CN107733487B - Signal detection method and device for large-scale multi-input multi-output system - Google Patents
Signal detection method and device for large-scale multi-input multi-output system Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/0413—MIMO systems
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
- H04B7/0837—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station using pre-detection combining
- H04B7/0842—Weighted combining
- H04B7/0848—Joint weighting
- H04B7/0854—Joint weighting using error minimizing algorithms, e.g. minimum mean squared error [MMSE], "cross-correlation" or matrix inversion
Abstract
The invention provides a signal detection method and a signal detection device for a large-scale multi-input multi-output system, which can perform parallel iterative computation and effectively reduce the complexity of detection computation. The method comprises the following steps: acquiring a channel matrix of a large-scale MIMO uplink system and a receiving signal of a base station side; performing matched filtering on the obtained channel matrix and the received signal to obtain matched filtering output of the received signal and a minimum mean square error filtering matrix; constructing an iterative format of a sending signal according to the obtained matched filtering output of the receiving signal and the minimum mean square error filtering matrix; and iterating according to the iteration format of the constructed transmission signal until a preset iteration termination condition is met, and ending iteration, wherein the value of the currently obtained transmission signal is used as the estimated value of the original transmission signal. The invention relates to the technical field of wireless communication.
Description
Technical Field
The present invention relates to the field of wireless communication technologies, and in particular, to a signal detection method and apparatus for a large-scale mimo system.
Background
A large-scale multi-Input multi-Output (Massive MIMO) system is provided with a large-scale antenna array at a base station side, so that a plurality of user terminals in the same frequency band in a cellular cell are simultaneously served, the spatial freedom of the system is fully explored, the overall utilization rate of frequency spectrum resources is improved, the link reliability is improved, the signal coverage range is enlarged, and the faster information transmission rate is provided.
Although the Massive MIMO has excellent performance, applying the Massive MIMO system to actual wireless transmission still faces great challenges, and one of them is the implementation of signal detection algorithm. With the great increase of the number of antennas, the computational complexity of the conventional linear detection algorithm, such as Minimum Mean Square Error (MMSE) and zero-forcing (ZF) signal detection algorithm, is also greatly increased. The main computational complexity is that the inverse operation is required to be carried out on a K multiplied by K matrix (K is the number of transmitting antennas), and the computational complexity reaches O (K)3). Therefore, when the K value is increased, if a direct inversion calculation method is used, the calculation complexity is very large, and the corresponding hardware system is difficult to implement.
In order to avoid accurate matrix inversion with high complexity, researchers provide a Massive MIMO detection algorithm based on a Gauss-Seidel (GS) iteration method and a sequential super relaxation (SOR) iteration method, linear equation sets are solved through the iteration method, and the complexity of the detection algorithm can be determined by O (K)3) To O (K)2). But GS and SOR iterative detection algorithms cannot perform parallel computations due to the characteristics of the iterative format.
Disclosure of Invention
The invention aims to solve the technical problem of providing a signal detection method and a signal detection device for a large-scale multi-input multi-output system, so as to solve the problems that the traditional linear detection algorithm in the prior art is high in computation complexity, and the GS and SOR iterative detection algorithm cannot perform parallel computation.
To solve the above technical problem, an embodiment of the present invention provides a signal detection method for a large-scale mimo system, including:
acquiring a channel matrix of a large-scale MIMO uplink system and a receiving signal of a base station side;
performing matched filtering on the obtained channel matrix and the received signal to obtain matched filtering output of the received signal and a minimum mean square error filtering matrix;
constructing an iterative format of a sending signal according to the obtained matched filtering output of the receiving signal and the minimum mean square error filtering matrix;
and iterating according to the iteration format of the constructed transmission signal until a preset iteration termination condition is met, and ending iteration, wherein the value of the currently obtained transmission signal is used as the estimated value of the original transmission signal.
Further, the performing matched filtering on the acquired channel matrix and the received signal to obtain a matched filtering output of the received signal and a minimum mean square error filtering matrix includes:
performing matched filtering on the obtained channel matrix and the received signal through a matched filter to obtain matched filtering output y of the received signalMF＝HHy and the minimum mean square error filter matrix W ═ G + N0IK；
Wherein y represents a received signal at the base station side, yMFA matched filter output representing the received signal y, H represents a channel matrix, (-)HDenotes the conjugate transpose, W denotes the minimum mean square error filter matrix, G denotes the Gram matrix, N0Representing the variance of the noise, IKRepresenting a K-dimensional identity matrix, K representing the number of total transmit antennas of the user terminal.
Further, the Gram matrix G is represented as: g ═ HHH；
Where H is a channel matrix of N × K, and N represents the number of receiving antennas at the base station side.
Further, the iterative format of the transmission signal is represented as:
x(m)＝Bx(m-1)+c
wherein, x represents the original sending signal of the user terminal, m represents the mth iteration, B represents the iteration matrix, c represents the iteration vector, k is the fast convergence factor, wjjRepresents the diagonal elements of the minimum mean square error filter matrix W, j 1, 2.
Further, the iterating according to the iteration format of the constructed transmission signal until a preset iteration termination condition is satisfied and the iterating is ended, where the currently obtained value of the transmission signal as the estimated value of the original transmission signal includes:
iteration is carried out according to the iteration format of the constructed sending signal, when the current iteration number reaches the preset maximum iteration number L, the iteration is ended, and the value x of the currently obtained sending signal(L)Is an estimate of the originally transmitted signal.
An embodiment of the present invention further provides a signal detection apparatus for a large-scale mimo system, including:
an obtaining unit, configured to obtain a channel matrix of a massive mimo uplink system and a received signal at a base station side;
the matched filter is used for performing matched filtering on the acquired channel matrix and the received signal to obtain matched filtering output of the received signal and a minimum mean square error filtering matrix;
the construction unit is used for constructing an iterative format of a sending signal according to the obtained matched filtering output of the receiving signal and the minimum mean square error filtering matrix;
and the determining unit is used for iterating according to the iteration format of the constructed transmission signal until a preset iteration termination condition is met and the iteration is ended, and the value of the currently obtained transmission signal is used as the estimated value of the original transmission signal.
Further, the matched filter is configured to perform matched filtering on the acquired channel matrix and the received signal to obtain a matched filtering output y of the received signalMF＝HHy and the minimum mean square error filter matrix W ═ G + N0IK；
Wherein y represents a received signal at the base station side, yMFA matched filter output representing the received signal y, H represents a channel matrix, (-)HDenotes the conjugate transpose, W denotes the minimum mean square error filter matrix, G denotes the Gram matrix, N0Representing the variance of the noise, IKRepresenting a K-dimensional identity matrix, K representing the number of total transmit antennas of the user terminal.
Further, the Gram matrix G is represented as: g ═ HHH；
Where H is a channel matrix of N × K, and N represents the number of receiving antennas at the base station side.
Further, the iterative format of the transmission signal is represented as:
x(m)＝Bx(m-1)+c
wherein, x represents the original sending signal of the user terminal, m represents the mth iteration, B represents the iteration matrix, c represents the iteration vector, k is the fast convergence factor, wjjRepresents the diagonal elements of the minimum mean square error filter matrix W, j 1, 2.
Further, the determining unit is specifically configured to perform iteration according to the iteration format of the constructed transmission signal, and when the current iteration number reaches a preset maximum iteration number L, end the iteration, and obtain a value x of the currently obtained transmission signal(L)Is an estimate of the originally transmitted signal.
The technical scheme of the invention has the following beneficial effects:
in the scheme, a channel matrix of a large-scale MIMO uplink system and a receiving signal of a base station side are obtained; performing matched filtering on the obtained channel matrix and the received signal to obtain matched filtering output of the received signal and a minimum mean square error filtering matrix; constructing an iterative format of a sending signal according to the obtained matched filtering output of the receiving signal and the minimum mean square error filtering matrix; and iterating according to the iteration format of the constructed transmission signal until a preset iteration termination condition is met, and ending iteration, wherein the value of the currently obtained transmission signal is used as the estimated value of the original transmission signal. Therefore, the method for determining the estimated value of the original sending signal by utilizing the matched filtering output of the receiving signal and the iterative format of the sending signal constructed by the minimum mean square error filtering matrix can carry out parallel iterative computation, can effectively reduce the complexity of detection computation, improve the convergence speed, does not influence the error performance of detection, is more beneficial to the realization of a hardware platform, and can flexibly change the iteration times in the iterative computation, thereby realizing different computation precisions and being capable of adapting to the performance requirements in different application scenes.
Drawings
Fig. 1 is a schematic flowchart of a signal detection method of a large-scale mimo system according to an embodiment of the present invention;
fig. 2 is a schematic diagram illustrating the comparison of the computation complexity of the fast linear iterative detection method and the direct inversion MMSE detection algorithm, GS iterative detection algorithm, and SOR iterative detection algorithm in the real number domain according to the embodiment of the present invention;
fig. 3 is a schematic diagram illustrating a comparison of the bit error rate when the maximum number of iterations is 2 by using the fast linear iterative detection method provided by the embodiment of the present invention when the total number of transmit antennas of all user terminals in the base station is 32 and the number of receive antennas of the base station is 256;
fig. 4 is a schematic diagram illustrating a comparison of the bit error rate when the maximum number of iterations is 3 by using the fast linear iterative detection method provided by the embodiment of the present invention when the total number of transmit antennas of all user terminals in the base station is 32 and the number of receive antennas of the base station is 256;
fig. 5 is a schematic structural diagram of a signal detection apparatus of a large-scale mimo system according to an embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
The invention provides a signal detection method and a signal detection device for a large-scale multi-input multi-output system, aiming at the problems that the traditional linear detection algorithm is high in computation complexity and the GS and SOR iterative detection algorithm cannot perform parallel computation.
Example one
As shown in fig. 1, a signal detection method of a large-scale mimo system according to an embodiment of the present invention includes:
s101, acquiring a channel matrix of a large-scale MIMO uplink system and a receiving signal of a base station side;
s102, performing matched filtering on the acquired channel matrix and the received signal to obtain matched filtering output of the received signal and a minimum mean square error filtering matrix;
s103, constructing an iterative format of a sending signal according to the obtained matched filtering output of the receiving signal and the minimum mean square error filtering matrix;
and S104, iterating according to the iteration format of the constructed transmission signal until a preset iteration termination condition is met, and ending iteration, wherein the value of the currently obtained transmission signal is used as the estimated value of the original transmission signal.
The signal detection method of the large-scale MIMO system of the embodiment of the invention obtains the channel matrix of the large-scale MIMO uplink system and the receiving signal of the base station side; performing matched filtering on the obtained channel matrix and the received signal to obtain matched filtering output of the received signal and a minimum mean square error filtering matrix; constructing an iterative format of a sending signal according to the obtained matched filtering output of the receiving signal and the minimum mean square error filtering matrix; and iterating according to the iteration format of the constructed transmission signal until a preset iteration termination condition is met, and ending iteration, wherein the value of the currently obtained transmission signal is used as the estimated value of the original transmission signal. Therefore, the method for determining the estimated value of the original sending signal by utilizing the matched filtering output of the receiving signal and the iterative format of the sending signal constructed by the minimum mean square error filtering matrix can carry out parallel iterative computation, can effectively reduce the complexity of detection computation, improve the convergence speed, does not influence the error performance of detection, is more beneficial to the realization of a hardware platform, and can flexibly change the iteration times in the iterative computation, thereby realizing different computation precisions and being capable of adapting to the performance requirements in different application scenes.
The signal detection method of the large-scale multi-input multi-output system provided by the embodiment of the invention can also be called a Massive MIMO rapid linear iterative detection method.
In order to better understand the method for rapid linear iterative detection of Massive MIMO described in the embodiments of the present invention, a Massive MIMO channel model may be established for analog operation, in an uplink transmission (uplink transmission refers to that a user terminal sends a signal and a base station receives a signal), a large-scale antenna array needs to be configured on a base station side to simultaneously provide service for a plurality of user terminals in the same frequency band, each user terminal is configured with a plurality of antennas and performs multi-stream transmission, and a receiver on the base station side restores an original sending signal (where the original sending signal is represented in a vector form) by using the method for rapid linear iterative detection of Massive MIMO described in the embodiments of the present invention according to a receiving signal (where the receiving signal is represented in a vector form) and a channel matrix; specifically, the method comprises the following steps:
in a Massive MIMO uplink system, a base station is provided with N receiving antennas, the total number of transmitting antennas of a user terminal is K, and the number N of the receiving antennas is greater than the number K of the transmitting antennas; and mapping parallel bit streams generated by the M multi-antenna user terminals into a planet seat symbol, and then adopting constellation diagram set energy normalization. Using x ═ x1,x2,...,xK]TThe original sending signal of the user terminal is shown, x comprises the transmission symbols of K transmitting antennas, and the 16-QAM mode is adopted for constellation mapping. H denotes a channel matrix whose dimension is N × K, and thus the received signal y on the base station side can be expressed as:
y＝Hx+n
wherein the received signal y is a vector of N × 1, and y ═ y1,y2,...,yN]T(ii) a An additive white Gaussian noise vector with N being Nx 1, whose elements obey a zero mean variance of N0The task of uplink multiuser signal detection is that a receiver at the base station side receives a signal y ═ y1,y2,...,yN]TThe original transmitted signal x of the user terminal is estimated. Assuming that a channel matrix H is known and is a flat Rayleigh fading channel, elements of the channel matrix H are subject to independent same distribution with the mean value of 0 and the variance of 1, and the Minimum Mean Square Error (MMSE) linear detection theory is adopted to estimate the vector of an original transmission signal
Expressed as:
wherein, (.)HDenotes conjugate transpose, yMFRepresenting the matched filtered output of the received signal, yMFExpressed in vector form, W represents the minimum mean square error filter matrix, N0Representing the variance of the noise, IKRepresenting a K-dimensional identity matrix, K representing the number of total transmit antennas of the user terminal.
If makeCalculated by direct inversion of W
The complexity is O (K)3) When the K value is increased, the complexity of the method is exponentially increased, which is not beneficial to the realization of a hardware system. To reduce the computational complexity, the present embodiment uses an iterative approach to solve
The direct inversion operation of the matrix is avoided.
Solving the above equation
The process of (a) can be equivalent to solving a linear equation:
solving using an iterative method
The iterative format of the transmission signal needs to be constructed, and the result obtained by the mth iteration is:
x(m)＝Bx(m-1)+c
where B denotes an iteration matrix and c denotes an iteration vector.
The iteration matrix B has a great influence on the convergence rate of the iterative algorithm, so that an iteration matrix B capable of rapidly converging the iterative algorithm needs to be constructed, a new iteration format is given by using the sum of elements on the diagonal of the MMSE filter matrix W, and a rapid convergence factor k is added in the iteration format to obtain the iteration matrix B and an iteration vector c:
wherein K is more than or equal to 1 and less than or equal to K, wjjRepresents the diagonal elements of the minimum mean square error filter matrix W, j 1, 2.
In this embodiment, as an optional embodiment, the performing matched filtering on the acquired channel matrix and the received signal to obtain a matched filtering output of the received signal and a minimum mean square error filtering matrix includes:
performing matched filtering on the obtained channel matrix and the received signal through a matched filter to obtain matched filtering output y of the received signalMF＝HHy and the minimum mean square error filter matrix W ═ G + N0IK；
Wherein y represents a received signal at the base station side, yMFA matched filter output representing the received signal y, H represents a channel matrix, (-)HDenotes the conjugate transpose, W denotes the minimum mean square error filter matrix, G denotes the Gram matrix, N0Representing the variance of the noise, IKRepresenting a K-dimensional identity matrix, K representing the number of total transmit antennas of the user terminal.
In this embodiment, the base station serves M user terminals simultaneously, and the number of antennas configured for the ith user terminal is KiTotal number of transmitting antennas
In this embodiment, as a further optional embodiment, the Gram matrix G is represented as: g ═ HHH；
Where H is a channel matrix of N × K, and N represents the number of receiving antennas at the base station side.
In this embodiment, as a further optional embodiment, the iterative format of the transmission signal is represented as:
x(m)＝Bx(m-1)+c
wherein, x represents the original sending signal of the user terminal, m represents the mth iteration, B represents the iteration matrix, c represents the iteration vector, k is the fast convergence factor, wjjRepresents the diagonal elements of the minimum mean square error filter matrix W, j 1, 2.
In the present embodiment, K is 1 ≦ K, and K may be made, for example.
In this embodiment, as an optional embodiment, the iterating according to the iteration format of the constructed transmission signal until a preset iteration termination condition is satisfied and the iterating is ended, where the currently obtained value of the transmission signal as the estimated value of the original transmission signal includes:
iteration is carried out according to the iteration format of the constructed sending signal, when the current iteration number reaches the preset maximum iteration number L, the iteration is ended, and the value x of the currently obtained sending signal(L)Is an estimate of the originally transmitted signal.
In this embodiment, the preset maximum number of iterations is set to L, and the iteration format x of the constructed transmission signal is used(m)＝Bx(m-1)+ c, solving the vector x for the K x 1 dimension(0)Carrying out iterative computation, and finishing iteration when the value of m is L, wherein x is the value(L)I.e. an estimate of the originally transmitted signal
At this time, the number of multiplications required is (LK)2+ K), the computational complexity is O (K) since L is small and does not increase with increasing K2)。
The Massive MIMO rapid linear iteration detection method provided by the embodiment of the invention can flexibly change the iteration times in the iterative computation, thereby realizing different computation precisions and being capable of adapting to performance requirements in different application scenes.
In this embodiment, when the number of iterations is 2 and 4, respectively, the comparison results of the computation complexity (the number of real multiplications) of different algorithms are shown in fig. 2, and as can be seen from fig. 2, the complexity of the Massive MIMO fast linear iterative detection method according to the embodiment of the present invention is much lower than the MMSE detection algorithm of direct inversion, and is close to the complexity of GS and SOR iterative detection algorithms; in view of parallel computing, the Massive MIMO rapid linear iterative detection method provided by the embodiment of the invention can perform parallel computing well, while GS and SOR iterative detection algorithms cannot perform parallel computing. When the antenna is configured to be a masivemimo system of 256 × 32, 16-QAM mapping is adopted, the simulation results are shown in fig. 3 and fig. 4, and as can be seen from the Bit Error Rate (BER) comparison results obtained in fig. 3 and fig. 4, the masivemimo fast linear iterative detection method disclosed by the embodiment of the invention is superior to a GS iterative detection algorithm and an SOR iterative detection algorithm in detection performance.
In summary, the method for rapid linear iterative detection of Massive MIMO according to the embodiments of the present invention not only effectively reduces the computational complexity of detecting the originally transmitted signal by Massive MIMO, but also does not affect the error performance of detection; meanwhile, the iteration times can be flexibly changed in the iterative computation, so that different computation accuracies are realized, and the performance requirements in different application scenes can be met; compared with other iterative algorithms, the Massive MIMO rapid linear iterative detection method disclosed by the embodiment of the invention has higher convergence speed, can perform parallel computation and is more beneficial to realization of a hardware platform.
Example two
The present invention further provides a specific embodiment of a signal detection apparatus for a large-scale mimo system, which corresponds to the specific embodiment of the signal detection method for a large-scale mimo system, and the signal detection apparatus for a large-scale mimo system provided by the present invention can achieve the object of the present invention by executing the process steps in the specific embodiment of the method, so the explanation in the specific embodiment of the signal detection method for a large-scale mimo system is also applicable to the specific embodiment of the signal detection apparatus for a large-scale mimo system provided by the present invention, and will not be repeated in the following specific embodiments of the present invention.
As shown in fig. 5, an embodiment of the present invention further provides a signal detection apparatus for a large-scale mimo system, including:
an obtaining unit 11, configured to obtain a channel matrix of a massive mimo uplink system and a received signal at a base station side;
a matched filter 12, configured to perform matched filtering on the obtained channel matrix and the received signal to obtain a matched filtering output of the received signal and a minimum mean square error filtering matrix;
a constructing unit 13, configured to construct an iterative format of a transmitted signal according to the obtained matched filtering output of the received signal and a minimum mean square error filtering matrix;
and the determining unit 14 is configured to perform iteration according to the constructed iteration format of the transmission signal until a preset iteration termination condition is met, and terminate the iteration, where a value of the currently obtained transmission signal is used as an estimated value of the original transmission signal.
The signal detection device of the large-scale multi-input multi-output system of the embodiment of the invention obtains a channel matrix of the large-scale multi-input multi-output uplink system and a receiving signal at a base station side; performing matched filtering on the obtained channel matrix and the received signal to obtain matched filtering output of the received signal and a minimum mean square error filtering matrix; constructing an iterative format of a sending signal according to the obtained matched filtering output of the receiving signal and the minimum mean square error filtering matrix; and iterating according to the iteration format of the constructed transmission signal until a preset iteration termination condition is met, and ending iteration, wherein the value of the currently obtained transmission signal is used as the estimated value of the original transmission signal. Therefore, the method for determining the estimated value of the original sending signal by utilizing the matched filtering output of the receiving signal and the iterative format of the sending signal constructed by the minimum mean square error filtering matrix can carry out parallel iterative computation, can effectively reduce the complexity of detection computation, improve the convergence speed, does not influence the error performance of detection, is more beneficial to the realization of a hardware platform, and can flexibly change the iteration times in the iterative computation, thereby realizing different computation precisions and being capable of adapting to the performance requirements in different application scenes.
In an embodiment of the foregoing signal detection apparatus for a large-scale multiple-input multiple-output system, further, the matched filter is configured to perform matched filtering on the acquired channel matrix and the received signal to obtain a matched filtering output y of the received signalMF＝HHy and the minimum mean square error filter matrix W ═ G + N0IK；
Wherein y represents a received signal at the base station side, yMFA matched filter output representing the received signal y, H represents a channel matrix, (-)HDenotes the conjugate transpose, W denotes the minimum mean square error filter matrix, G denotes the Gram matrix, N0Representing the variance of the noise, IKRepresenting a K-dimensional identity matrix, K representing the number of total transmit antennas of the user terminal.
In an embodiment of the foregoing signal detection apparatus for a large-scale mimo system, further, the Gram matrix G is represented as: g ═ HHH；
Where H is a channel matrix of N × K, and N represents the number of receiving antennas at the base station side.
In an embodiment of the foregoing signal detection apparatus for a large-scale multiple-input multiple-output system, further, the iterative format of the transmission signal is represented as:
x(m)＝Bx(m-1)+c
wherein, x represents the original sending signal of the user terminal, m represents the mth iteration, B represents the iteration matrix, c represents the iteration vector, k is the fast convergence factor, wjjRepresents the diagonal elements of the minimum mean square error filter matrix W, j 1, 2.
In an embodiment of the signal detection apparatus of the large-scale multiple-input multiple-output system, the determining unit is specifically configured to perform iteration according to an iteration format of the constructed transmission signal, and when a current iteration number reaches a preset maximum iteration number L, end the iteration, and obtain a value x of the currently obtained transmission signal(L)Is an estimate of the originally transmitted signal.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (6)
1. A signal detection method for a large-scale multiple-input multiple-output system is characterized by comprising the following steps:
acquiring a channel matrix of a large-scale MIMO uplink system and a receiving signal of a base station side;
performing matched filtering on the obtained channel matrix and the received signal to obtain matched filtering output of the received signal and a minimum mean square error filtering matrix;
constructing an iterative format of a sending signal according to the obtained matched filtering output of the receiving signal and the minimum mean square error filtering matrix;
iteration is carried out according to the iteration format of the constructed sending signal until a preset iteration termination condition is met, and the iteration is ended, wherein the value of the currently obtained sending signal is used as the estimated value of the original sending signal;
the performing matched filtering on the obtained channel matrix and the received signal to obtain a matched filtering output and a minimum mean square error filtering matrix of the received signal includes:
performing matched filtering on the obtained channel matrix and the received signal through a matched filter to obtain matched filtering output y of the received signalMF＝HHy and the minimum mean square error filter matrix W ═ G + N0IK；
Wherein y represents a received signal at the base station side, yMFA matched filter output representing the received signal y, H represents a channel matrix, (-)HDenotes the conjugate transpose, W denotes the minimum mean square error filter matrix, G denotes the Gram matrix, N0Representing the variance of the noise, IKExpressing a K-dimensional identity matrix, wherein K expresses the total number of transmitting antennas of the user terminal;
the iterative format of the transmitted signal is represented as:
x(m)＝Bx(m-1)+c
wherein, x represents the original sending signal of the user terminal, m represents the mth iteration, B represents the iteration matrix, c represents the iteration vector, k is the fast convergence factor, wjjRepresents the diagonal elements of the minimum mean square error filter matrix W, j 1, 2.
2. The signal detection method of the massive multiple-input multiple-output system according to claim 1, wherein the Gram matrix G is represented as: g ═ HHH；
Where H is a channel matrix of N × K, and N represents the number of receiving antennas at the base station side.
3. The signal detection method of the massive multiple-input multiple-output system according to claim 1, wherein the iterating according to the iteration format of the constructed transmission signal until a preset iteration termination condition is satisfied and the iterating is ended, and the step of using the value of the currently obtained transmission signal as the estimated value of the original transmission signal comprises:
iteration is carried out according to the iteration format of the constructed sending signal, when the current iteration number reaches the preset maximum iteration number L, the iteration is ended, and the value x of the currently obtained sending signal(L)Is an estimate of the originally transmitted signal.
4. A signal detection apparatus for a large-scale mimo system, comprising:
an obtaining unit, configured to obtain a channel matrix of a massive mimo uplink system and a received signal at a base station side;
the matched filter is used for performing matched filtering on the acquired channel matrix and the received signal to obtain matched filtering output of the received signal and a minimum mean square error filtering matrix;
the construction unit is used for constructing an iterative format of a sending signal according to the obtained matched filtering output of the receiving signal and the minimum mean square error filtering matrix;
the determining unit is used for iterating according to the iteration format of the constructed sending signal until a preset iteration termination condition is met and the iteration is finished, and the value of the currently obtained sending signal is used as the estimated value of the original sending signal;
the matched filter is used for performing matched filtering on the acquired channel matrix and the received signal to obtain matched filtering output y of the received signalMF＝HHy and the minimum mean square error filter matrix W ═ G + N0IK；
Wherein y represents a received signal at the base station side, yMFA matched filter output representing the received signal y, H represents a channel matrix, (-)HDenotes the conjugate transpose, W denotes the minimum mean square error filter matrix, G denotes the Gram matrix, N0Representing the variance of the noise, IKExpressing a K-dimensional identity matrix, wherein K expresses the total number of transmitting antennas of the user terminal;
the iterative format of the transmitted signal is represented as:
x(m)＝Bx(m-1)+c
wherein, x represents the original sending signal of the user terminal, m represents the mth iteration, B represents the iteration matrix, c represents the iteration vector, k is the fast convergence factor, wjjRepresents the diagonal elements of the minimum mean square error filter matrix W, j 1, 2.
5. The apparatus for signal detection of massive multiple-input multiple-output system according to claim 4, wherein the Gram matrix G is represented as: g ═ HHH；
Where H is a channel matrix of N × K, and N represents the number of receiving antennas at the base station side.
6. The apparatus according to claim 4, wherein the determining unit is specifically configured to perform iteration according to an iteration format of the constructed transmission signal, and when a current iteration count reaches a preset maximum iteration count L, the iteration is ended, and a value x of the currently obtained transmission signal is obtained(L)Is an estimate of the originally transmitted signal.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN101582742A (en) * | 2009-06-16 | 2009-11-18 | 北京邮电大学 | Method for detecting iteration of multiple input multiple output (MIMO) system, system thereof and device thereof |
| US8331477B2 (en) * | 2009-07-16 | 2012-12-11 | Industrial Technology Research Institute | Progressive parallel interference canceller and method and receiver thereof |
| CN105553614A (en) * | 2015-12-14 | 2016-05-04 | 清华大学 | Integration device based on signal detection algorithm |
| CN105933046A (en) * | 2016-06-24 | 2016-09-07 | 北京科技大学 | Massive multiple-input multiple-output system baseband and radio frequency hybrid pre-coding method |
| CN106850017A (en) * | 2017-03-06 | 2017-06-13 | 东南大学 | Extensive MIMO detection algorithms and hardware structure based on parallel GS iteration |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN101582742A (en) * | 2009-06-16 | 2009-11-18 | 北京邮电大学 | Method for detecting iteration of multiple input multiple output (MIMO) system, system thereof and device thereof |
| US8331477B2 (en) * | 2009-07-16 | 2012-12-11 | Industrial Technology Research Institute | Progressive parallel interference canceller and method and receiver thereof |
| CN105553614A (en) * | 2015-12-14 | 2016-05-04 | 清华大学 | Integration device based on signal detection algorithm |
| CN105933046A (en) * | 2016-06-24 | 2016-09-07 | 北京科技大学 | Massive multiple-input multiple-output system baseband and radio frequency hybrid pre-coding method |
| CN106850017A (en) * | 2017-03-06 | 2017-06-13 | 东南大学 | Extensive MIMO detection algorithms and hardware structure based on parallel GS iteration |
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