JP2010154087A - Receiving device and signal detection method - Google Patents

Abstract

To provide a receiving apparatus capable of improving the transmission characteristics by reducing the influence of co-channel interference while suppressing the amount of calculation of maximum likelihood detection processing.
A channel matrix for an own device is estimated, an interference channel matrix representing transmission path characteristics with the co-channel interference source is estimated, and a correlation matrix of error vectors caused by co-channel interference using the interference channel matrix. The inverse matrix of the correlation matrix is decomposed into a product of the complex matrix and the complex conjugate transpose of the complex matrix, and the matrix obtained by integrating the channel matrix and the complex matrix is unitary matrix and upper or lower triangular matrix Perform a trellis search for a given transmit symbol candidate using the received signal vector multiplied by the complex matrix, unitary matrix, and upper or lower triangular matrix, and detect possible combinations of transmit symbol candidates A receiving device is provided.
[Selection] Figure 4

Description

  The present invention relates to a receiving apparatus and a signal detection method. In particular, the present invention relates to a receiving apparatus and a signal detection method according to a MIMO (Multiple-Input and Multiple-Output) system in a mobile communication system.

  As one technique for increasing the communication speed between wireless devices, a multi-input / multi-output transmission system is known. This method is literally based on signal input / output using a plurality of antennas. The feature of this method is that a plurality of transmission data can be transmitted at the same time and at the same frequency using a plurality of different antennas. Therefore, as the number of channels that can be transmitted simultaneously increases, the amount of information that can be transmitted per unit time can be increased by the increased number of channels. Further, this method has an advantage that the occupied frequency band does not increase when the communication speed is improved.

  However, since a plurality of modulated signals having carrier components of the same frequency are transmitted at the same time, a means for separating the interfering modulated signals on the receiving side is necessary. Therefore, on the receiving side, a channel matrix representing the transmission characteristics of the wireless transmission path is estimated, and the transmission signal corresponding to each substream is separated from the received signal based on the channel matrix. The channel matrix is estimated using pilot symbols or the like. However, special measures are required to accurately remove the influence of noise added in the transmission path, interference generated between substreams, and the like and accurately reproduce the transmission signal for each substream.

  As a signal detection method in the MIMO system, for example, a method using MMSE (Minimum Mean Squared Error) detection is known. Further, as a method capable of improving the transmission characteristics as compared with the above MMSE detection method, for example, an MLD (Maximum Likelihood Detection) detection method is known. As an application example of the MLD detection method, for example, the following Non-Patent Document 1 discloses a QR decomposition MLD method capable of reducing a calculation load related to signal separation processing. Furthermore, as a technique for improving transmission characteristics regarding MLD, for example, the following Non-Patent Documents 2 and 3 disclose a technique called pre-whitening maximum likelihood estimation.

K. Higuchi, H .; Kawai, N .; Maeda and M.M. Sawahashi, “Adaptive selection of surviving symbol replica candidates based on maximum reliability in QRM-MLD for OFCDM MIMO Multiplexing”, Proc. IEEE GLOBECOM'04, pp. 2480-2486, Dallas, December 2004. G. E. Bottomley and K.M. Jamal, "Adaptive arrays and MLSE", IEEE VTC'95, pp. 50-54, May 1995. Y. Li, J. et al. Winters and N.W. Solenberger, “Signal detection for MIMO-OFDM wireless communications”, IEEE ICC'01, pp. 3077-3081, June 2001.

  When the technique described in Non-Patent Document 1 is used, it is possible to accurately separate MIMO subchannels with a relatively small amount of calculation. However, in the QR decomposition MLD system described in the same document 1, if there is another system using the same channel nearby, there is a problem that the transmission characteristics are greatly deteriorated due to the influence of the same channel interference received from the system. . On the other hand, if the techniques described in Non-Patent Documents 2 and 3 above are used, the influence of co-channel interference can be whitened, so that the influence of co-channel interference decreases as SNR (Signal to Noise Ratio) increases. can do.

  However, the pre-whitening maximum likelihood estimation described in Documents 2 and 3 needs to execute signal separation processing in consideration of all transmission symbol vectors. For this reason, there is a problem that the amount of calculation becomes remarkably large as the number of streams and the modulation multi-level number increase. In the QR decomposition MLD method, transmission symbols are sequentially determined for each substream, and error vector components used to determine the next transmission symbol are calculated using the transmission symbols. Therefore, the above prior whitening maximum likelihood estimation method that whitens the co-channel interference component using an error vector cannot be directly combined with the QR decomposition MLD method.

  Therefore, the present invention has been made in view of the above problems, and an object of the present invention is to accurately detect a transmission symbol vector with a small amount of computation while reducing the influence of co-channel interference. It is an object of the present invention to provide a new and improved receiving apparatus and signal detection method.

  In order to solve the above problems, according to an aspect of the present invention, an interference channel matrix representing a channel characteristic between a channel matrix estimation unit that estimates a channel matrix for the device itself and a co-channel interference source is estimated. An interference channel matrix estimation unit, a correlation matrix calculation unit for calculating a correlation matrix of an error vector caused by co-channel interference using the interference channel matrix, a complex matrix and a complex conjugate of the complex matrix A first matrix decomposing unit that decomposes a transposed product into a product; and a second matrix decomposing unit that decomposes a matrix obtained by integrating the channel matrix and the complex matrix into a product of a unitary matrix and an upper or lower triangular matrix. And a trellis search for a predetermined transmission symbol candidate using the received signal vector multiplied by the complex matrix, the unitary matrix, and the upper or lower triangular matrix, It seems and a maximum likelihood detector for detecting the combination of the transmission symbol candidate, the receiving apparatus is provided.

  In this way, by multiplying the reception signal vector and the channel matrix for the device itself by the complex matrix in advance, whitening related to co-channel interference can be performed in the previous stage of the maximum likelihood detection process. As a result, the QR (or QL) decomposition maximum likelihood detection method can be applied to a received signal vector subjected to whitening related to co-channel interference, which has been a problem in the interference whitening maximum likelihood estimation method. It is possible to reduce the amount of calculation to a realistic level. On the contrary, in the QR (or QL) decomposition maximum likelihood detection method, it is possible to solve the problem that the transmission characteristics are greatly deteriorated when a co-channel interference source is present.

The first matrix decomposition unit decomposes the inverse matrix of the correlation matrix into a product of a lower triangular matrix and an upper triangular matrix corresponding to a complex conjugate transpose of the lower triangular matrix using a Cholesky decomposition algorithm for a complex matrix. It may be configured to. Thus, by using the Cholesky decomposition algorithm, it is possible to reduce the amount of calculation required for calculating the complex matrix. If there is no significant increase in the computational load related to matrix decomposition processing, the first matrix decomposition unit uses an eigenvalue decomposition algorithm to convert the inverse matrix of the correlation matrix into an orthogonal matrix U and a complex conjugate transpose U of the orthogonal matrix U. ^It may be configured to decompose into a product of ^H and a diagonal matrix D, and to set the complex matrix to D ^1/2 U ^H.

  Further, the maximum likelihood detection unit multiplies the reception matrix by the complex matrix, and further multiplies the unitary matrix to generate a whitening reception signal vector, and the upper or lower triangle In order from the lowermost stage or the uppermost stage of the matrix, replica elements of each stage are generated using the elements of the respective stages and the predetermined transmission symbol candidates, and the replica symbols of the respective stages and the respective stages of the whitened reception signal vector And a trellis search unit that sequentially determines transmission symbol candidates such that the Euclidean distance between the component and the component corresponding to is reduced. As described above, the transmission symbol candidates are determined in order from the bottom or top of the upper or lower triangular matrix, so that it is not necessary to consider combinations for all transmission symbol candidates, and the amount of calculation can be greatly reduced. .

  In order to solve the above-described problem, according to another aspect of the present invention, an interference channel matrix estimation step for estimating an interference channel matrix representing transmission path characteristics with a co-channel interference source, and the interference channel A correlation matrix calculation step of calculating a correlation matrix of an error vector caused by co-channel interference using a matrix, and an inverse matrix of the correlation matrix is decomposed into a product of a complex matrix and a complex conjugate transpose of the complex matrix; A matrix decomposition step, a channel matrix estimation step for estimating a channel matrix for the device itself, and a matrix obtained by integrating the channel matrix and the complex matrix is decomposed into a product of a unitary matrix and an upper or lower triangular matrix Second matrix decomposition step, a received signal vector multiplied by the complex matrix, the unitary matrix, and the upper or lower triangular matrix. Trellis search is performed for the symbol candidates, the combination of the transmission symbol candidate plausible comprises a maximum likelihood detection step is detected, the signal detection method is provided.

  In this way, by multiplying the reception signal vector and the channel matrix for the device itself by the complex matrix in advance, whitening related to co-channel interference can be performed in the previous stage of the maximum likelihood detection process. As a result, the QR (or QL) decomposition maximum likelihood detection method can be applied to a received signal vector subjected to whitening related to co-channel interference, which has been a problem in the interference whitening maximum likelihood estimation method. It is possible to reduce the amount of calculation to a realistic level. Conversely, in the QR (or QL) decomposition maximum likelihood detection method, it is possible to solve the problem that the transmission characteristics are greatly deteriorated when a co-channel interference source is present.

  As described above, according to the present invention, it is possible to accurately detect a transmission symbol vector with a small amount of calculation while reducing the influence of co-channel interference.

  Exemplary embodiments of the present invention will be described below in detail with reference to the accompanying drawings. In addition, in this specification and drawing, about the component which has the substantially same function structure, duplication description is abbreviate | omitted by attaching | subjecting the same code | symbol.

[About the flow of explanation]
Here, the flow of explanation regarding the embodiment of the present invention described below will be briefly described. First, a conventional MIMO signal detection method using QR decomposition MLD will be described with reference to FIG. 1, and problems with the method will be described. Next, the configuration of the MIMO system when co-channel interference exists will be described with reference to FIG. Next, a conventional pre-whitening full likelihood (MLD) method will be described with reference to FIG. 3, and problems that the method has will be described. Next, the configuration of the receiving apparatus 100 according to the embodiment will be described with reference to FIGS. 4 and 5. Next, an application example of the technique according to the embodiment will be described with reference to FIG. Finally, effects obtained by applying the technique of the embodiment will be described with reference to FIG.

[Organization of issues]
First, prior to a detailed description of a technique according to an embodiment of the present invention, problems to be solved by the embodiment will be briefly summarized.

(MIMO signal detection method using QR decomposition MLD)
First, a MIMO signal detection method using QR decomposition MLD will be described. FIG. 1 is an explanatory diagram illustrating an example of a functional configuration of a reception device 30 that employs the QR decomposition MLD method. As illustrated in FIG. 1, the reception device 30 includes an N _R reception antennas, a channel matrix estimation unit 32, a rearrangement processing unit 34, a QR decomposition unit 36, a channel matrix upper triangulation unit 38, a trellis. And a search unit 40. In addition, although the case where N _R = 4 is illustrated in FIG. 1, it is not limited to this. Also, the receiving apparatus 30, it is assumed that independent transmission apparatus having a transmitting antenna of the N _T from (not shown) for each transmission antenna the N _T streams are transmitted.

First, the reception streams received by N _R receive antennas are input to the channel matrix estimating unit 32 and the channel matrix on the triangulation unit 38. The channel matrix estimation unit 32 estimates a channel matrix based on pilot signals included in the _NR received streams. For example, if a transmission symbol vector s, a noise vector n, the received signal vector x, using the channel matrix H _D estimated by the channel matrix estimating unit 32, is expressed as the following formula (1) . The channel matrix _{H D} is a matrix of _{N R} rows _{N T} columns.

Channel matrix H _D estimated by the channel matrix estimating unit 32 is input to the rearrangement unit 34. When the channel matrix H _D is input, the rearrangement unit 34 calculates the size of each column vector constituting the channel matrix H _D, and calculates the received signal power of each stream. Further, the rearrangement unit 34, replacing the order calculated reception signal power is large arranges the column vector of the channel matrix H _D. For example, the rearrangement processing unit 34 arranges column vectors from the right side in descending order of received signal power. In the following description, for the sake of simplicity, it is assumed that the arrangement order of the column vectors does not change before and after the rearrangement. Channel matrix H _D reordering process has been performed by the sorting processing section 34 is input to the QR decomposition unit 36. When the channel matrix H _D is input, QR decomposition unit 36, as shown in the following equation (2), breaks down the channel matrix H _D to the product of the unitary matrix Q and an upper triangular matrix R.

The unitary matrix Q obtained by the decomposition process by the QR decomposition unit 36 is input to the channel matrix upper triangulation unit 38. On the other hand, the upper triangular matrix R obtained by the decomposition process by the QR decomposition unit 36 is input to the trellis search unit 40. When unitary matrix Q is inputted, the channel matrix on the triangulation unit 38, as shown in the following formula (3) is multiplied by a complex conjugate transpose Q ^H of the unitary matrix Q to the reception signal vector x. When complex conjugate transpose Q ^H of the thus unitary matrix Q is multiplied by the received signal vector x, the signal vector z shown in the following equation (4) is obtained. However, the following equation (4) exemplifies the signal vector z in the case of N _T = N _R = 4. Thus, the multiplication processing according to the channel matrix on the triangulation unit 38, the channel matrix H _D is upper triangular of.

The signal vector z output from the channel matrix upper triangulation unit 38 is input to the trellis search unit 40. As described above, the upper triangular matrix R is input from the QR decomposition unit 36 to the trellis search unit 40. Therefore, the trellis search unit 40 uses the signal vector z and the upper triangular matrix R to estimate a transmission symbol vector s ″ as shown in the following equation (5) using a trellis search algorithm, where C _j Represents a set of constellations determined based on the number of modulation levels used in the j th stream, that is, s _j ′ is a transmission symbol candidate selected from the set of constellations corresponding to the j th stream Indicates.

In order to obtain the transmission symbol vector s ″ represented by the above equation (5), the trellis search unit 40 executes a trellis search in order from the lowest stage of the upper triangular matrix R. For example, the above equation (4) is obtained. For example, first, the trellis search unit 40 selects a transmission symbol candidate s ₄ ′, and calculates a product r ₄₄ * s ₄ ′ with the lowermost element r ₄₄ constituting the upper triangular matrix R. Next, the trellis search unit 40 calculates the Euclidean distance between the element z ₄ of the signal vector z obtained by the above equation (3) and the product r ₄₄ * s ₄ ′. , similarly _'calculates the Euclidean distance in the Euclidean distance transmission is small symbol candidates s _4' other transmitted symbol candidate s ₄ to extract. At this time, the transmission symbol candidate s ₄ Euclidean distance is minimized 'May be extracted, but a predetermined number of transmission symbol candidates s ₄ ' may be extracted in ascending order of the Euclidean distance.

Next, the trellis search unit 40 the extracted _'using the transmission symbol candidate s _4' transmitted symbol candidate s ₄ transmitted symbol candidate s for 3-stage element from the bottom of the upper triangular matrix R as in the case of ₃ 'is extracted. Similarly, the trellis search unit 40, transmitted symbol candidate _{s 2} ', _{s 1'} to extract. In this way, the trellis search unit 40 extracts the transmission symbol vectors s ″ that meet the above-described equation (5) by extracting the transmission symbol candidates s ₄ ′ to s ₁ ′ in order from the lowest stage of the upper triangular matrix R. The transmission symbol vector s ″ estimated in this way is output as an estimated symbol vector. In the above description, QR decomposition is assumed, but QL decomposition can also be used. In this case, since the unitary matrix Q and the lower triangular matrix L are obtained by QL decomposition, transmission symbol candidates s ₁ ′ to s ₄ ′ are extracted in order from the uppermost stage of the lower triangular matrix L.

  As described above, the trellis search is performed in order from the lowest stage of the upper triangular matrix, so that the number of transmission symbol candidates is sequentially narrowed down at each stage. Therefore, the Euclidean distance calculation target can be greatly reduced as compared with the case where all the transmission symbol candidates are combined for the stream. As a result, the use of the MIMO signal detection method based on QR decomposition MLD makes it possible to greatly reduce the computation load. However, the conventional QR decomposition MLD system described here does not consider the influence of co-channel interference. Therefore, if there is another system using the same channel nearby, the transmission symbol vector is estimated based on the received signal vector x including the influence of the same channel interference, and transmission characteristics are greatly degraded. As a method for reducing the influence of such co-channel interference, there is a pre-whitening maximum likelihood detection method described later. Next, this method will be described.

(MIMO signal detection method by Pre-whitening Full-MLD)
Next, a conventional pre-whitening maximum likelihood estimation method will be described. As described above, the pre-whitening maximum likelihood estimation method is very superior in that it reduces the effect of co-channel interference and provides good transmission characteristics even when other systems using the same channel exist nearby. Yes. First, with reference to FIG. 2, the configuration of a MIMO system when there is another system that uses the same channel will be briefly described.

(System configuration)
As shown in FIG. 2, the MIMO system includes a transmission device 10 and a reception device 12. However, the MIMO system is affected by co-channel interference from other MIMO systems including the transmission apparatus 20. In particular, it is assumed that the transmission device 20 uses the same channel as that used for stream transmission between the transmission device 10 and the reception device 12. The transmission apparatus 10 includes N _T transmission antennas, and transmits N _T streams to the reception apparatus 12 independently from each transmission antenna. On the other hand, the transmission device 20 includes a N _I antennas, and transmits the N _I book streams for different receivers independently from each transmit antenna and the receiver 12. However, since the transmission apparatuses 10 and 20 use the same channel, the stream transmitted by the transmission apparatus 20 reaches the reception apparatus 12.

Now, s is a transmission symbol vector transmitted by the transmission apparatus 10, c is a transmission symbol vector transmitted by the transmission apparatus 20, and H _D is a channel matrix representing a channel characteristic between the transmission apparatus 10 and the reception apparatus 12. When the interference channel matrix representing the transmission path characteristics between 20 and the receiving device 12 is expressed as H _U , the received signal vector x received by the receiving device 12 is expressed as the following equation (6). However, receiver 12 includes a _{N R} receive antennas present. Therefore, the interference channel matrix H _U is expressed by a matrix of N _R rows and N _I columns. N represents a noise vector.

As shown in equation (6) above, the received signal vector x to be received by the receiving apparatus 12, the term including interference channel matrix H _U is present as the influence of co-channel interference. For this reason, if the above QR decomposition MLD is performed on the received signal vector x, transmission characteristics are deteriorated. Therefore, in the pre-whitening maximum likelihood estimation method, the term of the interference channel matrix H _U whitened, so that the influence of interference channel matrix H _U when SNR is improved reduced. Hereinafter, the functional configuration of the receiving device 12 (receiving device 50) to which the conventional pre-whitening maximum likelihood estimation method is applied will be described with reference to FIG.

(Functional configuration of receiving device 50)
As shown in FIG. 3, the receiving device 50 includes a channel matrix estimation unit 52, a replica signal generation unit 54, an error vector calculation unit 56, an interference channel matrix estimation unit 58, an interference correlation inverse matrix calculation unit 60, The metric calculation unit 62 and the minimum metric search unit 64 are configured. Note that it is assumed that the transmission symbol vector s is transmitted from the transmission device 10 and the transmission symbol vector c is transmitted from the transmission device 20 to the reception device 50. Further, it is assumed that the reception signal vector is received by N _R reception antennas.

First, when the received signal vector at the receive antennas N _R present is received, the received signal vector is input to the channel matrix estimating unit 52, the interference channel matrix estimation unit 58 and the error vector calculator 56,. The channel matrix estimation unit 52 estimates a channel matrix for the device itself based on a pilot signal included in the received signal vector. The channel matrix estimated by the channel matrix estimation unit 52 is input to the replica signal generation unit 54. The replica signal generation unit 54 is input with transmission symbol vector candidates based on the modulation multi-value number for each stream transmitted from the transmission apparatus 10. Therefore, the replica signal generation unit 54 multiplies the channel matrix estimated by the channel matrix estimation unit 52 by the transmission symbol vector candidate to generate a replica signal vector. The replica signal vector generated by the replica signal generation unit 54 is input to the error vector calculation unit 56.

As described above, the received signal vector x is input to the error vector calculation unit 56. Therefore, the error vector calculation unit 56 calculates the difference between the received signal vector x and the replica signal vector H _D s ′ as shown in the following equation (7), and an error vector e for each transmission symbol vector candidate s ′. Is calculated. However, the transmission symbol vector s ′ = [s ₁ ′, s ₂ ′,..., S _NT ′] ^T. The received signal vector x is expressed by the above equation (6). In addition, in the following equation (7), when the transmission symbol vector candidate s ′ and the transmission symbol vector s transmitted by the transmission apparatus 10 are equal, the correlation matrix R _ee of the error vector e is expressed by the following equation (8). It is expressed as follows. Here, I _NR is a unit matrix of N _R rows and N _R columns. Further, σ ² indicates noise variance.

The error vector e calculated by the error vector calculation unit 56 as in the above equation (7) is input to the metric calculation unit 62. On the other hand, the interference channel matrix estimating unit 58, the interference channel matrix H _U indicating channel characteristics between the transmission device 20 based on the received signal vector is estimated. Interference channel matrix H _U that have been estimated in the interference channel matrix estimation unit 52 is input to the interference correlation inverse matrix calculating unit 60. At this time, the noise variance σ ² is also calculated and input to the interference correlation inverse matrix calculation unit 60. The interference correlation inverse matrix calculation unit 60 calculates the error vector correlation matrix R _ee using the interference channel matrix H _U and the noise variance σ ² estimated by the interference channel matrix estimation unit 58 according to the above equation (7). . Further, the interference correlation inverse matrix calculation unit 60 converts the calculated correlation matrix R _ee into an inverse matrix and inputs it to the metric calculation unit 62.

The metric calculator 62 estimates a transmission symbol vector using the error vector e input from the error vector calculator 56 and the inverse matrix R _ee ⁻¹ of the correlation matrix input from the interference correlation inverse matrix calculator 60. Calculate the metric. In the pre-whitening maximum likelihood estimation method, the norm of the error vector e is not simply used as a metric, but the inverse matrix R _ee ⁻¹ of the correlation matrix is sandwiched between the error vector e and its complex conjugate transpose e ^{H. H} R _ee ⁻¹ e is used. Also, the metric e ^H R _ee ⁻¹ e calculated by the metric calculation unit 62 is developed as shown in the following formula (9).

First, pay attention to the first term on the right side of the above equation (9). The first term on the right side includes the interference channel matrix H _U and its complex conjugate transpose H _U ^H. However, by the inverse matrix _R ^{ee -1} is sandwiched correlation matrix between the interference channel matrix _{H U} and its complex conjugate transpose _H ^{U H,} see the inverse matrix of the correlation matrix _R ^{ee -1} (equation (8) ) component of the interference channel matrix H _U from which the first term expanded is erased. However, the right side in the second and third terms, contains components of the interference channel matrix H _U. However, a noise vector n is applied to the second and third terms on the right side, and can be ignored as the noise decreases. That is, when the SNR increases, the influence of the interference channel matrix H _U is to become negligible. Therefore, if the SNR is large, also contain the term of the interference channel matrix H _U to the received signal vector x, to become the extent that components of the interfering channel matrix H _U appearing on the metric is negligible, giving the accuracy of the signal detection The effect is small. In this paper, this action is called whitening.

  The metric calculated by the metric calculation unit 62 is input to the minimum metric search unit 64. The minimum metric search unit 64 determines a transmission symbol vector having a minimum metric as shown in the following equation (10). Where S represents the set of all possible transmission symbol vectors. That is, the combination is considered for all transmission symbol candidates determined according to the modulation multi-level number of each stream, and the smallest one of the metrics calculated for all transmission symbol vectors is determined. Then, a transmission symbol vector corresponding to the determined metric is determined as an estimated symbol vector.

  As described above, when the prior whitening maximum likelihood estimation method is used, when the SNR is large, the influence of the co-channel interference included in the metric is reduced, and the influence of the co-channel interference on the signal detection can be reduced. However, it is necessary to consider all combinations of transmission symbol vectors that can be transmitted to the device itself, and the amount of calculation becomes enormous as the number of streams and the number of modulation multivalues increase. Further, in the above pre-whitening maximum likelihood estimation method, an error vector is required when calculating a metric. Therefore, it cannot be directly combined with a method in which the elements of the error vector are obtained sequentially in the trellis search process as in the QR decomposition MLD method. Therefore, a device capable of reducing the influence of the co-channel interference while suppressing the calculation amount is demanded.

<Embodiment>
An embodiment of the present invention will be described. The present embodiment proposes a technique capable of reducing the influence of co-channel interference while suppressing the amount of calculation. The basic idea of the present embodiment is that a process corresponding to whitening of the co-channel interference component is executed before the maximum likelihood detection is executed, and QR decomposition MLD is executed using the processing result. It is. The metric expressed by the above equation (10) used for the above prior whitening maximum likelihood estimation can be decomposed using a complex matrix V as in the following equation (11). Further, the error vector e converted by the complex matrix V (e ′) is expressed by the following equation (12).

Referring to the above equation (12), in order to whiten regard co-channel interference the error vector e is the received signal vector x and a complex matrix V appearing in equation (11) the channel matrix H _D of the of the apparatus for It turns out that it should just whiten. Therefore, by multiplying the complex matrix V to the received signal vector x and the channel matrix H _D, the co-channel interference components in terms of the whitening, the present embodiment because using these multiplication results to perform a QR decomposition MLD Is the basic idea. Hereinafter, a functional configuration of the receiving device 100 in which the idea is applied to the receiving device 12 will be described.

[Functional configuration of receiving apparatus 100]
As shown in FIG. 4, the receiving apparatus 100 includes a channel matrix estimation unit 102, an interference channel matrix estimation unit 104, a correlation matrix calculation unit 106, an inverse matrix calculation unit 108, a matrix decomposition unit 110, and interference whitening. Sections 112, 118, rearrangement processing section 114, QR decomposition section 116, channel matrix upper triangulation section 120, and trellis search section 122. It is assumed that transmission symbol vector s is transmitted from transmission device 10 and transmission symbol vector c is transmitted from transmission device 20 to reception device 100. Further, it is assumed that the received signal vector is received by N _R receive antennas.

First, when the received signal vector at the receive antennas N _R present is received, the received signal vector is input to the channel matrix estimator 102, an interference channel matrix estimator 104, and the interference whitening unit 118. Channel matrix estimation section 102 estimates a channel matrix for the device itself based on a pilot signal included in the received signal vector. The channel matrix estimated by the channel matrix estimation unit 102 is input to the interference whitening unit 112.

On the other hand, the interference channel matrix estimating unit 104, an interference channel matrix H _U indicating channel characteristics between the transmission device 20 based on the received signal vector is estimated. Interference channel matrix H _U that have been estimated in the interference channel matrix estimation unit 52 is input to the correlation matrix calculation section 106. At this time, the noise variance σ ² is also calculated and input to the correlation matrix calculation unit 106. Correlation matrix calculation section 106 calculates correlation matrix R _ee of the error vector using interference channel matrix H _U estimated by interference channel matrix estimation section 104 and noise variance σ ² according to equation (7) above. The correlation matrix R _ee calculated by the correlation matrix calculation unit 106 is input to the inverse matrix calculation unit 108. The inverse matrix calculation unit 108 converts the calculated correlation matrix R _ee into an inverse matrix R _ee ⁻¹ and inputs it to the matrix decomposition unit 110.

The matrix decomposition unit 110 calculates the complex matrix V appearing in the above equation (11). As apparent from the above equation (11), a complex matrix V is obtained by decomposing the inverse matrix R _ee ^-1 of the correlation matrix to the product of a complex matrix V and its complex conjugate transpose V ^H. As a decomposition method by the matrix decomposition unit 110, for example, a method by eigenvalue decomposition can be considered. According to this method, the inverse matrix R _ee ⁻¹ of the correlation matrix is decomposed as shown in the following equation (13) using the orthogonal matrix U and the diagonal matrix D. Then, the inverse matrix R _ee ⁻¹ of the correlation matrix can be decomposed into a desired form by expressing the complex matrix V using the orthogonal matrix U and the diagonal matrix D as shown in the following equation (14). it can. However, since the calculation amount of eigenvalue decomposition is large, a method of decomposing the inverse matrix R _ee ⁻¹ of the correlation matrix into a desired form with a smaller calculation amount is desired.

As a method of decomposing the inverse matrix R _ee ⁻¹ of the correlation matrix into a desired form with a smaller amount of computation than eigenvalue decomposition, for example, a method using a Cholesky decomposition algorithm for a complex matrix as shown in FIG. By using this Cholesky decomposition algorithm, the inverse matrix R _ee ⁻¹ of the correlation matrix can be decomposed into the product of the lower triangular matrix A and the upper triangular matrix A ^H. That is, the lower triangular matrix A with R _ee ⁻¹ = AA ^H is obtained. Thus, complex matrix V is determined as V = ^{A H.} The complex matrix V obtained by the matrix decomposition unit 110 in this way is input to the interference whitening units 112 and 118.

In the interference whitening unit 112, whitening of the co-channel interference component with respect to its own device's channel matrix H _D is performed. Specifically, with respect to the channel matrix H _D estimated by the channel matrix estimating unit 102, a complex matrix V obtained by the matrix decomposition unit 110 is multiplied. Channel matrix VH _D to complex matrix V is multiplied by the interference whitening unit 112 is input to the rearrangement unit 114. Rearrangement unit 114 calculates the size of each column vector constituting the channel matrix VH _D, to calculate the received signal power of each stream. Further, the rearrangement unit 114, changing the order calculated reception signal power is large arranges the column vector of the channel matrix VH _D. For example, the rearrangement processing unit 114 arranges column vectors from the right side in descending order of received signal power.

In the following description, for the sake of simplicity, it is assumed that the arrangement order of the column vectors does not change before and after the rearrangement. Channel matrix VH _D reordering process has been performed by the rearrangement unit 114 is input to the QR decomposition unit 116. When the channel matrix VH _D is input, QR decomposition unit 116, as shown in the following equation (15), breaks down the channel matrix VH _D to the product of the unitary matrix Q and an upper triangular matrix R. The unitary matrix Q calculated by the QR decomposition unit 116 is input to the channel matrix upper triangulation unit 120. On the other hand, the upper triangular matrix R calculated by the QR decomposition unit 116 is input to the trellis search unit 122.

Now, the interference whitening unit 118 multiplies the reception signal vector x by the complex matrix V calculated by the matrix decomposition unit 110, and executes whitening related to co-channel interference. The received signal vector Vx multiplied by the complex matrix V in the interference whitening unit 118 is input to the channel matrix upper triangulation unit 120. In channel matrix upper triangulation section 120, reception signal vector Vx whitened by interference whitening section 118 is multiplied by complex conjugate transpose Q ^H of unitary matrix Q input from QR decomposition section 116. The received signal vector z (= Q ^H Vx) converted by this multiplication processing is developed as in the following equation (16). That is, the channel matrix VH _D is upper triangular of.

The received signal vector z calculated by the channel matrix upper triangulation unit 120 is input to the trellis search unit 122. As described above, the upper triangular matrix R is input from the QR decomposition unit 116 to the trellis search unit 122. Therefore, the trellis search unit 122 uses the received signal vector z and the upper triangular matrix R to estimate a transmission symbol vector s ″ as shown in the following equation (17) using a trellis search algorithm. _j represents a set of constellations determined based on the number of modulation levels used in the j th stream, that is, s _j ′ is a transmission symbol selected from the set of constellations corresponding to the j th stream The trellis search algorithm is substantially the same as that described with reference to the above equation (5), and a detailed description thereof will be omitted.

As described above, by using the QR decomposition MLD, it is possible to greatly reduce the Euclidean distance calculation targets as compared to a case where all transmission symbol candidates are combined for the stream. On the other hand, since the whitening process is performed for the same channel interference on the received signal vector x and its own device for the channel matrix H _D, influence of co-channel interference is suppressed. As a result, even in a system environment where a co-channel interference source exists, high quality transmission characteristics can be obtained with a small amount of computation.

  Here, it is confirmed that the metric used in the present embodiment is equivalent to the metric used in the above pre-whitening maximum likelihood estimation method. An error vector e ′ used for trellis search in the present embodiment is expressed by the following equation (18). Therefore, the magnitude of the error vector e ′ is developed as in the following equation (19). In the present embodiment, the magnitude of the error vector e ′ expressed by the following equation (19) is used as a metric.

In the above formula (19), referring to the first term on the right side, interference channel matrices H _U and complex matrix V is included as an element. However, from the definition of a complex matrix V, elements of the interference channel matrix H _U and a complex matrix V contained in the first term on the right side is canceled, only the elements remain regarding the transmission symbol vector c of the transmitting apparatus 20. On the other hand, the second term on the right side to the fourth term on the right side include elements of the noise vector n, and can be ignored as the SNR increases. That is, the metric is equivalent to the pre-whitening maximum likelihood estimation method shown in the above equation (10). Therefore, when parameters (such as the number of surviving paths) in the trellis search algorithm are appropriately set, a MIMO signal having both a low computational complexity that is a feature of QR decomposition MLD and an interference cancellation function that is a feature of interference whitening maximum likelihood detection Detection is realized.

[Application example: Application to OFDM]
As an application example of the present embodiment, a case where the technique according to the present embodiment is extended to OFDM often used in a high-speed mobile communication system will be described with reference to FIG. FIG. 6 is an explanatory diagram illustrating a functional configuration example of the receiving device 200 according to this application example. Note that detailed description of components common to the receiving apparatus 100 is omitted.

As illustrated in FIG. 6, the reception device 200 includes a GI removal + FFT processing unit 202, an error correction decoding unit 204, and the like in addition to the components included in the reception device 100 described above. Note that the QR decomposition maximum likelihood detection unit 212 included in the reception device 200 corresponds to the channel matrix upper triangulation unit 120 and the trellis search unit 122 included in the reception device 100. Also, the channel matrix estimation unit 102, the interference channel matrix estimation unit 104, the correlation matrix calculation unit 106, the inverse matrix calculation unit 108, the matrix decomposition unit 110, the interference whitening unit 112, the rearrangement processing unit 114, the QR decomposition unit 116, the interference block including the whitening unit 118, QR decomposition maximum likelihood detection unit 212, LLR computation unit 214 is provided for the number of subcarriers _{N s} min.

First, when an OFDM signal is received by the _NR receiving antennas, the GI removal + FFT processing unit 202 removes the guard interval (GI) and performs FFT processing to convert it into a subcarrier signal. Note that FFT is an abbreviation of Fast Fourier Transform. Then, for each subcarrier to estimate the channel matrix H _U of interference stream received from the channel matrix H _D and other systems of the apparatus for. At this time, it is assumed that pilot signals orthogonal to each other are used between the system for the own apparatus and another system in which co-channel interference may occur in order to prevent interference in the channel matrix estimation process. Furthermore, the correlation matrix for the channel matrix H _U of the estimated interference stream is calculated, the noise variance value of the receiving apparatus 200 to the diagonal terms are added.

Then, the inverse matrix of the matrix obtained by this addition processing is calculated, and further decomposed into an upper triangular matrix and its complex conjugate transpose matrix (lower triangular matrix). Upper triangular matrix obtained in this decomposition process is multiplied to the channel matrix H _U of the received signal vector x and the own device for stream. In addition, the QR decomposition maximum likelihood detection unit 212 performs QR decomposition maximum likelihood detection on the reception signal vector and the channel matrix converted by the multiplication process, and the transmission symbol vector is estimated. Further, the LLR calculation unit 214 calculates a log-likelihood ratio (LLR) for each bit based on the estimated transmission symbol vector and inputs it to the error correction decoding unit 204. The error correction decoding unit 204 performs error correction based on the input LLR and outputs a reproduction stream.

[effect]
As described above, in the technique according to the present embodiment, the operation necessary for removing the co-channel interference is equivalently performed before the QR decomposition maximum likelihood detection. Therefore, an interference cancellation function can be added to the QR decomposition maximum likelihood detection. As a result, it is possible to improve transmission characteristics in an environment where co-channel interference sources exist, and to achieve a significant reduction in the amount of computation in the pre-whitening maximum likelihood detection method.

Hereinafter, a specific effect of improving transmission characteristics will be described with reference to FIG. FIG. 7 is an explanatory diagram illustrating a comparative example for explaining an effect obtained when the technique according to the present embodiment is applied. Comparative example Figure 7 shows the average bit error rate when the interference stream number N _I was varied from 0 to 3 for four rows and four columns MIMO system (local system for stream number = 4) is there. The modulation method is assumed to be QPSK. Further, the error rate characteristics when no error correction code is used are shown.

In the case of QRM-MLD (one-dot chain line), in the condition of N _I = 0 (condition in which no interference stream exists), the average BER characteristic improves with an increase in average E _b / N ₀ . However, if even one interference stream exists, the average BER characteristic deteriorates. On the other hand, in the case of interference whitening maximum likelihood detection (PW-FULL-MLD) (dashed line), the average BER characteristic deteriorates as the number of interference streams increases. However, it can be seen that it is more resistant to interference than QRM-MLD. However, in the case of PW-FULL-MLD (chain line), there is a problem that a very large amount of calculation is required, which is not realistic. In the case of the present embodiment (PW-QRM-MLD) (solid line), an average BER characteristic equivalent to PW-FULL-MLD is shown. However, the calculation amount is significantly reduced as compared with PW-FULL-MLD, and the calculation amount is suppressed to a practical range.

  As mentioned above, although preferred embodiment of this invention was described referring an accompanying drawing, it cannot be overemphasized that this invention is not limited to the example which concerns. It will be apparent to those skilled in the art that various changes and modifications can be made within the scope of the claims, and these are naturally within the technical scope of the present invention. Understood.

It is explanatory drawing which shows the function structural example of the receiver which concerns on QR decomposition | disassembly MLD system. It is explanatory drawing which shows the structural example of a MIMO system in case co-channel interference exists. It is explanatory drawing which shows the function structural example of the receiver which employ | adopted the prior whitening maximum likelihood estimation method. It is explanatory drawing which shows the function structural example of the receiver which concerns on one Embodiment of this invention. It is explanatory drawing which shows an example of the matrix decomposition | disassembly algorithm which concerns on the same embodiment. It is explanatory drawing which shows one application example of the receiver which concerns on the same embodiment. It is explanatory drawing which shows the effect acquired when the technique which concerns on the embodiment is applied.

Explanation of symbols

10, 20 Transmitting device 12, 30, 50, 100, 200 Receiving device 32, 52, 102 Channel matrix estimation unit 34, 114 Rearrangement processing unit 36, 116 QR decomposition unit 38, 120 Channel matrix upper triangulation unit 40, 122 Trellis search unit 54 Replica signal generation unit 56 Error vector calculation unit 58, 104 Interference channel matrix estimation unit 60 Interference correlation inverse matrix calculation unit 62 Metric calculation unit 64 Minimum metric search unit 106 Correlation matrix calculation unit 108 Inverse matrix calculation unit 110 Matrix decomposition Unit 112, 118 interference whitening unit 202 GI removal + FFT processing unit 204 error correction decoding unit

Claims (5)

A channel matrix estimator for estimating a channel matrix for the device;
An interference channel matrix estimator for estimating an interference channel matrix representing a channel characteristic between the co-channel interference sources;
A correlation matrix calculation unit for calculating a correlation matrix of an error vector caused by co-channel interference using the interference channel matrix;
A first matrix decomposition unit that decomposes an inverse matrix of the correlation matrix into a product of a complex matrix and a complex conjugate transpose of the complex matrix;
A second matrix decomposition unit for decomposing a matrix obtained by integrating the channel matrix and the complex matrix into a product of a unitary matrix and an upper or lower triangular matrix;
Maximum likelihood for detecting a likely combination of transmission symbol candidates by performing a trellis search for a predetermined transmission symbol candidate using the received signal vector multiplied by the complex matrix, the unitary matrix, and the upper or lower triangular matrix A detection unit;
A receiving device.   The first matrix decomposition unit decomposes the inverse matrix of the correlation matrix into a product of a lower triangular matrix and an upper triangular matrix corresponding to a complex conjugate transpose of the lower triangular matrix using a Cholesky decomposition algorithm for a complex matrix. The receiving device according to claim 1. The first matrix decomposition unit decomposes the inverse matrix of the correlation matrix into a product of an orthogonal matrix U, a complex conjugate transpose U ^H of the orthogonal matrix U, and a diagonal matrix D using an eigenvalue decomposition algorithm. set to D ^1/2 U ^H, the receiving apparatus according to claim 1. The maximum likelihood detection unit includes:
A whitened received signal vector generating unit that multiplies the complex matrix by a received signal vector and further multiplies the unitary matrix to generate a whitened received signal vector;
In order from the bottom or top of the upper or lower triangular matrix, replica elements of each stage are generated using the elements of the respective stages and the predetermined transmission symbol candidates, and the replica symbols of the respective stages and the whitened reception are generated. A trellis search unit that sequentially determines transmission symbol candidates such that the Euclidean distance between components corresponding to each stage of the signal vector is small;
The receiving device according to claim 1, comprising: An interference channel matrix estimation step in which an interference channel matrix representing channel characteristics between co-channel interference sources is estimated;
A correlation matrix calculating step of calculating a correlation matrix of an error vector caused by co-channel interference using the interference channel matrix;
A first matrix decomposition step in which an inverse matrix of the correlation matrix is decomposed into a product of a complex matrix and a complex conjugate transpose of the complex matrix;
A channel matrix estimation step for estimating a channel matrix for the device;
A second matrix decomposition step in which a matrix obtained by integrating the channel matrix and the complex matrix is decomposed into a product of a unitary matrix and an upper or lower triangular matrix;
A trellis search is performed for a predetermined transmission symbol candidate using the received signal vector multiplied by the complex matrix, the unitary matrix, and the upper or lower triangular matrix, and the most likely combination of transmission symbol candidates is detected. A likelihood detection step;
A signal detection method comprising:

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