in the interference signal environment.

Figure 112014071990544-pat00219

Figure 112014071990544-pat00220
Then, the covariance matrix
Figure 112014071990544-pat00221
, And the formula of the ML detection receiver can be expressed as follows.

Figure 112014071990544-pat00222

Similarly to Equation (27) derived above, each symbol

Figure 112014071990544-pat00223
By doing the detection by each, the ML detection receiver has a low calculation amount. More accurate symbol detection results can be obtained by the above-described repetitive detection algorithm.

A receiving signal detection method in an ML detection receiver using an iterative algorithm for transmission diversity transmission will be described in detail with reference to FIG.

In a wireless communication system for transmit diversity transmission, a transmit symbol (k) transmitted at a k < th > symbol time from two transmit antennas

Figure 112014071990544-pat00224
) Received from the i < th > receiving antenna of the M receiving antennas
Figure 112014071990544-pat00225
), The channel estimation of the channel (
Figure 112014071990544-pat00226
) Channel estimation error (
Figure 112014071990544-pat00227
) And channel estimation of the interference signal (
Figure 112014071990544-pat00228
) Channel estimation error (
Figure 112014071990544-pat00229
), And calculates a real noise signal ("
Figure 112014071990544-pat00230
) ≪ / RTI >
Figure 112014071990544-pat00231
) Was used to calculate the ML estimate
Figure 112014071990544-pat00232
), ML (Maximum Likelihood) receiver calculates the ML estimate using the following iterative algorithm
Figure 112014071990544-pat00233
).

First, the number of times of setting iteration execution

Figure 112014071990544-pat00234
(S100).

Current iteration count (

Figure 112014071990544-pat00235
) Is initialized to 0, and the received signal (
Figure 112014071990544-pat00236
), Channel estimation
Figure 112014071990544-pat00237
) Channel estimation error (
Figure 112014071990544-pat00238
), The interference channel estimation value (
Figure 112014071990544-pat00239
), The channel estimation error of the interference signal (
Figure 112014071990544-pat00240
) Is substituted into the above-described ML estimate value equation (Equation 41) to obtain the ML estimate value (
Figure 112014071990544-pat00241
(S102). At this time, the ML estimate value, which is the initial value,
Figure 112014071990544-pat00242
Lt; / RTI >

The initial value (

Figure 112014071990544-pat00243
) Is used to estimate the channel estimation error
Figure 112014071990544-pat00244
) For an instantaneous covariance matrix (
Figure 112014071990544-pat00245
) Of the diagonal
Figure 112014071990544-pat00246
(S104), and calculates a channel estimation error (
Figure 112014071990544-pat00247
) ≪ / RTI >
Figure 112014071990544-pat00248
), And using this,
Figure 112014071990544-pat00249
.

Now,

Figure 112014071990544-pat00250
,
Figure 112014071990544-pat00251
The ML estimate (
Figure 112014071990544-pat00252
(S106).

Thereafter, the current number of times of repeated execution (

Figure 112014071990544-pat00253
) Is set to the number of times of repeated execution (
Figure 112014071990544-pat00254
(S108).

As a result of the determination,

Figure 112014071990544-pat00255
) Is the number of times the setting is repeated (
Figure 112014071990544-pat00256
), The calculated ML estimate value (
Figure 112014071990544-pat00257
) To the ML estimate (
Figure 112014071990544-pat00258
(S110), and ends the iterative algorithm.

Further, after the determination of the number of times of repeated execution,

Figure 112014071990544-pat00259
) And the number of setting iterations
Figure 112014071990544-pat00260
), The current number of times of repeated execution (
Figure 112014071990544-pat00261
) Is increased by 1 (S112), and the calculated ML estimate value (
Figure 112014071990544-pat00262
)
Figure 112014071990544-pat00263
And the re-calculated
Figure 112014071990544-pat00264
The ML estimate (
Figure 112014071990544-pat00265
) Is calculated again and the ML estimate (
Figure 112014071990544-pat00266
). Using an iterative algorithm including this step, the ML estimate (
Figure 112014071990544-pat00267
) Is calculated as a final value.

The ML estimation value is calculated by the number of iterations set by the iterative algorithm as described above, and the ML estimates can obtain accurate results as the number of iterations is increased.

It is preferable to perform a simulation to evaluate the performance of the ML detection receiver of the present invention designed as described above.

The simulation environment can be set as follows.

Channel counting: Complex normal distribution

Figure 112014071990544-pat00268

The sum of the channel estimation error power of the desired signal

Figure 112014071990544-pat00269
:
Figure 112014071990544-pat00270
Even distribution

The sum of the channel estimation error power of the interference signal

Figure 112014071990544-pat00271
:
Figure 112014071990544-pat00272
Even distribution

Channel estimation error: Normalization is performed in order to satisfy the total channel error power after generation with uniform distribution.

Interference signal power: 10 times the symbol power (

Figure 112014071990544-pat00273
)

Modulation method: QPSK

FIG. 5 and FIG. 6 are simulation results of an ML detection receiver using 4 × 4 transmit diversity. As shown in FIG. 5 and FIG. 6, the BER (Bit Error Ratio) of a receiver according to a change in the sum of channel estimation error powers of desired signals and interference signals ) Performance.

As shown in FIGS. 5 and 6, the number of iterations of the ML detection receiver of the present invention is divided into four, one, two, four, and eight times. Proposed Receiver LB is ideal for ML detection receiver.

Figure 112014071990544-pat00274
And the covariance matrix of the channel estimation error is calculated using the calculated channel estimation error, which indicates the maximum performance that can be obtained by the ML detection receiver of the present invention, that is, the lower bound performance.

Conv. The robust receiver means an ML detection receiver using the average value of the covariance matrix of the channel estimation error. That is, it is an existing receiving method as compared with the ML detector of the present invention for estimating an instantaneous covariance matrix of the channel estimation error.

The highest performance of each performance test result is the ML detection receiver when the receiver knows the perfect channel state information (Imperfect CSI). In addition, the lowest performance is equivalent to the performance of a conventional ML detection receiver in which channel state information is not perfect (Perfect CSI), that is, channel estimation error exists but does not consider this.

FIG. 5 shows performance test results in an environment in which no interference signal exists in 4 × 4 transmit diversity. E = 6.4,

Figure 112015109453620-pat00275
, The average power of each channel estimation error value is
Figure 112015109453620-pat00276
. That is, an average power 1 of the channel counting, the power of the channel estimation error is 0.8 times as large as the error environment. As a result of the performance test, the receiver of the present invention shows higher performance than the conventional receiver, and BER =
Figure 112015109453620-pat00277
And SNR (Signal to Noise Ratio) gains of 2.2 and 2.9 dB, respectively, after four and eight iterations, respectively.

FIG. 6 is a diagram illustrating the performance of 4 × 4 transmit diversity in an interference signal presence environment.

Figure 112015109453620-pat00278
,
Figure 112015109453620-pat00279
Assuming a channel estimation error power,
Figure 112015109453620-pat00280
Wow,
Figure 112015109453620-pat00281
The maximum average power of each channel estimation error is
Figure 112015109453620-pat00282
,
Figure 112015109453620-pat00283
. BER =
Figure 112015109453620-pat00284
The SNR gain of 3.0 dB and 3.4 dB after 4 times and 8 repetitions of the conventional receiver, respectively, show higher reception performance than the conventional receiver.

100: transmitting side antenna 200: receiving side antenna
210: signal processor 220: channel estimator
230: channel estimation error unit 240: ML estimation unit

Claims (15)

In a wireless communication system for transmission diversity transmission, a transmission symbol transmitted at k < th > symbol time from two or more transmission antennas
Figure 112015109453620-pat00285
) Received from the i < th > receiving antenna of the M receiving antennas
Figure 112015109453620-pat00286
), Channel estimation (
Figure 112015109453620-pat00287
) Channel estimation error (
Figure 112015109453620-pat00288
), And outputs the substantial noise signal (
Figure 112015109453620-pat00289
) ≪ / RTI >
Figure 112015109453620-pat00290
), The ML estimate (
Figure 112015109453620-pat00291
), The ML detector comprising:
The received signal (< RTI ID = 0.0 >
Figure 112015109453620-pat00292
);
The channel estimate (H) for the channel < RTI ID = 0.0 >
Figure 112015109453620-pat00293
);
The channel estimate (
Figure 112015109453620-pat00294
The channel estimation error < RTI ID = 0.0 >
Figure 112015109453620-pat00295
A channel estimation error part for calculating a channel estimation error;
The received signal (
Figure 112015109453620-pat00296
), Channel estimation
Figure 112015109453620-pat00297
), Channel estimation error (
Figure 112015109453620-pat00298
),
Figure 112015109453620-pat00299
The initial value of the real noise signal is obtained, and the covariance matrix (
Figure 112015109453620-pat00300
), And then, using this,
Figure 112015109453620-pat00301
Lt; RTI ID = 0.0 > (ML) < / RTI > estimate
Figure 112015109453620-pat00302
);
, ≪ / RTI &
The iterative algorithm includes:
Trials predetermined algorithm repeat settings, which means the number of iterations performed (
Figure 112015109453620-pat00549
),
Current iteration count (
Figure 112015109453620-pat00550
) Is initialized to 0, and the received signal (
Figure 112015109453620-pat00551
), Transmission channel estimation (
Figure 112015109453620-pat00552
), Channel estimation error (
Figure 112015109453620-pat00553
)
Figure 112015109453620-pat00554
Of ML estimates (
Figure 112015109453620-pat00555
) And stores it as an initial value,
The initial value (
Figure 112015109453620-pat00556
) Is used to estimate the channel estimation error
Figure 112015109453620-pat00557
) For an instantaneous covariance matrix (
Figure 112015109453620-pat00558
), And using this,
Figure 112015109453620-pat00559
Of
Figure 112015109453620-pat00560
Lt; / RTI >
remind
Figure 112015109453620-pat00561
The ML estimate (
Figure 112015109453620-pat00562
),
Current iteration count (
Figure 112015109453620-pat00563
) Is set to the number of times of repeated execution (
Figure 112015109453620-pat00564
), ≪ / RTI >
As a result of the determination,
Figure 112015109453620-pat00565
), The algorithm is terminated, and the calculated ML estimate value (
Figure 112015109453620-pat00566
) To the ML estimate (
Figure 112015109453620-pat00567
), ≪ / RTI >
The method according to claim 1,
Two transmission antennas are provided, and the ML estimates (
Figure 112014071990544-pat00303
),
Figure 112014071990544-pat00304

(here,
Figure 112014071990544-pat00305
Is a noise signal
Figure 112014071990544-pat00306
), Respectively,
Figure 112014071990544-pat00307
Symbol
Figure 112014071990544-pat00308
The average symbol power of each circle,
Figure 112014071990544-pat00309
,
Figure 112014071990544-pat00310
.)
(≪ RTI ID = 0.0 >
Figure 112014071990544-pat00311
) ≪ / RTI >
Figure 112014071990544-pat00312
). ≪ / RTI >
3. The method of claim 2,
In order to minimize the cost function,
Figure 112015109453620-pat00568
Is obtained,
Figure 112015109453620-pat00569
Assuming that,
Figure 112015109453620-pat00570
To get the value
Figure 112015109453620-pat00314
The channel estimation error power sum defined by the sum of the channel estimation error power,
Figure 112015109453620-pat00315
Defined as
Figure 112015109453620-pat00316
, And the calculated
Figure 112015109453620-pat00317
Lt; / RTI >
Figure 112015109453620-pat00318

(here,
Figure 112015109453620-pat00319
,
Figure 112015109453620-pat00320
.)
(≪ RTI ID = 0.0 >
Figure 112015109453620-pat00321
) ≪ / RTI >
Figure 112015109453620-pat00322
) To each transmission symbol (
Figure 112015109453620-pat00323
), ML detection receiver.
The method of claim 3,
Channel estimation error (
Figure 112014071990544-pat00324
) Of the instantaneous covariance matrix
Figure 112014071990544-pat00325
Lt; / RTI >
The real noise signal (
Figure 112014071990544-pat00326
), The instantaneous covariance matrix for < RTI ID = 0.0 >
Figure 112014071990544-pat00327
Lt; / RTI >
Figure 112014071990544-pat00328
The
Figure 112014071990544-pat00329
And orthogonality of the channel estimation error and channel estimation error,
Figure 112014071990544-pat00330
Lt; th > diagonal component value of <
Figure 112014071990544-pat00331
Lt; / RTI >
From this, the sum of channel estimation error power
Figure 112014071990544-pat00332
Assuming that the maximum value is limited as shown in Fig.
remind
Figure 112014071990544-pat00333
From
Figure 112014071990544-pat00334
Wherein the channel estimation error power sum is defined as:
delete The method according to claim 1,
Figure 112015109453620-pat00354
) Setting the number of times to repeat (
Figure 112015109453620-pat00355
) After judging the same,
Current iteration count (
Figure 112015109453620-pat00356
) And the number of setting iterations
Figure 112015109453620-pat00357
), The current number of times of repeated execution (
Figure 112015109453620-pat00358
) To increase by 1, and outputs the previously calculated ML estimate value (
Figure 112015109453620-pat00359
)
Figure 112015109453620-pat00360
Therefore,
The re-
Figure 112015109453620-pat00361
(ML) of the next iteration
Figure 112015109453620-pat00362
) And re-calculates the ML estimate value (ML) calculated using the re-
Figure 112015109453620-pat00363
) To perform the iterative algorithm.
In a wireless communication system for transmission diversity transmission, a transmission symbol transmitted at k < th > symbol time from two or more transmission antennas
Figure 112015109453620-pat00364
) Received from the i < th > receiving antenna of the M receiving antennas
Figure 112015109453620-pat00365
), The channel estimation of the channel (
Figure 112015109453620-pat00366
) Channel estimation error (
Figure 112015109453620-pat00367
) And channel estimation of the interference signal upon reception of the interference signal (
Figure 112015109453620-pat00368
) Channel estimation error (
Figure 112015109453620-pat00369
), And calculates channel estimation errors (
Figure 112015109453620-pat00370
,
Figure 112015109453620-pat00371
) Corresponding to the real noise signal (
Figure 112015109453620-pat00372
), And calculates a reception signal (
Figure 112015109453620-pat00373
) Was used to calculate the ML estimate
Figure 112015109453620-pat00374
), The ML detector comprising:
The received signal (
Figure 112015109453620-pat00375
A signal processing unit receiving the interference signal or receiving the interference signal;
The received signal (
Figure 112015109453620-pat00376
) ≪ / RTI >
Figure 112015109453620-pat00377
), A channel estimate for the interference signal upon reception of the interference signal (
Figure 112015109453620-pat00378
);
The channel estimation (
Figure 112015109453620-pat00379
Channel estimation error (
Figure 112015109453620-pat00380
) Or a channel estimation error for an interference signal upon reception of an interference signal (
Figure 112015109453620-pat00381
A channel estimation error part for calculating a channel estimation error;
The received signal (
Figure 112015109453620-pat00382
), Channel estimation
Figure 112015109453620-pat00383
,
Figure 112015109453620-pat00384
), Channel estimation error (
Figure 112015109453620-pat00385
,
Figure 112015109453620-pat00386
)
Figure 112015109453620-pat00387
The initial value of the real noise signal is obtained, and the covariance matrix (
Figure 112015109453620-pat00388
), And then, using this,
Figure 112015109453620-pat00389
Lt; RTI ID = 0.0 > (ML) < / RTI > estimate
Figure 112015109453620-pat00390
);
, ≪ / RTI &
The iterative algorithm
The number of iterations (number of iterations)
Figure 112015109453620-pat00571
),
Current iteration count (
Figure 112015109453620-pat00572
) Is initialized to 0, and the received signal (
Figure 112015109453620-pat00573
), Channel estimation of the transmission channel (
Figure 112015109453620-pat00574
) Channel estimation error (
Figure 112015109453620-pat00575
), A transmission channel estimation value of an interference signal (
Figure 112015109453620-pat00576
), The channel estimation error of the interference signal (
Figure 112015109453620-pat00577
) Was used to calculate the ML estimate
Figure 112015109453620-pat00578
) And stores it as an initial value,
The initial value (
Figure 112015109453620-pat00579
) Is used to estimate the channel estimation error
Figure 112015109453620-pat00580
) For an instantaneous covariance matrix (
Figure 112015109453620-pat00581
)
Figure 112015109453620-pat00582
And calculates a channel estimation error ("
Figure 112015109453620-pat00583
) ≪ / RTI >
Figure 112015109453620-pat00584
), And using this,
Figure 112015109453620-pat00585
Lt; / RTI >
remind
Figure 112015109453620-pat00586
,
Figure 112015109453620-pat00587
The ML estimate (
Figure 112015109453620-pat00588
),
Current iteration count (
Figure 112015109453620-pat00589
) Is set to the number of times of repeated execution (
Figure 112015109453620-pat00590
), ≪ / RTI >
As a result of the determination,
Figure 112015109453620-pat00591
) Is the number of times the setting is repeated (
Figure 112015109453620-pat00592
), The algorithm is terminated, and the calculated ML estimate value (
Figure 112015109453620-pat00593
) To the ML estimate (
Figure 112015109453620-pat00594
), ≪ / RTI >
8. The method of claim 7, wherein two transmit antennas are provided,
Figure 112015109453620-pat00391

(here,
Figure 112015109453620-pat00392
Lt; RTI ID = 0.0 >
Figure 112015109453620-pat00393
), Respectively,
Figure 112015109453620-pat00394
Symbol
Figure 112015109453620-pat00395
The average symbol power of each circle,
Figure 112015109453620-pat00396
Is the average power of the interference signal,
Figure 112015109453620-pat00397
, the power sum of the influence of the channel estimation error of the desired signal and the interference signal at the i < th >
Figure 112015109453620-pat00398
)silver
Figure 112015109453620-pat00399
.)
(≪ RTI ID = 0.0 >
Figure 112015109453620-pat00400
) ≪ / RTI >
Figure 112015109453620-pat00401
). ≪ / RTI >
9. The method of claim 8, wherein the ML estimate < RTI ID = 0.0 >
Figure 112014071990544-pat00402
) ≪ / RTI >
The channel estimate error power sum of the desired signal
Figure 112014071990544-pat00403
, The channel estimation error power sum of interference signals
Figure 112014071990544-pat00404
Using the Koshi-Schwartz inequality

Figure 112014071990544-pat00405

(here,
Figure 112014071990544-pat00406
,
Figure 112014071990544-pat00407
,
Figure 112014071990544-pat00408
,
Figure 112014071990544-pat00409
,
Figure 112014071990544-pat00410
,
Figure 112014071990544-pat00411
,
Figure 112014071990544-pat00412
.)
, And the transmission symbol (
Figure 112014071990544-pat00413
) ≪ / RTI >
Figure 112014071990544-pat00414
). ≪ / RTI >
delete 8. The method of claim 7,
Figure 112015109453620-pat00439
) Is the number of times the setting is repeated (
Figure 112015109453620-pat00440
) After judging the same,
Current iteration count (
Figure 112015109453620-pat00441
) And the number of setting iterations
Figure 112015109453620-pat00442
), The current number of times of repeated execution (
Figure 112015109453620-pat00443
) Is incremented by 1 and counts the calculated ML estimate value (
Figure 112015109453620-pat00444
)
Figure 112015109453620-pat00445
Lt; / RTI >
Recalculated
Figure 112015109453620-pat00446
(ML) of the next iteration
Figure 112015109453620-pat00447
), And the ML estimate (
Figure 112015109453620-pat00448
), ≪ / RTI > which performs an iterative algorithm.
In a wireless communication system for transmission diversity transmission, a transmission symbol transmitted at k < th > symbol time from two or more transmission antennas
Figure 112015109453620-pat00449
) Received from the i < th > receiving antenna of the M receiving antennas
Figure 112015109453620-pat00450
), Channel estimation (
Figure 112015109453620-pat00451
) Channel estimation error (
Figure 112015109453620-pat00452
), And outputs the substantial noise signal (
Figure 112015109453620-pat00453
) Using the received signal including the ML estimate < RTI ID = 0.0 > (
Figure 112015109453620-pat00454
) In the ML detection method of the ML detection receiver,
The number of iterations of setting iterations, which means the number of iterations of the preset algorithm (
Figure 112015109453620-pat00455
Receiving and storing the input data;
Current iteration count (
Figure 112015109453620-pat00456
) Is initialized to 0, and the received signal (
Figure 112015109453620-pat00457
), A transmission channel estimate (
Figure 112015109453620-pat00458
) And channel estimation error (
Figure 112015109453620-pat00459
)
Figure 112015109453620-pat00460
Of ML estimates (
Figure 112015109453620-pat00461
And storing the initial value as an initial value;
The initial value (
Figure 112015109453620-pat00462
) Is used to estimate the channel estimation error
Figure 112015109453620-pat00463
) For an instantaneous covariance matrix (
Figure 112015109453620-pat00464
), And using this,
Figure 112015109453620-pat00465
Of
Figure 112015109453620-pat00466
, And
Figure 112015109453620-pat00467
The ML estimate (
Figure 112015109453620-pat00468
);
Current iteration count (
Figure 112015109453620-pat00469
) Is set to the number of times of repeated execution (
Figure 112015109453620-pat00470
), ≪ / RTI >
As a result of the determination,
Figure 112015109453620-pat00471
), The algorithm is terminated, and the calculated ML estimate value (
Figure 112015109453620-pat00472
) As a final value;
Lt; RTI ID = 0.0 > ML < / RTI > estimate
Figure 112015109453620-pat00473
),
After the determination of the number of times of repeated execution,
From the result of the determination,
Figure 112015109453620-pat00595
) And the number of setting iterations
Figure 112015109453620-pat00596
), The current number of times of repeated execution (
Figure 112015109453620-pat00597
) Is incremented by 1, and the calculated ML estimate value (
Figure 112015109453620-pat00598
)
Figure 112015109453620-pat00599
And the re-calculated
Figure 112015109453620-pat00600
The ML estimate (
Figure 112015109453620-pat00601
) Is calculated again and the ML estimate (
Figure 112015109453620-pat00602
);
Lt; RTI ID = 0.0 > (ML) < / RTI >
Figure 112015109453620-pat00603
). ≪ / RTI >
delete In a wireless communication system for transmission diversity transmission, a transmission symbol transmitted at k < th > symbol time from two or more transmission antennas
Figure 112015109453620-pat00483
) Received from the i < th > receiving antenna of the M receiving antennas
Figure 112015109453620-pat00484
), The channel estimation of the channel (
Figure 112015109453620-pat00485
) Channel estimation error (
Figure 112015109453620-pat00486
) And channel estimation of the interference signal (
Figure 112015109453620-pat00487
) Channel estimation error (
Figure 112015109453620-pat00488
), And calculates a real noise signal ("
Figure 112015109453620-pat00489
) ≪ / RTI >
Figure 112015109453620-pat00490
0.0 > ML < / RTI > estimate
Figure 112015109453620-pat00491
) In the ML detection method of the ML detection receiver,
The number of iterations of the setting, which means the number of times the algorithm is repeated.
Figure 112015109453620-pat00492
Receiving and storing the input data;
The current number of iterations
Figure 112015109453620-pat00493
) And initializes the received signal (
Figure 112015109453620-pat00494
), Channel estimation of the transmission channel (
Figure 112015109453620-pat00495
) Channel estimation error (
Figure 112015109453620-pat00496
), A transmission channel estimation value of an interference signal (
Figure 112015109453620-pat00497
), The channel estimation error of the interference signal (
Figure 112015109453620-pat00498
)
Figure 112015109453620-pat00499
The ML estimates of < RTI ID = 0.0 >
Figure 112015109453620-pat00500
);
The initial value (
Figure 112015109453620-pat00501
) Is used to estimate the channel estimation error
Figure 112015109453620-pat00502
) For an instantaneous covariance matrix (
Figure 112015109453620-pat00503
) Of the diagonal
Figure 112015109453620-pat00504
And calculates a channel estimation error ("
Figure 112015109453620-pat00505
) ≪ / RTI >
Figure 112015109453620-pat00506
), And using this,
Figure 112015109453620-pat00507
;
remind
Figure 112015109453620-pat00508
,
Figure 112015109453620-pat00509
The ML estimate (
Figure 112015109453620-pat00510
),
Current iteration count (
Figure 112015109453620-pat00511
) Is set to the number of times of repeated execution (
Figure 112015109453620-pat00512
), ≪ / RTI >
As a result of the determination,
Figure 112015109453620-pat00513
), The calculated ML estimate value (
Figure 112015109453620-pat00514
) To the final value of the ML estimate, using the iterative algorithm, the ML estimate < RTI ID = 0.0 >
Figure 112015109453620-pat00515
),
After the determination of the number of times of repeated execution, as a result of the determination,
Figure 112015109453620-pat00604
) And the number of setting iterations
Figure 112015109453620-pat00605
), The current number of times of repeated execution (
Figure 112015109453620-pat00606
) Is incremented by 1, and the calculated ML estimate value (
Figure 112015109453620-pat00607
)
Figure 112015109453620-pat00608
And the re-calculated
Figure 112015109453620-pat00609
The ML estimate (
Figure 112015109453620-pat00610
) Is calculated again and the ML estimate (
Figure 112015109453620-pat00611
Using the iterative algorithm,
The ML estimate < RTI ID =
Figure 112015109453620-pat00612
). ≪ / RTI >
delete
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