WO2011149221A2 - Procédé et dispositif de décodage pour système entrée multiple sortie multiple - Google Patents

Procédé et dispositif de décodage pour système entrée multiple sortie multiple Download PDF

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Publication number
WO2011149221A2
WO2011149221A2 PCT/KR2011/003739 KR2011003739W WO2011149221A2 WO 2011149221 A2 WO2011149221 A2 WO 2011149221A2 KR 2011003739 W KR2011003739 W KR 2011003739W WO 2011149221 A2 WO2011149221 A2 WO 2011149221A2
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transmission symbol
transmission
node
intermediate variables
soft decision
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PCT/KR2011/003739
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English (en)
Korean (ko)
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WO2011149221A3 (fr
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윤석현
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단국대학교 산학협력단
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Priority claimed from KR1020110004888A external-priority patent/KR101198396B1/ko
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Publication of WO2011149221A2 publication Critical patent/WO2011149221A2/fr
Publication of WO2011149221A3 publication Critical patent/WO2011149221A3/fr

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L1/00—Arrangements for detecting or preventing errors in the information received
    • H04L1/004—Arrangements for detecting or preventing errors in the information received by using forward error control
    • H04L1/0045—Arrangements at the receiver end
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L1/00—Arrangements for detecting or preventing errors in the information received
    • H04L1/20—Arrangements for detecting or preventing errors in the information received using signal quality detector

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  • the disclosed technique relates to a decoder and a decoding method, and more particularly, but not exclusively, to a decoder and decoding method for a multiple input multiple output system.
  • a multiple input multiple output (MIMO) system is a core technology of wireless digital communication system, and many related transmission and reception techniques are being studied.
  • MIMO multiple input multiple output
  • the reception techniques for processing signals at the receiving end include a linear detection technique, a maximum likelihood detection (ML) technique, a successive interference cancellation (SIC) technique, a sphere decoding technique .
  • ML maximum likelihood detection
  • SIC successive interference cancellation
  • examples of the linear detection technique include ZF (zero forcing) technique and a minimum mean square error (MMSE) technique.
  • the ML technique can improve performance by selecting all of the transmittable symbols from all transmit antennas and selecting the least squared Euclidian distance input.
  • the complexity increases exponentially with the number of transmit antennas and modulation size.
  • the SIC technique is a technique for enhancing performance by preferentially detecting and removing transmission data having a large signal-to-interference plus noise ratio (SINR).
  • SINR signal-to-interference plus noise ratio
  • ordering is separately required in order to obtain the best performance.
  • the SIC scheme requires an optimal reception algorithm capable of achieving channel capacity, and a sequential interference cancellation on a block-by-block basis.
  • the ML technique has a good performance in terms of the frame error rate, it has a problem that it is difficult to apply to an actual receiver because the computational complexity is too high.
  • a method for decoding a received signal by a receiver for a multiple-input multiple-output system comprising the steps of: Calculating an effective reception signal, an effective channel coefficient, and an effective noise plus interference power (hereinafter, parameters) used for generating a determination value; Wherein each of the transmission symbol nodes corresponding to the transmission symbols is calculated at a transmission symbol node through a translation operation according to the parameters, Calculating second intermediate variables from the first intermediate variables provided to the second intermediate variables; And combining the calculated second intermediate variables to generate a soft decision value of each transmission symbol.
  • a channel estimator for estimating a channel matrix from a received signal.
  • a linear processing unit for calculating an effective reception signal, an effective channel coefficient, and an effective noise + interference power (hereinafter, parameters) used for generating a soft decision value of transmission symbols based on the reception signal and the channel matrix; And for each transmission symbol among the transmission symbols, first intermediate variables for each transmission symbol of other transmission symbols, second intermediate variables calculated by performing a translation operation according to the parameters, And a joint decoding unit for performing soft combining of the transmission symbols by performing inter-symbol combining.
  • Embodiments of the disclosed technique may have effects that include the following advantages. It should be understood, however, that the scope of the disclosed technology is not to be construed as limited thereby, since the embodiments of the disclosed technology are not meant to include all such embodiments.
  • the decoder and decoding method according to an embodiment of the disclosed technology has an advantage of relatively high performance and low computational complexity.
  • the decoder according to the disclosed technique has a much lower computational complexity than the ML / MAP-based decoder.
  • the decoder according to the disclosed technology has an advantage over the sphere decoding that exhibits performance close to that of the ML-based decoder, and has hardware advantages compared with the spear decoding.
  • 1 is a diagram for explaining a graph model for an Nx4 MIMO channel.
  • FIG. 2 is a diagram for explaining a method of processing messages between nodes in the graph of FIG.
  • FIG. 3 is a block diagram illustrating a decoder according to an embodiment of the disclosed technique.
  • FIG. 4 is a block diagram for explaining a linear processing unit according to an embodiment.
  • FIG. 5 is a block diagram for explaining a joint decoding unit according to an embodiment.
  • FIG. 6 is a flowchart illustrating a decoding method according to an embodiment of the disclosed technique.
  • first " first ", " second ", and the like are used to distinguish one element from another and should not be limited by these terms.
  • first component may be referred to as a second component
  • second component may also be referred to as a first component.
  • each step may take place differently from the stated order unless explicitly stated in a specific order in the context. That is, each step may occur in the same order as described, may be performed substantially concurrently, or may be performed in reverse order.
  • a multiple input multiple output (MIMO) system transmits multiple (e.g., M, M is a natural number) transmission symbols simultaneously and a plurality (e.g., N.N is a natural number) And restores the original data from the received symbols.
  • MIMO multiple input multiple output
  • N.N is a natural number
  • the received symbol received through the channel in the MIMO system can be expressed as Equation (1).
  • x is x
  • each element j of the transmission symbol vector x of Mx1 has a QPSK, 16QAM, one element of the S modulation symbols to a set of modulation symbols such as 64QAM with a value.
  • H is the channel matrix of NxM
  • n is the noise vector of Nx1
  • y is the received symbol vector of Nx1.
  • a decoder and a decoding method for a MIMO system according to the disclosed technique are comprised of a linear processing and a combining decoding process.
  • the linear processing the values of the parameters required in the joint decoding process are calculated.
  • the decoder may perform linear processing of the MMSE equalization form.
  • a soft-decision value for each transmission symbol is generated based on a belief propagation algorithm between transmission symbols.
  • the decoder generates a soft-decision value for each transmission symbol through message-passing between transmission symbols corresponding to the transmission symbol. At this time, message transmission between nodes is performed between all nodes, and a soft decision value of each node is generated through one or more repetitive message transmission and message update.
  • the joint decoding process can be expressed as a message processing on a graph composed of M nodes representing transmission symbols of each antenna and a branch having a direction connecting each node (Directed Edge).
  • a full-connected graph and a ring-type graph-based reception algorithm will be described in this specification, but the present invention is also applicable to a general graph model.
  • FIG. 1 is a diagram for explaining a graph model for an Nx4 MIMO channel.
  • 1 (a) shows a fully-connected graph model for an Nx4 MIMO channel
  • FIG. 1 (b) shows a ring graph model for an Nx4 MIMO channel.
  • the graph model of FIG. 1 is a conceptual graph model for explaining the trust propagation algorithm in the present invention, and there is no physical entity.
  • the graph model shown in FIGS. 1 (a) and 1 (b) includes a plurality of nodes 110, 112, 114, and 116 and branches (edges: 120 and 122) connecting these nodes. Each node represents a transmission symbol node corresponding to a transmission symbol of the MIMO system.
  • FIG. 1 is a diagram for explaining a graph model for an Nx4 MIMO channel.
  • 1 (a) shows a fully-connected graph model for an Nx4 MIMO channel
  • FIG. 1 (b) shows a ring graph model for an Nx4 MIMO channel.
  • M 4
  • transmit symbol nodes 110, 112, 114, and 116 are shown.
  • branches are connected to all the nodes except for itself.
  • the neighbor nodes ie, the nodes connected to the front and back
  • Branches 120, 122, etc. are directed edges, always in pairs.
  • edge ij a branch connected from the x i node to the x j node.
  • FIG. 2 is a diagram for explaining a method of processing messages between nodes in the graph of FIG. 2 (a) shows a message processing process in a fully-connected graph model, and FIG. 2 (b) shows a message processing process in a ring graph model.
  • the joint decoding process can be described by message processing on the graph.
  • a message processing procedure in the fully-connected graph model and the ring graph model illustrated in FIG. 1 will be described with reference to FIG.
  • ⁇ i ⁇ j (x i ) is the first intermediate variable that is used in combination decoding process
  • ⁇ i ⁇ j (x j ) is a second intermediate variable that is used in combination decoding process.
  • ⁇ i ⁇ j ( x i ) and ⁇ i ⁇ j ( x j ) denote , for each element of the set of modulation symbols S, a confidence function that represents the probability of x i and x j having that element as a value, (Belief) form.
  • T i ⁇ j (x i, x j) is a translation function (Translation function) to translate messages ⁇ i ⁇ j (x i) to j from the node i is a function that generates a ⁇ i ⁇ j (x j).
  • T 1 ⁇ 4 (x 1 , x 4 ) translates the message ⁇ 1 ⁇ 4 ( x 1 ) from node x 1 to node x 4 to produce ⁇ 1 ⁇ 4 ( x 4 ).
  • a detailed description of the translation function will be given later.
  • the decoder 300 according to the disclosed technique generates a soft decision value for each transmission symbol from a received symbol through linear processing and joint decoding in a MIMO system. For example, the decoder 300 may generate a soft decision value through message-passing between transmission symbol nodes based on a graph model. In the linear process, a value of parameters required in the joint decoding process is calculated, In the joint decoding process, the processes of updating and delivering messages are repeated according to a bilateral message (ie, intermediate variable) transmission algorithm of each node.
  • a bilateral message ie, intermediate variable
  • the decoder 300 Upon completion of the iterative message transmission of a predetermined number of times (or until a predetermined condition is satisfied), the decoder 300 calculates and outputs a final soft decision value for each node.
  • the decoder 300 includes a demultiplexer 310, a channel estimator 320, a linear processor 330, and a joint decoder 340.
  • the demultiplexer 310 switches the received signal vector r of Nx1 input from the MIMO channel at a predetermined timing and transmits the input corresponding to the pilot to the channel estimator 320 and the received symbol y corresponding to the data to a linear processing unit 330).
  • the appropriate predetermined time may be a time point at which a symbol including a pilot is received from the transmitter.
  • the channel estimator 320 estimates a channel matrix H from the received signal.
  • the channel estimator 320 estimates a channel matrix from a received signal vector corresponding to the pilot provided from the demultiplexer 310 and provides the estimated channel matrix to the linear processing unit 330 for use in decoding the received symbol y do.
  • the channel estimation algorithm that can be used in the channel estimation unit 320 may include known channel estimation algorithms, and any person skilled in the art can understand it without any particular limitation.
  • the linear processing unit 330 calculates parameters used for generating a soft decision value of transmission symbols based on the received signal and the estimated channel matrix.
  • the linear processing unit 330 generates necessary parameters in the joint decoding unit 340 using the received symbol y provided from the demultiplexer 310 and the channel matrix H provided from the channel estimation unit 320.
  • the parameters required for the configuration of the effective received signal translation function T i ⁇ j (x i, x j) of the combined decode unit 340 ( ), Effective channel coefficient ( ) And effective noise + interference power ( ).
  • the first intermediate variables can be expressed as ⁇ i ⁇ j ( x i )
  • the second intermediate variables can be expressed as ⁇ i ⁇ j ( x j ).
  • the second intermediate variables are calculated by performing a translation operation on the first intermediate variables. The translation operation is performed according to the parameters calculated by the linear processing unit 330, and a detailed description will be given later with reference to FIG. 5 and FIG.
  • the decoder 300 may further include a log likelihood ratio (LLR) generator (not shown).
  • LLR log likelihood ratio
  • the LLR generator calculates an LLR value for each bit (k-th bit of the j-th symbol, b jk ) of each transmission symbol from the soft decision value generated by the joint decoding unit 340.
  • the decoder 300 may further include a memory (not shown) for storing parameters and first and second intermediate variables calculated by the linear processing unit 330 and the joint decoding unit 340.
  • the linear processing unit 330 calculates the parameters required for soft decision value generation and provides the parameters to the joint decoding unit 340. [ The calculated parameters are the effective received signal ( ), Effective channel coefficient ( ) And effective noise + interference power ( ). The linear processing unit 330 calculates parameters using the received symbol y and the channel matrix H. [ According to one embodiment, the linear processing unit 330 computes the parameters as in Equation (2) to Equation (6), similar to minimum mean square error (MMSE) equalization. The effective reception signal can be calculated according to Equation (2).
  • MMSE minimum mean square error
  • a H means Hermitian Transpose of vector a , Means the conditional MMSE filter of x j for a given x i and can be computed according to equation (3).
  • h j denotes the j-th column of the channel matrix H
  • I a partial covariance matrix and can be calculated according to Equation (4).
  • the linear processing unit 330 can calculate the effective reception signal, the effective channel coefficient, and the effective noise + interference power from the reception symbol y and the channel matrix H.
  • the joint decoding unit 340 includes a translation operation unit 510, an external information operation unit 520, and a soft decision operation unit 530.
  • the joint decoding unit 340 generates a soft decision value for each transmission symbol x j (jth element of the transmission symbol x ) from the parameters calculated by the linear processing unit 330 according to Equations (2) to (6).
  • the operations performed by the joint decoding unit 340 are based on the graph model described in FIGS.
  • the translation function used for the translation operation T i ⁇ j (x i, x j) is a two variables function with the parameters calculated from the linear processor 330, and specific description of the translation function was 6 Will be described later.
  • the extrinsic information computation unit 520 recursively updates the extrinsic information, i.e., the value of the first intermediate variable used for generating the soft decision value.
  • the exogenous information operation section 520 performs a coupling between the second intermediate variable ⁇ i ⁇ j (x j), the outer (external) for each of edge ij is calculated to the second intermediate variables, And updates the first intermediate variable ⁇ j ⁇ i ( x j ) provided to the branch edge ji that is paired with each branch edge ij .
  • the external second intermediate variable is one for the edge edge ij , it can be understood that the external second intermediate variable itself is the result of the above-described combining operation.
  • the intermediate variable is ⁇ j + 1 ⁇ j ( x j ).
  • the first intermediate variable ⁇ j ⁇ j-1 ( x j ) provided to a branch edge j (j-1) paired with the corresponding branch is equal to ⁇ j + 1 ⁇ j ( x j ) .
  • the process of calculating the first and second intermediate variables may be performed by a predetermined number of times (for example, 2 to 3 times) or until a predetermined condition is satisfied (for example, when the value of the second intermediate variable is changed And the accuracy of the calculation can be increased.
  • the soft decision operation unit 530 performs a combination between the second intermediate variables calculated by the translation operation unit 510 to generate a soft decision value of each transmission symbol. That is, the soft decision unit 530 combines the second intermediate variables provided through the branches toward each transmission symbol node to generate a soft decision value of each transmission symbol. If the process of calculating the first and second intermediate variables in the translation operation unit 510 and the external information operation unit 520 is repeated two or more times, the soft decision operation unit 530 adds the second intermediate variable calculated in the last iteration Thereby generating a soft decision value.
  • the joint decryption unit 340 may not include the foreign information calculation unit 520.
  • the process of calculating the first and second intermediate variables is not repeated, and the soft decision operation unit 530 can generate the soft decision value by combining the first intermediate variables calculated in the translation operation unit 510 have.
  • FIG. 6 is a flowchart illustrating a decoding method according to an embodiment of the disclosed technique.
  • a method for generating the soft decision value (reliability or posterior probability q j (x j )) of the transmission symbol x j will be described in more detail with reference to FIG. 6.
  • the case where the decoder 300 of FIG. 3 is implemented in a time-series manner also corresponds to the present embodiment, and therefore, the description of the decoder 300 with reference to FIGS. 3 to 5 applies to this embodiment as it is.
  • step S610 the decoder 300 calculates parameters used for generating a soft decision value of transmission symbols based on the received signal and a channel matrix estimated from the received signal.
  • the parameters include the effective received signal, the effective channel coefficient and the effective noise + interference power, and can be calculated according to, for example, Equation (2) to Equation (6) similarly to the MMSE equalization algorithm.
  • the decoder 300 When the parameters are calculated in step S610, the decoder 300 generates a soft decision value of the transmission symbol through steps S620 to S660.
  • step S620 the decoder 300 sets the initial value of the first intermediate variable l i ⁇ j (s).
  • step S630 the decoder 300 computes (or initializes as in step S620) the first intermediate variables provided on the directed edges directed to the respective transmission symbol node And calculates intermediate variables.
  • the decoder 300 may calculate the second intermediate variables from the first intermediate variables as shown in Equation (7) through a translation operation according to the parameters calculated in step S610.
  • T i ⁇ j (x i, x j) is the i-th transmitted symbol translation function of the branches at the edge ij is connected at the node to the j-th transmit symbol node
  • x i is the i-th transmit symbol
  • x j is the j-th transmit symbol
  • l i ⁇ j (x i) is the second intermediate is the i-th transmitted symbol nodes provided in the first intermediate variable
  • ⁇ i ⁇ j (x j) is the j-th transmit symbol node via the edge ij for providing the edge ij
  • the variable a is a normalization constant
  • S is a set of modulation symbols that each transmission symbol can have.
  • Equation (7) is performed on all directed edges directed to x j on the graph.
  • all i? J i.e., all i, j
  • nodes with i, j 1, 2, .., M and j?
  • step S640 the decoder 300 performs a process of calculating an extrinsic information.
  • the decoder 300 calculates a first intermediate variable ⁇ j ⁇ i ( x j ) provided at an edge ji that is paired with an edge ij from an ith node to a jth node, as shown in Equation (9) Can be calculated.
  • Equation 9 is performed for all directed edges starting at x j on the graph.
  • the second intermediate variable is newly calculated based on the updated first intermediate variable (S630), and the first intermediate variable is updated based on the newly calculated second intermediate variable (S640 ) Is repeated a preset number of times. If it is determined in step S650 that the decoder 300 has repeated the steps S630 and S640 a predetermined number of times, the repetition ends. According to another embodiment, unlike the case shown in FIG. 6, the decoder 300 may determine whether to repeat or not according to a predetermined condition, instead of setting the number of repetitions in advance. For example, the decoder 300 may repeat steps S630 and S640 until the value of the second intermediate variable no longer changes.
  • step S660 the decoder 300 combines the recursively calculated second intermediate variables through steps S630 to S650 to generate a soft decision value of each transmission symbol.
  • the soft decision value for each transmission symbol (x j) is as shown in equation (10)
  • q j (x j ) is a soft decision value of x j
  • N (j) is a set of other transmission symbol nodes connected to the j th transmission symbol node
  • ⁇ is a normalization constant
  • y is Is set to an appropriate constant (for example, 1).
  • the decoder 300 may not perform steps S640 and S650, unlike the order of FIG. That is, the decoder 300 may combine the second intermediate variable calculated from the initialized first intermediate variable to generate a soft decision value of the transmission symbol.
  • the decoder 300 may calculate a log likelihood ratio (LLR) value for each bit of the transmission symbol from the soft decision value according to an embodiment . For example, it can be obtained as shown in Equation 11, the LLR values for the respective bit x j b jk (k-th bit of the j th symbols), from the soft decision value for the j-th transmitted symbol (x j).
  • LLR log likelihood ratio
  • d k (x) represents the k-th bit of the transmission symbol x.
  • steps S620 through S660 may be converted into operations in the logarithmic domain, in which case the products are summed and the sum is replaced by the maximum value operation.
  • the operations of the equations (7) to (11) in the logarithmic area are converted into the following equations (12) to (16), respectively.
  • T ' i ⁇ j (x i , x j ) is a function that T i j (x i , x j ) is converted into a logarithmic region, and the constant a' Is set to an appropriate constant (for example, 0).
  • the above-described joint decoding process can be simplified as follows.
  • N i ⁇ x i -1 , x i +1 ⁇
  • the first intermediate variable l i ⁇ j ( s ) provided by the node is l i ⁇ i + 1 ( s ) or l i ⁇ i-1 ( s ).
  • the second intermediate variable ⁇ i ⁇ j ( s ) provided to the ith node is also ⁇ i -1 ⁇ i ( x i ) or ⁇ i + 1 ⁇ i ( x i ). Also, as described in step S640, since only one external second intermediate variable is present for each branch in the ring graph, in the case of the foreign information calculation of Equation (9) And .
  • the first and second intermediate variables are classified into a forward variable according to a message transmission direction, Or a backward variable. . ≪ / RTI >
  • the following ( ⁇ ) M means 1-based modulo operation.
  • the translation function is also defined as a forward translation function F i (x i , x i + 1 ) and a backward translation function B i (x i , x i-1 ) according to the direction of translation operation .
  • Is the translation function at the forward edge connected from the ith transmission symbol node to the (i + 1) th transmission symbol node Denotes a translation function at the backward edge connected from the i-th transmission symbol node to the (i-1) th transmission symbol node.
  • Equation (19) Expressing the intermediate variable and the translation function as shown in Equations (17) and (18), the translation operation of Equation (7) can be expressed as Equation (19).
  • the decoder 300 After such an operation is repeated a predetermined number of times, the decoder 300 generates a soft decision value for each transmission symbol as shown in Equation (20). According to another embodiment, the decoder 300 may repeat this operation until a predetermined condition is satisfied (e.g., until the value of the second intermediate variable no longer changes) And generates a soft decision value for each transmission symbol.
  • Equations (19) and (20) can also be converted into operations in the logarithmic region as shown in Equations (21) and (22).
  • the decoder 300 obtains the LLR value for each bit (kth bit of the jth symbol, b jk ) from the soft decision value.
  • the process of obtaining the LLR value can be similarly calculated by the equation (11) or (16) in the case of the ring type.

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Abstract

La présente invention porte sur un procédé et un dispositif de décodage, plus spécifiquement mais non exclusivement sur un procédé et un dispositif de décodage pour système entrée multiple sortie multiple. Parmi les modes de réalisation de la présente invention, le procédé de décodage d'un signal reçu à l'aide d'un décodeur pour système entrée multiple sortie multiple comprend les étapes suivantes consistant à : calculer un signal reçu effectif, un coefficient de canal effectif et une puissance de bruit et de brouillage effective (appelés paramètres dans ce qui suit), utilisés dans la génération de valeurs de décision souple de symboles envoyés, sur la base d'un signal reçu et d'une matrice de canal estimée à partir du signal reçu; calculer, à partir d'autres nœuds de symbole envoyé, chaque nœud de symbole envoyé des nœuds de symbole envoyé qui correspondent aux symboles envoyés par l'intermédiaire d'une opération de traduction conformément aux paramètres de manière à calculer des secondes variables intermédiaires à partir de premières valeurs intermédiaires qui ont été fournies sur le bord orienté faisant face à chacun des nœuds de symbole envoyé; et une étape de génération des valeurs de décision souple de symboles envoyés par réalisation d'un couplage parmi les secondes variables intermédiaires calculées.
PCT/KR2011/003739 2010-05-27 2011-05-20 Procédé et dispositif de décodage pour système entrée multiple sortie multiple WO2011149221A2 (fr)

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Cited By (1)

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Publication number Priority date Publication date Assignee Title
WO2017172091A1 (fr) * 2016-03-30 2017-10-05 Intel Corporation Sélection de couche initiale dans des systèmes d'élimination d'interférence successifs

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US20060256888A1 (en) * 2005-02-07 2006-11-16 Nissani Nissensohn Daniel N Multi input multi output wireless communication reception method and apparatus
US20080310556A1 (en) * 2007-06-12 2008-12-18 Samsung Electronics Co., Ltd. Apparatus and method for detecting signal in multiple-input multiple-output (MIMO) wireless communication system
US20090190683A1 (en) * 2004-04-22 2009-07-30 Qualcomm Incorporated Mimo receiver using maximum likelihood detector in combination with qr decomposition
US20090252242A1 (en) * 2008-04-02 2009-10-08 Samsung Electronics Co., Ltd. Apparatus and method for detecting signal based on lattice reduction to support different coding scheme for each stream in multiple input multiple output wireless communication system

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US20090190683A1 (en) * 2004-04-22 2009-07-30 Qualcomm Incorporated Mimo receiver using maximum likelihood detector in combination with qr decomposition
US20060256888A1 (en) * 2005-02-07 2006-11-16 Nissani Nissensohn Daniel N Multi input multi output wireless communication reception method and apparatus
US20080310556A1 (en) * 2007-06-12 2008-12-18 Samsung Electronics Co., Ltd. Apparatus and method for detecting signal in multiple-input multiple-output (MIMO) wireless communication system
US20090252242A1 (en) * 2008-04-02 2009-10-08 Samsung Electronics Co., Ltd. Apparatus and method for detecting signal based on lattice reduction to support different coding scheme for each stream in multiple input multiple output wireless communication system

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Publication number Priority date Publication date Assignee Title
WO2017172091A1 (fr) * 2016-03-30 2017-10-05 Intel Corporation Sélection de couche initiale dans des systèmes d'élimination d'interférence successifs

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