METHODS FOR MULTI-USER MIMO WIRELESS COMMUNICATION USING APPROXIMATION OF ZERO-FORCING BEAMFORMING MATRIX

Abstract

This invention presents methods for signal detection and transmission in MU-MIMO wireless communication systems, for inverse matrix approximation error calculation, for adaptively selecting the number of multiplexed UEs in a MU-MIMO group, for adaptively choosing a modulation and channel coding scheme appropriate for the quality of MU-MIMO channels with the approximation error of matrix inverse being incorporated.

Claims

1. A method for link adaption for MU-MIMO wireless communication systems comprising a BS calculating the SIRs caused by a precoding matrix obtained using an approximation; using the SIRs, the BS selecting an accuracy level of approximation; and, the BS determining the number of UEs in a MU-MIMO group.

2. The method in claim 1 further comprising the BS selecting a desired MCS for a MU-MIMO group.

3. The method in claim 2 wherein the desired MCS is the maximum MCS.

4. The method in claim 1 wherein the approximation includes the computation of an AIM.

5. The method in claim 4 wherein an accuracy level of the approximation is determined by the order of truncation in the computation of an AIM.

6. The method in claim 5 further comprising a minimal truncation order N.sub.min being pre-defined at the BS as the following steps: the BS first calculates the required SINR denoted by SINR.sub.max corresponding to the maximum MCS of a MU-MIMO group in a specific resource block; then, the BS compares the SIR denoted by SIR.sub.max corresponding to N.sub.min and SINR.sub.max; if SIR.sub.max≧SINR.sub.max, the BS chooses the truncation order as N.sub.min; otherwise, the BS chooses the truncation order that the corresponding SIR is no less than SINR.sub.max; if no one truncation order can be find to satisfy the condition that the corresponding SIR is no less than SINR.sub.max, the BS chooses the truncation order as 2N.sub.max.

7. The method in claim 1 further comprising being applied to both the uplink and downlink transmission in wireless communication systems.

8. The method in claim 1 further comprising being applied to an FDD wireless communication system.

9. The method in claim 1 further comprising being applied to a TDD wireless communication system.

10. The method in claim 1 further comprising the BS calculating the SIRs corresponding to different numbers of UEs in a MU-MIMO group.

11. The method in claim 1 further comprising the BS calculating the SIRs corresponding to different accuracy levels of approximation.

12. The method in claim 11 further comprising the SIRs associated with truncation orders no more than N.sub.max being pre-calculated and stored in the memory of the BS and being recalled when needed.

13. The method in claim 1 further comprising the BS modifying the CQI of a UE with the SIR caused by the approximation and selecting the MCS for a UE with the modified CQI.

14. The method in claim 1 further comprising the BS determining the maximum number of UEs in a MU-MIMO group for each MCS level for a pre-defined accuracy level of approximation.

15. The method in claim 14 further comprising the BS selecting the number of UEs in a MU-MIMO group so that the SIR corresponding to the chosen number is larger than the required SINR corresponding to a MCS level.

16. The method in claim 1 further comprising the BS determining the maximum number of UEs in a MU-MIMO group by calculating the ratio between the number of BS antennas and the number of UEs in a MU-MIMO group so that the ratio is no more than a specific value.

17. The method in claim 16 further comprising the specific value being determined by a pre-defined fixed value or $\frac{M\left[E\left(x\right)+\delta \left(x\right)\right]}{E\left(x\right)+\delta \left(x\right)+1}$ where E(x)=(M−1)B(1.5, M−1) and δ(x)=√{square root over (E(x.sup.2)−E(x).sup.2)} with E(x.sup.2)=(M−1)B(2, M−1), and the function B(a, b) with a and b being complex-valued numbers is the beta function defined as
B(a, b)=∫.sub.0.sup.1t.sup.a−1(1−t).sup.b−1dt,{a},{b}>0.

Description

BRIEF DESCRIPTION OF DRAWINGS

[0012] The aforementioned implementation of the invention as well as additional implementations would be more clearly understood as a result of the following detailed description of the various aspects of the invention when taken in conjunction with the drawings. Like reference numerals refer to corresponding parts throughout the several views of the drawings.

[0013] FIG. 1 illustrates a typical massive MU-MIMO communication system.

[0014] FIG. 2 illustrates the process of selecting MCS for each UE by incorporating the approximation error of AIM in the uplink.

[0015] FIG. 3 shows the flowchart for determining the number of served UEs K for a given M.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

[0016] FIG. 1 presents a typical massive MU-MIMO communication system, where the BS 1 is equipped with a large number of antennas 2 to serve several UEs 3 in the same time-frequency resource. For such systems, NS can be employed to approximate the inverse matrix for ZF based detection methods in the uplink and precoding methods in the downlink. The SIR caused by approximation error of AIM with various numbers of transceiving antennas, multiplexed UEs, and truncation orders can be calculated by formulas (1)-(4) given below. Moreover, these values can be calculated off-line and stored in the memory of the BS in advance, e.g., in the form of a lookup table. For example, let M, K, and N denote the three aforementioned numbers respectively, where N≦4, then the SIR is calculated by the following formulas

[00001]$\begin{array}{cc}.Math.S.Math..Math.I.Math..Math.R_1\left(M,K\right)=\frac{{\beta }_1}{\left(K-1\right).Math.B_{2,M}},N=1,& \left(1\right)\\ .Math.S.Math..Math.I.Math..Math.R_2\left(M,K\right)=\frac{{\beta }_2}{\left(K-1\right).Math.B_{3,M}+2.Math.\left(K-2\right).Math.\left(K-1\right).Math.B_{2,M}^2},.Math.N=2,& \left(2\right)\\ S.Math..Math.I.Math..Math.R_3\left(M,K\right)=\frac{{\beta }_3}{\left(K-2\right).Math.\left(K-1\right).Math.\left(5.Math.K-8\right).Math.B_{2,M}^3+\left(2.Math.K-3\right).Math.\left(K-1\right).Math.B_{3,M}.Math.B_{2,M}},.Math..Math.N=3,& \left(3\right)\\ S.Math..Math.I.Math..Math.R_4\left(M,K\right)=\frac{{\beta }_4}{\begin{array}{c}\left(2.Math.K-3\right).Math.\left(K-1\right).Math.B_{3,M}^2+{\left(2.Math.K-3\right)}^2.Math.{\left(K-1\right)}^2.Math.B_{4,M}.Math.B_{2,M}+\\ \left(K-2\right).Math.{\left(K-1\right)}^2.Math.{\mathrm{KB}}_{2,M}^4\end{array}},.Math..Math.N=4,& \left(4\right)\end{array}$

where β.sub.1, β.sub.2, β.sub.3 and β.sub.4 are scaling factors which are determined by the parameters α, M, K and N, e.g., when α=0, then β.sub.1=β.sub.2=β.sub.2=β.sub.4=1.

[0017] For uplink data transmission, supposing that the number of receiving antennas is M and the number of UEs multiplexed on a specific RB is K, three methods to combat the approximation error of AIM are presented below.

[0018] Method-1

[0019] A minimal truncation order N.sub.min, N.sub.min≦4, of NS is configured in the BS. When the BS detects the signals belonging to the K UEs on a specific RB, it first finds the maximal MCS of these K UEs, which is denoted by MCS.sub.max. Then, it compares the minimal required SINR for MCS.sub.max denoted by SINR.sub.MCSmax and SIR.sub.N.sub.min(M, K). If SINR.sub.MCSmax≦SIR.sub.N.sub.min(M, K), the BS would adopt N.sub.min for NS on the current RB. Otherwise, the BS would find the minimal N, N≦4, so that SINR.sub.MCSmax≦SIR.sub.N(M, K). If the number of N that satisfies the condition cannot be found, the BS would choose N=8.

[0020] Method-2

[0021] When the BS selects the MCS for each UE multiplexed on a RB according to their CQIs, it modifies the CQI of each UE first by incorporating the approximation error of AIM. Then, it selects the MCS for each UE according to the modified CQI. For example, let CQI.sub.k denote the linear CQI value of the k.sup.th, k=1, . . . , K, UE before being modified, then the BS modifies it to CQI.sub.k.sup.New according to the following formula

[00002]$\begin{array}{cc}C.Math..Math.Q.Math..Math.I_k^{\mathrm{New}}=\frac{C.Math..Math.Q.Math..Math.I_k\times S.Math..Math.I.Math..Math.R_N\left(M,K\right)}{C.Math..Math.Q.Math..Math.I_k+S.Math..Math.I.Math..Math.R_N\left(M,K\right)},k=1,\mathrm{.Math.}.Math.,K,& \left(5\right)\end{array}$

where M and K denote the number of receiving antennas at the BS and the number of UEs multiplexed on a RB respectively. Finally, the BS selects the MCS for the k.sup.th UE according to CQI.sub.k.sup.New. This process is illustrated in FIG. 2, which begins 4 when the BS schedules K UEs on a RB 5. Then, when the BS selects the MCS for each UE multiplexed on this RB according to their estimated CQI, it modifies the CQI of each UE first by incorporating the approximation error of AIM 6. After that, it selects the MCS according to the modified CQI 7 before the process ends 8.

[0022] Method-3

[0023] A fixed value of truncation order N is configured for NS in the BS. For each allowable MCS in the uplink transmission, the maximal number of UEs multiplexed on a RB is calculated off-line and stored in the memory of the BS. For example, assuming L.sub.UL MCS levels in the uplink transmission of a wireless communication system, for the l.sup.th MCS, the minimal required SINR for the system specified Block Error Rate (BLER) is SINR.sub.l.sup.min, then the maximum number of multiplexed UE can be computed as

K.sub.k=arg max.sub.k(SIR.sub.N(M, k)≧SINR.sub.l.sup.min), l=1, . . . , L.sub.UL.   (6)

Hence, the l.sup.th, l=1, . . . , L.sub.UL, MCS level and its corresponding K.sub.1 are stored in the memory of the BS. For each RB, the BS could determine the highest MCS level and the corresponding maximal number of multiplexed UE multiplexed according to their relation determined by (6).

[0024] For the downlink data transmission, supposing that the number of transmitting antenna is M and the number of UE multiplexed on the current RB is K, three methods to combat the approximation error are presented below.

[0025] Method-1

[0026] A minimal truncation order N.sub.min, N.sub.min<4, of NS is configured in the BS. When the BS computes the precoding matrix of these K UEs on a specific RB, it first finds the maximal MCS of these K UEs, which is denoted by MCS.sub.max. Then, it compares the minimal required SINR for MCS.sub.max denoted by SINR.sub.MCSmax and SIR.sub.N.sub.min (M, K). If SINR.sub.MCSmax≦SIR.sub.N.sub.min (M, K), the BS would adopt N.sub.min for NS on the current RB. Otherwise, the BS would find the minimal N, N≦4, so that SINR.sub.MCSmax≦SIR.sub.N(M, K). If the number of N that satisfies the condition cannot be found, the BS would choose N=8.

[0027] Method-2

[0028] For example, let CQI.sub.k denote the linear CQI value of the k.sup.th UE before modified, then the BS modifies it to CQI.sub.k.sup.New according to the following formula

[00003]$\begin{array}{cc}C.Math..Math.Q.Math..Math.I_k^{\mathrm{New}}=\frac{C.Math..Math.Q.Math..Math.I_k\times S.Math..Math.I.Math..Math.R_N\left(M,K\right)}{C.Math..Math.Q.Math..Math.I_k+S.Math..Math.I.Math..Math.R_N\left(M,K\right)},k=1,\mathrm{.Math.}.Math.,K& \left(7\right)\end{array}$

where M and K denote the number of transmitting antennas at the BS and the number of UEs multiplexed on a RB respectively. Finally, the BS selects the MCS for the k.sup.th UE according to CQI.sub.k.sup.New.

[0029] Method-3

[0030] A fixed value of truncation order N is configured for NS in the BS. For each allowable MCS in the downlink transmission, the maximal number of UEs multiplexed on a RB is calculated off-line and stored in the memory of the BS. For example, assuming a total of L.sub.DLMCS levels in the downlink transmission of a wireless communication system, for the l.sup.th MCS, the minimal required SINR for the system specified BLER is SINR.sub.l.sup.min, then the maximum number of multiplexed UEs can be computed as

K.sub.l=arg max.sub.k(SIR.sub.N(M, k)≧SINR.sub.l.sup.min), l=1, . . . , L.sub.DL.   (8)

Hence, the l.sup.th, l=1, . . . , L.sub.DL MCS level and its corresponding K.sub.l are stored in a table. For each RB, the BS could determine the highest MCS level and the corresponding maximal number of multiplexed UE according to the relation determined by (8).

[0031] Another embodiment provides a method to estimate the probability of convergence of NS in calculating the AIM. Given the number of BS antennas M, this estimate can be used to determine the maximum number of served UEs K for the NS-based AIM to be a valid method in massive MIMO systems. One of such estimates is given as

[00004]$\frac{M}{K}>5.83$

which indicates that the NS-based AIM has very high convergence probability.

[0032] A tighter condition for G=Ĥ.sup.HĤ to be a Diagonally Dominant Matrix (DDM) in very high probability, resulting in a good NS-based AIM with a small number of N, is given as

[00005]$\begin{array}{cc}\frac{M}{K}>\frac{M\left[E\left(x\right)+\delta \left(x\right)\right]}{E\left(x\right)+\delta \left(x\right)+1}& \left(9\right)\end{array}$

where E(x)=(M−1)B(1.5, M−1) and δ(x)=√{square root over (E(x.sup.2)−E(x).sup.2)} with E(x.sup.2)=(M−1)B(2, M−1). The function B(a, b) with a and b being complex-valued numbers is the beta function defined as

B(a, b)=∫.sub.0.sup.2t.sup.a−1(1−t).sup.b−1dt,{a},{b}>0.

[0033] This condition can be used to determine the maximum number of served UEs K given the number of BS antennas M for the NS-based AIM to achieve good performance and quick convergence, i.e., with small N, for ZF decoding or detection.

[0034] FIG. 3 shows the flowchart for determining the number of served UEs K, for a given M, to ensure high probability of convergence and/or quick convergence. The process begins 9 when the number of BS antennas M is selected or obtained 10. Then, the number of served UEs S is found with a sufficiently large ratio of M/S to achieve high probability of convergence or high probability of DDM so that NS converges with a small N 11. After that, the BS selects K≦S UEs to serve 12 before the process ends 13.

[0035] Although the foregoing descriptions of the preferred embodiments of the present inventions have shown, described, or illustrated the fundamental novel features or principles of the inventions, it is understood that various omissions, substitutions, and changes in the form of the detail of the methods, elements or apparatuses as illustrated, as well as the uses thereof, may be made by those skilled in the art without departing from the spirit of the present inventions. Hence, the scope of the present inventions should not be limited to the foregoing descriptions. Rather, the principles of the inventions may be applied to a wide range of methods, systems, and apparatuses, to achieve the advantages described herein and to achieve other advantages or to satisfy other objectives as well.