KR101585675B1 - Maximum likelihood detecting receiver using iterative algorithm for transmit diversity mode and maximum likelihood detecting method thereof - Google Patents
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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-
) Received from the i < th > receiving antenna of the M receiving antennas ), Channel estimation ( ) Channel estimation error ( ), And outputs the substantial noise signal ( ) And the channel estimation error ( ), The ML estimate ( ), The ML receiver comprising: a reception signal generator ); The channel estimate (H) for the channel < RTI ID = 0.0 > ); The channel estimate ( The channel estimation error < RTI ID = 0.0 > A channel estimation error part for calculating a channel estimation error; The received signal ( ), Channel estimation ), Channel estimation error ( ), The initial value of the real noise signal is obtained, and the covariance matrix ( ), And then, using this, Lt; RTI ID = 0.0 > (ML) < / RTI > estimate ); .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
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,
and 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.
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-
) Received from the i < th > receiving antenna of the M receiving antennas ), Channel estimation ( ) Channel estimation error ( ), And outputs the substantial noise signal ( ) ≪ / RTI > ), The ML estimate ( ), Characterized in that the reception signal from the transmission antenna ); The channel estimate (H) for the channel < RTI ID = 0.0 > ); The channel estimate ( The channel estimation error < RTI ID = 0.0 > A channel estimation error part for calculating a channel estimation error; The received signal ( ), Channel estimation ), Channel estimation error ( ), The initial value of the real noise signal is obtained, and the covariance matrix ( ), And then, using this, Lt; RTI ID = 0.0 > (ML) < / RTI > estimate 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-
) Received from the i < th > receiving antenna of the M receiving antennas ), Channel estimation ( ) Channel estimation error ( ), And using the received signal including the substantial noise signal, the ML estimate ( (ML) detection method of an ML detection receiver for calculating an ML number Receiving and storing the input data; Current iteration count ( ) Is initialized to 0, and the received signal ( ), A transmission channel estimate ( ) And channel estimation error ( ) Of ML estimates ( And storing the initial value as an initial value; The initial value ( ) Is used to estimate the channel estimation error ) For an instantaneous covariance matrix ( ), And using this, Of , And The ML estimate ( ); Current iteration count ( ) Is set to the number of times of repeated execution ( ), ≪ / RTI > As a result of the determination, ), The algorithm is terminated, and the calculated ML estimate value ( ) As a final value, using an iterative algorithm including the ML estimate < RTI ID = 0.0 > ( ). ≪ / 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.
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.
: Conjugate matrix
: Transpose matrix
: Conjugate Transpose matrix
: Euclidean norm
: Identity matrix
3, the ML detection receiver is provided on the reception side and includes a
First, transmit diversity using two antennas is performed by using two symbols
, From the transmitting-At this time,
, And a signal received for two symbol times in the receiving
here,
Denotes a noise signal at a receiving end in a k-th symbol time.As described above, the reception signal ("
May be transmitted to theIn addition, when an interference signal such as a jamming signal is received from the
The
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,
, 0, , And a signal of 0 is transmitted. During the next symbol time , 0, , And a signal of 0 is transmitted. Then 0, , 0, 0 " for the next symbol time, , 0, Respectively.The reception signal of the transmit diversity using the four transmit antennas
) Is expressed as the following equation.
Now, the reception signal of 2 x 1 transmit diversity using the above two antennas
) Is expressed by the following equation. ≪ EMI ID = 1.0 >
Here, the transmission symbol vector
, The channel matrix , The noise signal , The following reception signal ( ) Can be obtained.
Channel matrix (
) Is the channel matrix estimate < RTI ID = 0.0 > ) To the error matrix ( ) Is added, the channel matrix estimate is < RTI ID = 0.0 > in And can be represented by the following equation.
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.
Here, the reception signal of the i < th >
, And the channel estimation matrix and the channel estimation error matrix of the i < th > , .The channel estimation matrix and the channel estimation error matrix have orthogonality characteristics as shown in the following equations.
here,
The Lt; / RTI > The , And I denotes an identity matrix.In Equation (7)
Wow, Are defined as follows., The entire received signal ( ) Can be expressed as the following equation.
Here,
of , The following expression can be expressed.
Noise signal
The average power of each circle of , symbol The average symbol power of each circle of , And an instantaneous covariance matrix of the channel estimation error is represented by The covariance matrix of the total real noise signal can be expressed as follows.
At this time, the real noise signal (
) Follows the normal distribution, and the instantaneous covariance matrix of the channel estimation error ( )silver And that the channel estimation error is approximated to a diagonal matrix by the independence between the orthogonality and the channel estimation error,The value of the i-th diagonal component of Can be expressed as follows.
In Equation (11)
Because of, Can be represented by a diagonal matrix as follows.
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.
From the above equation (13), the following equation is established.
Transmit symbol
The ML (maximum likelihood) estimate of Can be expressed as follows.
here,
to be.now,
Of the orthogonal characteristic , It can be expressed as the following expression.
In Equation (16)
, The following equation can be derived.
The above Equation (17)
Estimate of Can be finally expressed as follows.
Now, in the equation of the above equation (18), the cost function is minimized
., It is difficult to optimize , It is assumed that the optimal . At this time, the following formula for the power sum of the channel estimation error is used.
In the formula of the above formula (18)
To In addition, We can use the following inequality for the cost function.
The above expression (20) can be expressed as follows.
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)
, It has the minimum value. Accordingly, the above equation (21) can be expressed as follows.
In Kosi-Schwartz inequality
And When a proportional relationship is established between terms, using the satisfaction of the equation Can be obtained from the following equation.
Through the above equation (23)
Can be expressed as follows.
[Equation 24]
, It can be expressed as follows.
Here, the constant C can be expressed as follows.
By substituting the expression for the constant c in the expression (26) into the expression (24) above,
Diagonal Can be obtained.Now,
The equation (18) of the signal of the ML detection receiver can be expressed as follows.
In Equation (27), the transmission symbol vector
Each symbol Detection is performed on the ML estimates separately. Each symbol 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,
about , And the calculated And updates about Again, use this to 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,
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,
Can be expressed as follows.
here,
Denotes a channel estimation matrix (2M 2) of the interference signal, Denotes a channel estimation error matrix of an interference signal.The actual noise signal
, The above expression (28) can be expressed as follows.
For the signal model of Equation (29), the transmission symbol vector
Can be represented as follows.
here,
Is the average power of the interference signal, 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 (
) 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.
Specific symbol vector
, The expression for the ML detector receiver can be expressed as follows.
In Equation 32,
To , To , To , To Lt; / RTI > N, M, A, Is represented by B, the above equation (31) can be simply expressed as follows.
The above equation (32) can be simply expressed as follows.
Using the Koshi-Schwartz inequality in the above equation (34), it can be expressed as follows.
The above expression (35) can be expressed as follows.
Applying Equation (33) to Equation (36) can be simplified as follows.
In order to establish the equation in the above equation (36)
And The term is when there is a proportional relationship, which can be expressed as
The following M equations can be derived through Equation (38).
From the above equations,
, Linear combination of Can be defined and solved. That is, the following equation can be derived in the interference signal environment.
Then, the covariance matrix , And the formula of the ML detection receiver can be expressed as follows.
Similarly to Equation (27) derived above, each symbol
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
) Received from the i < th > receiving antenna of the M receiving antennas ), The channel estimation of the channel ( ) Channel estimation error ( ) And channel estimation of the interference signal ( ) Channel estimation error ( ), And calculates a real noise signal (" ) ≪ / RTI > ) Was used to calculate the ML estimate ), ML (Maximum Likelihood) receiver calculates the ML estimate using the following iterative algorithm ).First, the number of times of setting iteration execution
(S100).Current iteration count (
) Is initialized to 0, and the received signal ( ), Channel estimation ) Channel estimation error ( ), The interference channel estimation value ( ), The channel estimation error of the interference signal ( ) Is substituted into the above-described ML estimate value equation (Equation 41) to obtain the ML estimate value ( (S102). At this time, the ML estimate value, which is the initial value, Lt; / RTI >The initial value (
) Is used to estimate the channel estimation error ) For an instantaneous covariance matrix ( ) Of the diagonal (S104), and calculates a channel estimation error ( ) ≪ / RTI > ), And using this, .Now,
, The ML estimate ( (S106).Thereafter, the current number of times of repeated execution (
) Is set to the number of times of repeated execution ( (S108).As a result of the determination,
) Is the number of times the setting is repeated ( ), The calculated ML estimate value ( ) To the ML estimate ( (S110), and ends the iterative algorithm.Further, after the determination of the number of times of repeated execution,
) And the number of setting iterations ), The current number of times of repeated execution ( ) Is increased by 1 (S112), and the calculated ML estimate value ( ) And the re-calculated The ML estimate ( ) Is calculated again and the ML estimate ( ). Using an iterative algorithm including this step, the ML estimate ( ) 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
The sum of the channel estimation error power of the desired signal
: Even distributionThe sum of the channel estimation error power of the interference signal
: Even distributionChannel 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 (
)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.
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,
, The average power of each channel estimation error value is . That is, anFIG. 6 is a diagram illustrating the performance of 4 × 4 transmit diversity in an interference signal presence environment.
, Assuming a channel estimation error power, Wow, The maximum average power of each channel estimation error is , . BER = 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)
The received signal (< RTI ID = 0.0 > );
The channel estimate (H) for the channel < RTI ID = 0.0 > );
The channel estimate ( The channel estimation error < RTI ID = 0.0 > A channel estimation error part for calculating a channel estimation error;
The received signal ( ), Channel estimation ), Channel estimation error ( ), The initial value of the real noise signal is obtained, and the covariance matrix ( ), And then, using this, Lt; RTI ID = 0.0 > (ML) < / RTI > estimate );
, ≪ / RTI &
The iterative algorithm includes:
Trials predetermined algorithm repeat settings, which means the number of iterations performed ( ),
Current iteration count ( ) Is initialized to 0, and the received signal ( ), Transmission channel estimation ( ), Channel estimation error ( ) Of ML estimates ( ) And stores it as an initial value,
The initial value ( ) Is used to estimate the channel estimation error ) For an instantaneous covariance matrix ( ), And using this, Of Lt; / RTI >
remind The ML estimate ( ),
Current iteration count ( ) Is set to the number of times of repeated execution ( ), ≪ / RTI >
As a result of the determination, ), The algorithm is terminated, and the calculated ML estimate value ( ) To the ML estimate ( ), ≪ / RTI >
Two transmission antennas are provided, and the ML estimates ( ),
(here, Is a noise signal ), Respectively, Symbol The average symbol power of each circle, , .)
(≪ RTI ID = 0.0 > ) ≪ / RTI > ). ≪ / RTI >
In order to minimize the cost function, Is obtained, Assuming that, To get the value The channel estimation error power sum defined by the sum of the channel estimation error power,
Defined as , And the calculated Lt; / RTI >
(here, , .)
(≪ RTI ID = 0.0 > ) ≪ / RTI > ) To each transmission symbol ( ), ML detection receiver.
Channel estimation error ( ) Of the instantaneous covariance matrix Lt; / RTI >
The real noise signal ( ), The instantaneous covariance matrix for < RTI ID = 0.0 > Lt; / RTI >
The And orthogonality of the channel estimation error and channel estimation error,
Lt; th > diagonal component value of < Lt; / RTI >
From this, the sum of channel estimation error power Assuming that the maximum value is limited as shown in Fig.
remind From Wherein the channel estimation error power sum is defined as:
Current iteration count ( ) And the number of setting iterations ), The current number of times of repeated execution ( ) To increase by 1, and outputs the previously calculated ML estimate value ( ) Therefore,
The re- (ML) of the next iteration ) And re-calculates the ML estimate value (ML) calculated using the re- ) To perform the iterative algorithm.
The received signal ( A signal processing unit receiving the interference signal or receiving the interference signal;
The received signal ( ) ≪ / RTI > ), A channel estimate for the interference signal upon reception of the interference signal ( );
The channel estimation ( Channel estimation error ( ) Or a channel estimation error for an interference signal upon reception of an interference signal ( A channel estimation error part for calculating a channel estimation error;
The received signal ( ), Channel estimation , ), Channel estimation error ( , ) The initial value of the real noise signal is obtained, and the covariance matrix ( ), And then, using this, Lt; RTI ID = 0.0 > (ML) < / RTI > estimate );
, ≪ / RTI &
The iterative algorithm
The number of iterations (number of iterations) ),
Current iteration count ( ) Is initialized to 0, and the received signal ( ), Channel estimation of the transmission channel ( ) Channel estimation error ( ), A transmission channel estimation value of an interference signal ( ), The channel estimation error of the interference signal ( ) Was used to calculate the ML estimate ) And stores it as an initial value,
The initial value ( ) Is used to estimate the channel estimation error ) For an instantaneous covariance matrix ( ) And calculates a channel estimation error (" ) ≪ / RTI > ), And using this, Lt; / RTI >
remind , The ML estimate ( ),
Current iteration count ( ) Is set to the number of times of repeated execution ( ), ≪ / RTI >
As a result of the determination, ) Is the number of times the setting is repeated ( ), The algorithm is terminated, and the calculated ML estimate value ( ) To the ML estimate ( ), ≪ / RTI >
(here, Lt; RTI ID = 0.0 > ), Respectively, Symbol The average symbol power of each circle, Is the average power of the interference signal, , the power sum of the influence of the channel estimation error of the desired signal and the interference signal at the i < th > )silver .)
(≪ RTI ID = 0.0 > ) ≪ / RTI > ). ≪ / RTI >
The channel estimate error power sum of the desired signal , The channel estimation error power sum of interference signals Using the Koshi-Schwartz inequality
(here, , ,
, , , , .)
, And the transmission symbol ( ) ≪ / RTI > ). ≪ / RTI >
Current iteration count ( ) And the number of setting iterations ), The current number of times of repeated execution ( ) Is incremented by 1 and counts the calculated ML estimate value ( ) Lt; / RTI >
Recalculated (ML) of the next iteration ), And the ML estimate ( ), ≪ / RTI > which performs an iterative algorithm.
The number of iterations of setting iterations, which means the number of iterations of the preset algorithm ( Receiving and storing the input data;
Current iteration count ( ) Is initialized to 0, and the received signal ( ), A transmission channel estimate ( ) And channel estimation error ( ) Of ML estimates ( And storing the initial value as an initial value;
The initial value ( ) Is used to estimate the channel estimation error ) For an instantaneous covariance matrix ( ), And using this, Of , And The ML estimate ( );
Current iteration count ( ) Is set to the number of times of repeated execution ( ), ≪ / RTI >
As a result of the determination, ), The algorithm is terminated, and the calculated ML estimate value ( ) As a final value;
Lt; RTI ID = 0.0 > ML < / RTI > estimate ),
After the determination of the number of times of repeated execution,
From the result of the determination, ) And the number of setting iterations ), The current number of times of repeated execution ( ) Is incremented by 1, and the calculated ML estimate value ( ) And the re-calculated The ML estimate ( ) Is calculated again and the ML estimate ( );
Lt; RTI ID = 0.0 > (ML) < / RTI > ). ≪ / RTI >
The number of iterations of the setting, which means the number of times the algorithm is repeated. Receiving and storing the input data;
The current number of iterations ) And initializes the received signal ( ), Channel estimation of the transmission channel ( ) Channel estimation error ( ), A transmission channel estimation value of an interference signal ( ), The channel estimation error of the interference signal ( ) The ML estimates of < RTI ID = 0.0 > );
The initial value ( ) Is used to estimate the channel estimation error ) For an instantaneous covariance matrix ( ) Of the diagonal And calculates a channel estimation error (" ) ≪ / RTI > ), And using this, ;
remind , The ML estimate ( ),
Current iteration count ( ) Is set to the number of times of repeated execution ( ), ≪ / RTI >
As a result of the determination, ), The calculated ML estimate value ( ) To the final value of the ML estimate, using the iterative algorithm, the ML estimate < RTI ID = 0.0 > ),
After the determination of the number of times of repeated execution, as a result of the determination, ) And the number of setting iterations ), The current number of times of repeated execution ( ) Is incremented by 1, and the calculated ML estimate value ( ) And the re-calculated The ML estimate ( ) Is calculated again and the ML estimate ( Using the iterative algorithm,
The ML estimate < RTI ID = ). ≪ / RTI >
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Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
KR100605860B1 (en) | 2003-01-09 | 2006-07-31 | 삼성전자주식회사 | Apparatus and method for transmitting in wireless telecommunication system using 4 transmit antennas |
KR100630108B1 (en) | 2002-10-10 | 2006-09-27 | 삼성전자주식회사 | Transmitting and receiving apparatus for supporting transmission antenna diversity using space-time block code |
KR100720872B1 (en) | 2004-11-04 | 2007-05-22 | 삼성전자주식회사 | Transmitting and receiving apparatus and method employing apparatus and method of space time block code for increasing performance |
-
2014
- 2014-07-29 KR KR1020140096833A patent/KR101585675B1/en active IP Right Grant
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
KR100630108B1 (en) | 2002-10-10 | 2006-09-27 | 삼성전자주식회사 | Transmitting and receiving apparatus for supporting transmission antenna diversity using space-time block code |
KR100605860B1 (en) | 2003-01-09 | 2006-07-31 | 삼성전자주식회사 | Apparatus and method for transmitting in wireless telecommunication system using 4 transmit antennas |
KR100720872B1 (en) | 2004-11-04 | 2007-05-22 | 삼성전자주식회사 | Transmitting and receiving apparatus and method employing apparatus and method of space time block code for increasing performance |
Non-Patent Citations (6)
Title |
---|
A. Al-Dweik and A. Shami, Multitone Jamming Rejection of Frequency Hopped OFDM Systems in Wireless Channels, IEEE VTC Fall, 2012. |
Farhoodi, A.et al.; Robust ML Detection Algorithm for MIMO Receivers in Presence of Channel Estimation Error; Personal, Indoor and Mobile Radio Communications, 2006 IEEE 17th International Symposium* * |
Furrer, S. et al.; Multiple-Antenna Signaling Over Fading Channels With Estimated Channel State Information: Performance Analysis; Information Theory, IEEE Transactions on ; June 2007* * |
S. Shahbazpanahi, et al, Robust Adaptive Beamforming for General-Rank Signal Model, IEEE Trans. on Signal Processing, Vol. 51, pp. 2257-2269, Sep. 2003. |
Someya, T. et al.; SAGE algorithm for channel estimation and data detection with tracking the channel variation in MIMO system; Global Telecommunications Conference, 2004. GLOBECOM '04. IEEE(Volume 6)* * |
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. |
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