HK1168494B - Method and system for spatial channel state information feedback based on a kronecker product - Google Patents

Description

Method and system for feeding back spatial channel state information based on kronecker product

Priority

Priority is claimed for U.S. provisional patent application No. 61/282,275, filed on 12/1/2010, the disclosure of which is incorporated herein by reference in its entirety.

Technical Field

The field of the invention is to provide spatial Channel State Information (CSI) for mobile communications enhanced by multiple-input multiple-output techniques.

Background

Multiple Input Multiple Output (MIMO) is a class of techniques that uses multiple antennas at the transmitter or at the receiver, or both, to exploit (exploit) the spatial dimension in order to improve data throughput and transmission reliability. Data throughput can be increased by spatial multiplexing or beamforming.

Spatial multiplexing allows multiple data streams to be transmitted simultaneously to the same user over parallel channels in a MIMO setup, especially for diversity antennas with low spatial correlation between antennas (at the transmitter and receiver). Beamforming helps to enhance the signal-to-interference-and-noise ratio (SINR) of the channel, thus improving the channel rate. Such SINR improvement is achieved by appropriate weighting on multiple transmit antennas. The weighting calculation can be based on long-term measurements (e.g., open loop) or via feedback (e.g., closed loop). In the context of MIMO studies, closed loop transmit weights are commonly referred to as precoding.

FIG. 1 illustrates precoding MIMO for a Single User (SU), where M data streams u₁，...，u_MAre spatially multiplexed by exploiting the mxn spatial channel matrix H. Since the number of transmit antennas N is greater than the number of receive antennas M, precodingApplied, the precoding is represented as a matrix F.

Precoding MIMO can also operate in a multi-user MIMO (MU-MIMO) mode to further improve the overall rate for multiple users sharing the same time and frequency resources. Fig. 2 illustrates MU-MIMO for two users, where beamforming (e.g., precoding) is used to spatially separate the two users (and improve SINR), while for each user, the two data streams (light and dark shading) are spatially multiplexed.

As described in 3GPP TR 36.814, v1.1.1 "future advancement for E-UERA, Physical Layer accessories," month 6, 2009, MU-MIMO, especially downlink MU-MIMO, is a hot topic in long term evolution (LTE-Advanced) studies of third generation partnership project (3GPP) evolution. MU-MIMO may also enhance data throughput of LTE systems. An engineering project for DL MU-MIMO is created in the 3GPP physical layer working group (RAN 1).

A key specification-affecting aspect of precoding MIMO is the spatial CSI feedback required for closed-loop precoding. The spatial channel matrix H as seen in fig. 1 contains the complete spatial CSI. Alternatively, the NxN covariance matrix R is expressed as

R＝H^HH (1)，

The nxn covariance matrix R can provide sufficient spatial information for transmitter precoding, where the superscript "H" denotes the complex conjugate. In general, floating point versions of H or R are too costly to feed back because they typically contain a significant number of complex coefficients in each band. Quantization is therefore required to make the feedback more efficient.

A codebook known by both the receiver and the transmitter is often used for CSI quantization in order to feed back only the codeword index. The codeword can be chosen to maximize channel capacity or minimize the distance between floating-point CSI and quantized CSI.

Codebook design itself is the subject of much research since a good codebook must effectively span the entire relevant spatial distance. In this sense, generic codebooks are rarely effective and, in fact, codebooks are tailored to suit different antenna configurations and deployment schemes. In general, the more complex the antenna configuration, the more difficult the codebook design.

Table 1 is a table derived from the general information in 3GPP TS 36.211 "Evolved Universal Radio Access (E-UTRA); extract from 3GPP RAN1 LTE standard specification described in Physical Channels and Modulation ". The codebook is used for a very simple MIMO configuration with two transmit and two receive antennas (M-2, N-2) as in fig. 1. Thus, the total number of multiplexed streams (also referred to as layers) is 2.

TABLE 1 codebook for 2 × 2MIMO in LTE Specification

Multi-user MIMO (MU-MIMO) requires more accurate spatial CSI feedback in order to perform efficient spatial separation and multiplexing operations compared to single-user MIMO (SU-MIMO). As a result, CSI feedback and codebook design in MU-MIMO is more challenging.

Mathematically, usingThe kronecker product represented is an operation on two arbitrarily sized matrices that results in a block matrix. For example,

the kronecker product has been used for codebook design, for example for cross-polarized antennas described in 3GPP, R1-094752, "DL codebook design for 8 tx MIMO in LTE-a," ZTE, RAN #59, jizhou, korea, november, 2009. More specifically, the codebook is constructed by the kronecker product of the LTEREL-8 codebook and a normalized 2 × 2 matrix. Note that the concept described in 3GPP, R1-094752, "DL codebook design for 8T × MIMO in LTE-a" ZTE, RAN #59, china, korea, november 2009 is to have a single codebook and the feedback is still a single index of that codebook.

The kronecker product can be used to decompose a larger transmit covariance matrix R into two smaller matrices R, as described in 3GPP, R1-094844, "Low-overhead feedback of spatial covariance matrix" Motorola, RAN1#59, jen, korea, november, 2009_ULAAnd R_PolIn order to be able to reduce the feedback overhead:

by applying the mixed product attribute of kronecker products, the above decomposition also works in the eigen-domain (eigen-domain),

wherein the matrix "V_xx"separately containing transmit covariance matrices" R_xx"of the eigenvectors. Diagonal matrix "D_xx"including transmit covariance matrix" R_xx"intrinsic value of.

The key issue to point out is that the design principle of CSI feedback described in 3GPP, R1-094844, "Low-overhead feedback of spatial covariance matrix" Motorola, RAN1#59, jejuna, korea, november, 2009, is to directly quantize the transmit covariance matrix in an element-by-element manner. Such a method is distinct from the codebook-based quantization mentioned earlier. Thus, even after the kronecker decomposition, the content of the feedback is still the covariance matrix (or matrices) rather than the codebook index (or indices).

Summary of The Invention

The present invention is directed to wireless communication methods and systems that provide accurate spatial CSI feedback for MIMO operations while keeping the feedback overhead as low as possible.

In these methods and systems, spatial channel state information is measured at the receiving device, resulting in CSI. In some embodiments, the CSI is on a channel matrix or covariance matrix and may also be quantized through the use of a codebook.

The CSI is decomposed, yielding component CSI. Each component CSI may represent a characteristic of a beamforming antenna or a cross-polarized antenna. The beamforming antenna may also be denoted as a Uniform Linear Array (ULA).

In some embodiments, the decomposition is performed by using a kronecker product. Further, the decomposition of the covariance matrix may include applying a mixed product attribute of a kronecker product.

The index is also generated using the codebook quantized component CSI. The codebooks used may be the same or different, and the indices may point to vectors or matrices within the codebooks.

The index is fed back to the transmitting means and an outer product (outer product) can be calculated.

Further aspects and advantages of the improvements will appear from the description of the preferred embodiments.

Brief description of the drawings

Embodiments of the invention are illustrated by way of the accompanying drawings in which:

FIG. 1 illustrates a block diagram of a precoded SU-MIMO with Minimum Mean Square Error (MMSE) receiver;

FIG. 2 illustrates MU-MIMO with two sets of closely spaced cross-polarized antennas for two users;

FIG. 3 illustrates feedback setup and block diagrams associated with the present invention; and

fig. 4 illustrates an example of eight transmit antennas consisting of beam forming antennas and cross-polarized antennas.

Detailed description of the preferred embodiments

The kronecker product described in 3GPP, R1-094844, "Low-overhead feedback of spatial covariance matrix" Motorola, RAN1#59, china, korea, november, 2009, is applied to codebook-based CSI quantization. The method is particularly applicable to antenna arrangements comprising a plurality of closely spaced cross-polarized antennas. In such an arrangement, the spatial correlation statistics of the cross-polarized antennas and the beamforming antennas are very different.

A suitable kronecker decomposition for a specific antenna configuration must first be determined in order to be able to distinguish the different spatial characteristics of the different components of the antenna. The size of the component covariance matrix may vary. Then, for each component covariance matrix, the index of the codeword is selected from the appropriate codebook appropriate for the component antenna configuration.

The above process is repeated multiple times to find a set of codeword indices for each component covariance matrix, which results in the best match between the quantized covariance matrix and the floating point covariance matrix. The set of codeword indexes is fed back to the transmitter.

At the transmitter, each quantized version of the component covariance matrix is reconstructed by finding the codeword index in the corresponding codebook. A composite covariance matrix is synthesized by performing a kronecker product on all quantized component covariance matrices.

In more detail, the feedback setup and block diagram related to the present invention is shown in fig. 3. Fig. 3 serves as a dual illustration service: one for the description of the entity blocks and another for the block diagram of the process.

There are two main entities in the setup: an evolved node b (enb) represents a base station, and a User Equipment (UE) represents a mobile device. In this downlink example (data transmission from eNB to UE), the feedback is from UE to eNB. Both eNB and UE have multiple antennas for precoding MIMO. Of particular note is a configuration where the number of receive antennas at the UE is less than the number of transmit antennas at the eNB.

Based on the air interface specification, the eNB and UE are both aware of the codebook, and the codebook may be a subset of the codebooks specified in the standard. The actual codebook for each component CSI depends on the antenna configuration and deployment environment and is typically determined by the network. This information can be signaled to the UE via semi-static Radio Resource Control (RRC) signaling.

At the UE, the spatial CSI is first measured. The measurement may be directly for the channel matrix H, or for the covariance matrix R, or other matrices. In some embodiments of the invention, R is of primary interest, as shown in equation (1), which may be directly estimated or post-processed. Here, although chip implementations often use fixed-point algorithms, for simplicity of presentation, it is assumed that the measured spatial CSI (e.g., R) is floating-point accurate. In other words, the internal quantization in the chip is expected to be much finer than the quantization of the feedback.

Once the covariance matrix R is estimated, matrix decomposition may be performed. To further illustrate this process, an example of eight transmit antennas (N ═ 8) is shown in fig. 4, with four antennas at each polarization (light and dark shades). For each of the four pairs, the two antennas are mounted in orthogonal polarization directions, +45/-45 angles, or so-called cross-polarizations. The spacing between adjacent beamforming elements is typically half the wavelength at which four-element beamforming is implemented. Such a beamforming arrangement is also referred to as a Uniform Linear Array (ULA) since the antenna spacing is uniform.

In such an antenna configuration, high spatial correlation is expected between four antennas of the same polarization, while low spatial correlation is expected between antennas of different polarizations. Thus, the spatial CSI is decomposed between the beamforming antennas and the cross-polarized antennasReasonably, as shown in equation (3). More specifically, the 8 × 8 covariance matrix is decomposed into a 4 × 4 component matrix R_ULAAnd a 2 × 2 component matrix R_Pol。

Then, for each component covariance matrix, the appropriate codebook is used for quantization. The codeword index may be selected to minimize the distance between the quantized covariance matrix and the floating point covariance matrix. For example, the distance may be measured as,

wherein the content of the first and second substances,is the ith quantized eigenvector of spatial channel H, which corresponds to the ith column of the codeword, and λ_i|²Is R_ULAOr R_PolThe ith eigenvalue of (1). Note that the index may indicate a vector or a matrix in the codebook.

For the antenna configuration shown in fig. 4, most likely one index indication corresponds to R_ULA4 x 1 vector (codeword). Mathematically, such a 4 × 1 vector can be represented as [1, e^j2∏θ，e^j4∏θ，e^j6∏θ]^TWhere θ is determined by the wavelength, the antenna spacing between adjacent ULA elements, and the emission angle (AoD) of the mobile device to the line of sight of the ULA. Another index indication corresponds to R_Pol2 x 1 vectors (codewords) or 2 x 2 matrices (codewords). A 2 × 1 vector may be selected from table 1 (number of layers 1), for example, in the form of [1, a when the normalization constant is ignored₁]^T. The 2 × 2 matrix may be selected from table 1 (number of layers 2), for example, in the form of [1, a when the normalization constant is ignored₁；1，a₂]^T. Thus, the spatial CSI feedback will contain two indices.

When CSI feedback is received from the UE, a series of operations are to be performed. First by looking up the feedback index in the corresponding codebook and then performing an outer product, e.g.To reconstruct each quantized component CSI, e.g. R_ULAAnd R_Pol. Next, by quantized R_ULAAnd R_PolThe kronecker product of (a) to derive a quantized composite CSI, e.g., R. Finally, the quantized composite CSI is used to compute a precoding matrix.

The above composite spatial CSI reconstruction process at the transmitter can also be pre-processed by combining the codebook of the beamformed ULA with the cross-polarized codebook. The principle follows equation (4), whichIs the kronecker product of the beamforming ULA and the eigenvalues of the cross polarization. In particular, the combination is by means of cross-polarized code word vectors or matrices (e.g. [1, a ]₁]^TOr [1, a₁；1，a₂]^T) And the codeword vector of the ULA (e.g., [1, e ]^j2∏θ，e^j4∏θ，e^j6∏θ]) The kronecker product of (a). Each codeword in the combined codebook, while still separately for the ULA and cross-polarization index, will take on a value such as [1, e ] for a rank of 1^j2∏θ，e^j4∏θ，e^j6∏θ，a₁，a₁e^j2∏θ，a₁e^j4∏θ，a₁e^j6∏θ]^TOr for a level of 2 would take a form such as [1, e ]^j2∏θ，e^j4∏θ，e^j6∏θ，a₁，a₁e^j2∏θ，a₁e^j4∏θ，a₁e^j6∏θ；1，e^j2∏θ，e^j4∏θ，e^j6∏θ，a₂，a₂e^j2∏θ，a₂e^j4∏θ，a₂e^j6∏θ]^TIn the form of (1).

While embodiments of the present invention have been shown and described, it will be apparent to those skilled in the art that various modifications are possible without departing from the inventive concepts herein. Accordingly, the invention is not to be restricted except in the spirit of the appended claims.

Claims (26)

1. A method of providing spatial channel state information for a multiple-input multiple-output technique having a transmitting device and a receiving device, the method comprising:

measuring spatial channel state information at the receiving device to generate CSI;

decomposing the CSI to generate at least a first component CSI and a second component CSI;

quantizing the first component CSI and the second component CSI using one or more of a plurality of codebooks, resulting in at least a first index and a second index, wherein both the first index and the second index point to: either (i) a vector in one of the plurality of codebooks, or (ii) a matrix in one of the plurality of codebooks; and

feeding back the first index and the second index to the transmitting device;

wherein decomposing the CSI includesTo decompose the CSI, wherein R_ULAIs the first component CSI, and R_PolIs the second component CSI.

2. The method of claim 1, wherein decomposing the CSI comprises decomposing the CSI using a kronecker product.

3. The method of claim 1, wherein the CSI is for one of a channel matrix H and a covariance matrix R.

4. The method of claim 3, wherein at least one of the channel matrix H and the covariance matrix R is quantized.

5. The method of claim 3, further comprising decomposing the covariance matrix R into a 4 x 4 first component matrix R_ULAAnd a 2 x 2 second component matrix R_Pol。

6. The method of claim 3, wherein decomposing the covariance matrix R comprises applying a mixed product attribute of a kronecker product.

7. The method of claim 1, wherein quantizing the first component CSI and the second component CSI comprises quantizing the first component CSI and the second component CSI using different codebooks, respectively, of the plurality of codebooks.

8. The method of claim 1, wherein the quantifying comprises computing a metric measured asThe distance of (a), wherein,is the ith quantized eigenvector of spatial channel H, which corresponds to the ith column of codewords, | λ_i|²Is a first component matrix R_ULAAnd a second component matrix R_PolN is the number of transmitter antennas.

9. The method of claim 1, wherein at least one of the first component CSI and the second component CSI is represented by a kronecker product of two codewords, the first codeword being a 4 x 1 vector and the second codeword being one of a 2 x 1 vector and a 2 x 2 matrix.

10. The method of claim 1, wherein the first component CSI represents characteristics of a beamforming antenna and the second component CSI represents characteristics of a cross-polarized antenna.

11. The method of claim 10, wherein the characteristics of the beamforming antenna are represented by a Uniform Linear Array (ULA) comprising four elements and the characteristics of the cross-polarized antenna are represented by two antenna elements.

12. The method of claim 1, further comprising calculating an outer product.

13. The method of claim 12, wherein the calculating is byAnd (c) characterizing, wherein,is the ith quantized eigenvector of spatial channel H, which corresponds to the ith column of codewords, | λ_i|²Is a first component matrix R_ULAAnd a second component matrix R_PolThe ith eigenvalue of one of them.

14. A system for providing spatial channel state information for multiple-input multiple-output techniques, the system comprising:

means for measuring spatial channel state information and generating CSI at a receiving device;

means for decomposing the CSI and generating at least a first component CSI and a second component CSI;

means for quantizing the first component CSI and the second component CSI using one or more of a plurality of codebooks and generating at least a first index and a second index, wherein both the first index and the second index point to: either (i) a vector in one of the plurality of codebooks, or (ii) a matrix in one of the plurality of codebooks; and

feeding back the first index and the second index to a transmitting device;

wherein the means for decomposing the CSI includes means for decomposing the CSI in accordance withMeans for decomposing said CSI, wherein R_ULAIs the first component CSI, and R_PolIs the second component CSI.

15. The system of claim 14, wherein the means for decomposing the CSI comprises means for decomposing the CSI using a kronecker product.

16. The system of claim 14, wherein the CSI is for one of a channel matrix H and a covariance matrix R.

17. The system of claim 16, wherein at least one of the channel matrix H and the covariance matrix R is quantized.

18. The system of claim 16, further comprising decomposing the covariance matrix R into a 4 x 4 first component matrix R_ULAAnd a 2 x 2 second component matrix R_PolThe apparatus of (1).

19. The system of claim 16, wherein the means for decomposing the covariance matrix R comprises applying a mixed product property of a kronecker product.

20. The system of claim 14, wherein quantizing the first component CSI and the second component CSI comprises quantizing the first component CSI and the second component CSI using different codebooks, respectively, of the plurality of codebooks.

21. The system of claim 14, wherein the means for quantifying comprises computing a metric measured asThe distance of (a), wherein,is the ith quantized eigenvector of spatial channel H, said ith quantized eigenvector corresponding to the ith column of the codeword,|λ_i|²is a first component matrix R_ULAAnd a second component matrix R_PolN is the number of transmitter antennas.

22. The system of claim 14, at least one of the first component CSI and the second component CSI is represented by a kronecker product of two codewords, the first codeword being a 4 x 1 vector and the second codeword being one of a 2 x 1 vector and a 2 x 2 matrix.

23. The system of claim 14, wherein the first component CSI represents characteristics of a beamforming antenna and the second component CSI represents characteristics of a cross-polarized antenna.

24. The system of claim 23, wherein the characteristics of the beamforming antenna are represented by a Uniform Linear Array (ULA) comprising four elements and the characteristics of the cross-polarized antenna are represented by two antenna elements.

25. The system of claim 14, further comprising means for calculating an outer product.

26. The system of claim 25, wherein the means for computing passes throughAnd (c) characterizing, wherein,is the ith quantized eigenvector of spatial channel H, which corresponds to the ith column of the codeword, and λ_i|²Is a first component matrix R_ULAAnd a second component matrix R_PolThe ith eigenvalue of one of them.