Downlink transmission in a MU-MIMO system

Abstract

A method is provided for managing a downlink transmission in a Multi User-Multiple Input Multiple Output (MU-MIMO) system. The MU-MIMO system includes a base station and a set of remote radio units connected to the base station. The method includes: obtaining large scale fading data related to a large scale fading over uplink transmission associated with a user equipment (UE); generating a UE-specific channel vector by using the large scale fading data; and scheduling a downlink transmission by using the UE-specific channel vector.

Claims

1. A method comprising: managing a downlink transmission in a Multi User-Multiple Input Multiple Output MU-MIMO system, the MU-MIMO system comprising a base station, and a set of remote radio units connected to the base station, the method comprising the following acts performed by the base station: obtaining large scale fading data related to a large scale fading over uplink transmission associated with a user equipment (UE), the act of obtaining comprising sorting remote radio units antennas sensed by the user equipment into two sets Di and Ui which are expressed as:
D.sub.i={j:l.sub.ij>l.sub.ik,∀kεU.sub.i}
U.sub.i={j:l.sub.ik>l.sub.ij,∀kεD.sub.i} where l.sub.ij refers to an estimation of large scale channel gain from user equipment i to antenna j, generating a UE-specific channel vector by using the large scale fading data, and scheduling the downlink transmission in the MU-MIMO system by using the UE-specific channel vector.

2. The method according to claim 1, wherein the said large scale fading data is obtained by the base station performing a large scale fading estimation.

3. The method according to claim 2, further comprising: receiving a Sounding Reference Signal SRS received from the user equipment, wherein the estimation is performed by the base station using the Sounding Reference Signal SRS received from the user equipment.

4. The method according to claim 1, wherein the said large scale fading data is obtained by performing a large scale fading estimation by a user equipment, and wherein the act of obtaining the large scale fading data comprises the base station receiving the large scale fading estimation from the user equipment.

5. The method according to claim 4, wherein the method comprises the base station transmitting a Channel State Indication CSI-RS to the user equipment, and wherein the estimation is performed by using the Channel State Indication CSI-RS received at the user equipment from the base station.

6. The method according to claim 1, wherein the act of generating comprises the base station calculating a UE-specific channel vector as: ${\tilde{h}}_{i} = {\hat{h}}_{i} .Math. diag (V_{i} .Math. \sqrt{\frac{M_{t}}{M_{i}}})$ where ĥ.sub.i is a code vector indexed by a user equipment feedback in a Discrete Fourier Transformation codebook, M.sub.t is a total number of remote radio units antennas, M.sub.i is a size of set Ui, and Vi is a vector defined as V.sub.i=(v.sub.i,1, v.sub.i,2, . . . , v.sub.i,M.sub.t), where $v_{i, j} = {\begin{matrix} 1 & j \in U_{i} \\ 0 & j \in D_{i} \end{matrix} .$

7. A non-transitory computer readable medium comprising a computer program product and having thereon a computer program comprising program instructions, the computer program being loadable into a data-processing unit and adapted to cause the data-processing unit of a base station to carry out a method when the computer program is run by the data-processing unit, the method comprising: managing a downlink transmission by the base station in a Multi User-Multiple Input Multiple Output MU-MIMO system, the MU-MIMO system comprising the base station, and a set of remote radio units connected to the base station, wherein managing comprises: obtaining large scale fading data related to a large scale fading over uplink transmission associated with a user equipment (UE), the act of obtaining comprising sorting remote radio units antennas sensed by the user equipment into two sets Di and Ui which are expressed as:
D.sub.i={j:l.sub.ij>l.sub.ik,∀kεU.sub.i}
U.sub.i={j:l.sub.ik>l.sub.ij,∀kεD.sub.i} where l.sub.ij refers to an estimation of large scale channel gain from user equipment i to antenna j, generating a UE-specific channel vector by using the large scale fading data, and scheduling a downlink transmission in the MU-MIMO system by using the UE-specific channel vector.

8. A base station of a Multi User-Multiple Input Multiple Output MU-MIMO system, the MU-MIMO system further comprising a set of remote radio units connected to the base station, the base station comprising: a non-transitory computer-readable medium comprising instructions stored thereon; a data processing unit configured by the instructions to perform acts of: managing a downlink transmission in the Multi User-Multiple Input Multiple Output MU-MIMO system, wherein managing comprises: obtaining large scale fading data related to a large scale fading over uplink transmission associated with a user equipment, the act of obtaining comprising sorting remote radio units antennas sensed by the user equipment into two sets Di and Ui which are expressed as:
D.sub.i={j:l.sub.ij>l.sub.ik,∀kεU.sub.i}
U.sub.i={j:l.sub.ik>l.sub.ij,∀kεD.sub.i} where l.sub.ij refers to an estimation of large scale channel gain from user equipment i to antenna j, generating a UE-specific channel vector by using the large scale fading data, and scheduling the downlink transmission in the MU-MIMO system by using the UE-specific channel vector.

9. A Multi User-Multiple Input Multiple Output MU-MIMO system, comprising the base station according to claim 8, and the set of remote radio units connected to the base station.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) The present invention is illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings, in which like reference numerals refer to similar elements and in which:

(2) FIG. 1 is a schematic diagram of a MU-MIMO system based on distributed antenna architecture, according to some embodiments of the invention;

(3) FIG. 2 is a flow chart showing steps of a method for managing a downlink transmission in a MU-MIMO system, according to some embodiments of the invention:

(4) FIG. 3 is a schematic diagram similar to FIG. 1, where the MU-MIMO system comprises a BS connected to two RRUs; and

(5) FIG. 4 to FIG. 7 are charts which show the system throughput versus the number of UEs in four scenario.

DESCRIPTION OF EMBODIMENTS

(6) Embodiments of the invention deal with the problem of improving the downlink transmission in a Multi User-Multiple Input Multiple Output (MU-MIMO) system based on distributed antenna architecture.

(7) FIG. 1 represents a MU-MIMO system based on distributed antenna architecture. The MU-MIMO system comprises a base station (BS) 1, which is equipped with a powerful base band unit (BBU), and several remote radio units (RRUs) 2a-2c, which are respectively connected to the BS 1, for example over high-speed optical fibers 3a-3c.

(8) The BS 1 is configured to perform base band processing, and to manage radio resource and network. Each RRU 2a-2c is equipped with multiple antennas and is configured to perform a conversion between radio frequency (RF) and digital intermediate frequency (IF) signals.

(9) The MU-MIMO system is configured to receive signals from a set of User Equipments (UE) 4a-4d, and to transmit signals to the set of UEs 4a-4d.

(10) FIG. 2 represents steps of a method for managing a downlink transmission in a Multi User-Multiple Input Multiple Output MU-MIMO system, according to some embodiments of the invention.

(11) In step S1, the BS 1 obtains large scale fading data corresponding to large scale fading over uplink transmission associated with a UE 4i, index i being equal to a, b, c or d in the example of FIG. 1.

(12) There may be two ways for the BS 1 to obtain the large scale fading data. The first is based on the UE's report, and the second is based on an estimation of the large scale fading performed at the BS 1.

(13) According to a first embodiment of the invention, the large scale fading data is received at the BS 1 from the UE 4i.

(14) A large scale fading estimation is here performed at the UE 4i, by using the Channel State Indication CSI-RS.

(15) Then, the UE 4i performs an operation of sorting antennas of RRUs sensed by the UE 4i into the two sets Di and Ui, which are expressed as:
D.sub.i={j:l.sub.ij>l.sub.ik,∀kεU.sub.i} (15)
U.sub.i={j:l.sub.ik>l.sub.ij,∀kεD.sub.i} (16)
where l.sub.ij refers to an estimation of large scale channel gain from UE 4i to antenna j.

(16) Then, the UE 4i calculates a vector Vi, containing the large scale fading data, and defined as V.sub.i=(v.sub.i,1, v.sub.i,2, . . . , v.sub.i,M.sub.t), where:

(17) $v_{i, j} = {\begin{matrix} 1 & j \in U_{i} \\ 0 & j \in D_{i} \end{matrix}$

(18) Then, the UE 4i determines a codebook Ci oriented to UE as:

(19) $\begin{matrix} C_{i} = diag (V_{i} .Math. \sqrt{\frac{M_{t}}{M_{i}}}) .Math. C_{DFT} & (19) \end{matrix}$

(20) where C.sub.DFT refers to the DFT codebook defined in 3GPP LTE-A.

(21) Then, the UE 4i calculates its channel direction information (CDI) and channel quality information (CQI) with the codebook Ci, for example by following the way defined in the 3GPP LTE-A specification.

(22) Then, in the feedback from the UE 4i to the BS 1, the UE 4i reports its CDI, its CQI, and its vector Vi. The feedback may be performed periodically.

(23) For instance, based on the downlink CSI-RS estimation, vector V.sub.1 determined at UE 4.sub.1 may be V.sub.1=(1,0,0). Then, the UE 4.sub.1 may calculate its CDI and CQI with the modified codebook C.sub.1=diag(√{square root over (3)}V.sub.1).Math.C.sub.DFT. Finally, the CQI feedback is calculated based on a vector of (h1,0,0), while CDI reported to BS 1 still appears as the index pointing to the vector (h1,h2,h3) in the 3GPP LTE-A DFT codebook. At the side of BS 1, the BS 1 uses the CDI and the vector V.sub.1 reported from UE 4.sub.1 to determine the channel vector oriented to UE 4.sub.1 as (h1,0,0).

(24) According to a second embodiment of the invention, the large scale fading data is obtained by performing a large scale fading estimation at the BS 1. The estimation may be performed by using a Sounding Reference Signal SRS received from the UE 4i.

(25) Here, the UE 4i estimates a large scale fading of CSI-RS over downlink to compute its vector Vi. Then, the UE 4i uses the vector Vi to calculate the CDI and CQI to report to BS 1. The vector Vi is not reported to the BS 1.

(26) For instance, based on the downlink CSI-RS estimation, vector V.sub.1 determined at UE 4.sub.1 is V.sub.1=(1,0,0). Then, the UE 4.sub.1 calculates its CDI and CQI with the modified codebook C.sub.1=diag(√{square root over (3)}V.sub.1).Math.C.sub.DFT. Finally, the CQI feedback is calculated based on the vector of (h1,0,0), while CDI reported to BS 1 still appears as the index pointing to (h1,h2,h3) in the 3GPP LTE-A DFT codebook.

(27) Step S1 then comprises, at the BS 1, an operation of sorting antennas of RRUs sensed by the UE 4i into the two sets Di and Ui defined above.

(28) For example, the generation of sets Ui and Di can be described as following:

(29) TABLE-US-00002 TABLE 2 Step 1: List the BS's antennas as T.sub.i = (t.sub.1,t.sub.2,. . . ,t.sub.M.sub.i) with the ascending order of l.sub.ij Step 2: Set D.sub.i = ∅ and U.sub.i = T.sub.i Step 3: For each t.sub.k in T.sub.i $If {(\sqrt{\frac{M_{i}}{M_{i} - 1}} - 1)}^{2} \geq \frac{l_{{it}_{k}}^{2}}{\underset{{jeU}_{i}}{.Math.} l_{ij}^{2} - l_{{it}_{k}}^{2}} Then$ 1) D.sub.i = D.sub.i+{t.sub.k} 2) U.sub.i = U.sub.i −{t.sub.k} Else Break

(30) Then, a vector Vi containing the large scale fading data is defined as V.sub.i=(v.sub.i,1, v.sub.i,2, . . . , v.sub.i,M.sub.t), where:

(31) $v_{i, j} = {\begin{matrix} 1 & j \in U_{i} \\ 0 & j \in D_{i} \end{matrix}$

(32) In the example, at the side of BS 1, by estimating SRS over uplink, the BBU can obtain the vector V.sub.1 expressed as (1,0,0) as well. After receiving the CDI report from UE 4.sub.1, the BS 1 can extract the channel vector of (h1,h2,h3) from DFT codebook. Then, the BS 1 may deduce the channel vector oriented to UE 4.sub.1 as (h1,0,0).

(33) Step S1 is repeated for each UE 4a-4d.

(34) In some embodiments of the invention, a part of the UEs 4a-4d may report their respective vector Vi, while other part of the UEs 4a-4d do not report their vectors Vi.

(35) Step S1 may thus comprise, at the BS 1, an operation of checking whether the vector Vi has been reported for a given UE 4i. Then, if the UE 4i merely reports its CDI and CQI, the BS 1 has to use the second embodiment disclosed above to determine the UE-specific channel vector. If the UE 4i reports the vector Vi in addition to CDI and CQI, the BS 1 can use the first embodiment disclosed above to generate the UE-specific channel vector.

(36) It has to be noted that vector Vi determined at a UE 4i may be different from vector Vi determined at BS 1, even if they are computed in the same way. Indeed, the vector Vi determined at the UE 4i is calculated based on an estimation of CSI-RS over downlink, while the vector Vi determined at the BS 1 is calculated based on an estimation of SRS over uplink. However, if the BS 1 can acquire the vector Vi from UE's report, it doesn't need to calculate the vector Vi by itself.

(37) In step S2, the BS 1 generates a UE-specific channel vector by using the large scale fading data obtained in step S1.

(38) The BS 1 calculates the UE-specific channel vector, for the UE 4i, as:

(39) $\begin{matrix} {\tilde{h}}_{i} = {\hat{h}}_{i} .Math. diag (V_{i} .Math. \sqrt{\frac{M_{t}}{M_{i}}}) & (17) \end{matrix}$

(40) where ĥ.sub.i is a code vector indexed by a UE feedback in a Discrete Fourier Transformation (DFT) codebook, M.sub.t is a total number of RRU antennas, and M.sub.i is a size of set Ui.

(41) Step S2 is repeated for each UE 4a-4d.

(42) In step S3, the BS 1 schedules a downlink transmission by using the UE-specific channel vectors calculated in step S2.

(43) FIG. 3 represents a MU-MIMO system where the BBU 1 connects two RRUs 2a-2b that cover three areas named as A, B C, respectively. This system is used to evaluate a performance by simulating the system throughput in different scenarios.

(44) To simplify the simulation, we assume that the UEs in a same area have the same distance to a given RRU 2a-2b. For instance, the distance between all the UEs in area A and RRU 2b is considered to be a constant. Similarly, the UEs in area A have a constant distance to RRU 2a. In addition, it is assumed that there is no penetration loss and shadow fading between the RRU 2a-2b and the UEs in its coverage. For example, it is assumed that the UEs in area A have no penetration loss and shadow fading to RRU 2a, but there is penetration loss and shadow fading between RRU 2a and the UEs in area C.

(45) For the performance evaluation, we consider four scenarios: I—UEs are located in area A, II—UEs are equally located in areas A and C, III—UEs are equally located in areas A, B and C, and IV—UEs are equally located in area A and B.

(46) Other simulation parameters are listed in Table 3.

(47) TABLE-US-00003 TABLE 3 Number of antennas per RRU 2 Number of antennas per UE 1 Shadowing model log-normal random variable, standard deviation = 8.0 dB Fading model Flat Rayleigh fading Distance between RRU1and UEs in area A 5 m Distance between RRU1 and UEs in area B 20 m Distance between RRU1 and UEs in area C 30 m Distance between RRU2 and UEs in area A 30 m Distance between RRU2 and UEs in area B 20 m Distance between RRU2 and UEs in area C 5 m Distance dependent path loss 126.3 + 38 × log.sub.10 (d) dB Penetration loss 10 dB, 100 dB Radio Receiver Sensitivity -110 dBm Transmit Power per RRU 20 dBm Noise Power -104 dBm Bits for feedback 4

(48) FIG. 4 to FIG. 7 are charts which show the system throughput versus the number of UEs in Scenario I (FIG. 4), in Scenario II (FIG. 5), in Scenario III (FIG. 6) and in Scenario IV (FIG. 7).

(49) In the figures, the curves with the name of CVQ indicates the throughput achieved by the 3GPP LTE-A method, the curves identified as Sensitivity-based refers to the performance of the system disclosed in WO 2011/077260, while the curves termed as Large Scale Fading illustrate the simulation results with the method according to the invention.

(50) It is observed that the 3GPP LTE-A method always has the poor system throughput both in the case of 10 dB penetration loss and 100 dB penetration loss. For the system disclosed in WO 2011/077260, the performance improvement is obvious as the penetration loss is 100 dB, but its performance degrades dramatically in the case with the 10 dB penetration loss.

(51) The method according to the invention always achieves the significant throughput improvement, which reaches 600% at most, no matter in the case whose penetration loss is 10 dB or 100 dB.

(52) While there has been illustrated and described what are presently considered to be the preferred embodiments of the present invention, it will be understood by those skilled in the art that various other modifications may be made, and equivalents may be substituted, without departing from the true scope of the present invention. Additionally, many modifications may be made to adapt a particular situation to the teachings of the present invention without departing from the central inventive concept described herein. Furthermore, an embodiment of the present invention may not include all of the features described above. Therefore, it is intended that the present invention not be limited to the particular embodiments disclosed, but that the invention include all embodiments falling within the scope of the invention as broadly defined above.

(53) Expressions such as “comprise”, “include”, “incorporate”, “contain”, “is” and “have” are to be construed in a non-exclusive manner when interpreting the description and its associated claims, namely construed to allow for other items or components which are not explicitly defined also to be present. Reference to the singular is also to be construed in be a reference to the plural and vice versa.

(54) A person skilled in the art will readily appreciate that various parameters disclosed in the description may be modified and that various embodiments disclosed may be combined without departing from the scope of the invention.

Downlink transmission in a MU-MIMO system

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Abstract

Claims

Description