Pilot allocation method based on coherence time for large-scale MIMO system

Abstract

Disclosed is a pilot allocation method based on coherence time for a large-scale multiple input multiple-output (MIMO) system. The present invention achieves optimal allocation of pilot resources by fully utilizing the feature that different users possibly have different moving speeds and coherence time of corresponding channels is accordingly different, thereby improving overall data transmission performance of the system and achieving certain practicability. Moreover, the present invention effectively uses limited transmission resources in the case of limited total transmission resources, thereby improving overall data transmission performance of the system and effectively reducing pilot contamination.

Description

CROSS REFERENCE TO RELATED APPLICATIONS

This application is the national phase of International Application No. PCT/CN2014/095245, filed on Dec. 29, 2014, which is based upon and claims priority to Chinese Patent Application No. 201410404568.X, filed on Aug. 15, 2014, the entire contents of which are incorporated herein by reference.

TECHNICAL FIELD OF THE INVENTION

The present invention relates to the technical field of wireless communications, and in particular, to a pilot allocation method based on coherence time for a large-scale multiple-input multiple-output (MIMO) system.

BACKGROUND OF THE INVENTION

With the ever-increasing user demand for a high-speed data service and continuous growth in the number of cell users, the mobile communications network has an ever-increasing demand for spectrum resources. Utilization of a system spectrum of a large-scale multiple-user multiple-input multiple-output (MU-MIMO) system is improved by increasing the number of base station antennas, which gains wide attention. Antennas that are far more than users in number are provided at a base station side of the large-scale MIMO system, while the cell users are provided with an individual antenna. By using the numerous antennas, the base station simultaneously serves multiple terminal users in the same time-and-frequency resource, and obtains uplink and downlink channel estimates of all the users by using a pilot transmitted on the uplink and the channel reciprocity of a Time Division Duplex (TDD) system, thereby achieving downlink precoding.

The large-scale MU-MIMO system is essentially characterized in that the number of the antennas at the base station side increases by more than one order of magnitude in comparison with that of the conventional MU-MIMO system. Compared with the conventional MU-MIMO system, it has the following specific advantages: it achieves higher multiples of capacity, higher power utilization, and higher spectrum utilization; may use a relatively cheap and low-power device; and has better link reliability.

For the conventional large-scale MIMO system, all users in a cell use orthogonal pilots, and the base station performs channel estimation by using these orthogonal pilots and the channel reciprocity of the TDD system, thereby obtaining uplink and downlink channel estimation information of all the users. However, due to coherence time and limitation on the number of users, the same orthogonal pilot sequence needs to be reused in multiple cells, so that the base station is interfered by pilot information sent by users in an intra-frequency cell when receiving uplink pilot information, thereby resulting in pilot contamination.

SUMMARY OF THE INVENTION

Technical Problem

In order to overcome defects in the prior art, the present invention provides a pilot allocation method based on coherence time for a large-scale MIMO system, which can achieve optimal allocation of pilot resources, improve overall data transmission performance of the system, and effectively reduce pilot contamination.

Technical Solution

To solve the foregoing technical problems, the present invention provides a pilot allocation method based on coherence time for a large-scale MIMO system, which includes the following steps:

step 1: grouping L cells into L_f cells formed by rapidly moving users and L_s cells formed by slowly moving users, where each cell has K randomly distributed users, each user undergoes independent channel information, the L_f cells form a set Γ_f, and the L_s cells form a set Γ_s;

step 2: calculating coherence time of each user at a carrier frequency of the system;

step 3: setting the minimum coherence time length of the users in the set Γ_f as a unit coherence time T, where T is a channel estimation interval for all the users in the set Γ_f, selecting the minimum coherence time length T_m in the set Γ_s, and setting that

$Q=\lfloor \frac{T_m}{T}\rfloor ,$ so that QT is a channel estimation interval for all the users in the set Γ_s, where the number of the unit coherence time is N_c;

step 4: estimating, by a base station, channel information of all the users within the first unit coherence time, and performing downlink data transmission according to channel estimates, to obtain a system downlink achievable rate C₁;

step 5: determining, within the nth unit coherence time, whether mod(n,Q) is equal to 1 or whether Q is equal to 1, where mod( ) represents a modulo operation; if mod(n,Q)=1 or Q=1, the users in the sets Γ_f and Γ_s update the channel estimates; or otherwise, the users only in the set Γ_f update the channel estimates; and

step 6: entering the (n+1)th unit coherence time, and repeating step 5 till the determination within the N_cth unit coherence time is done.

Further, a speed of the rapidly moving users in step 1 ranges from 35 km/h to 120 km/h, and a speed of the slowly moving users ranges from 1 km/h to 15 km/h.

Further, the number N_c of the unit coherence time in step 3 is equal to Q.

Advantageous Effect

Compared with the prior art, the present invention achieves optimal allocation of pilot resources by fully utilizing the feature that different users possibly have different moving speeds and coherence time of corresponding channels is accordingly different, thereby improving overall data transmission performance of the system and achieving certain practicability. Moreover, the present invention effectively uses limited transmission resources in the case of limited total transmission resources, thereby improving overall data transmission performance of the system and effectively reducing pilot contamination.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows comparison of downlink achievable rate versus user number between a pilot allocation method of the present invention and a conventional method without allocation.

DETAILED DESCRIPTION OF THE INVENTION

The technical solution of the present invention is further explained below with reference to the accompanying drawings.

The present invention provides a pilot allocation method based on coherence time for a large-scale MIMO system, where the solution includes the following process:

Step 1: There are L cells, each cell has one base station and K users, M represents the total number of antennas of the base station, and g_ik^j represents a channel vector from the kth user in the ith cell to the base station of the jth cell, where k=1, 2, 3 . . . K, g_ik^j=β_ik^jh_ik^j, h_ik^j represents a complex fast fading vector from the kth user terminal in the ith cell to the base station of the jth cell, h_ik^j remains unchanged within a coherence time length T_ik, T_ik represents channel coherence time of the kth user terminal in the ith cell, and β_ik^j represents a slow fading cofficient from the kth user terminal in the ith cell to the base station of the jth cell. The slow fading coefficient β_ik^j is obtained by using a long-term estimation method.

Step 2: There are L_f cells formed by rapidly moving users and L_s cells formed by slowly moving users in the L cells, the L_f cells form a set Γ_f, and the L_s cells form a set Γ_s, where L_f+L_s=L, L_f>1, and L_s>1. A unit coherence time length T is set to min{T_ik}_iεΓf,∀k, where T is a channel estimation interval for all the users in the set Γ_f. For the cells in Γ_s, a multiple of T_m=min{T_ik}_iεΓf,∀k relative to T is calculated and is rounded down, which is recorded as Q, that is,

$Q=\lfloor \frac{T_m}{T}\rfloor .$ Then, QT is a channel estimation interval for all the users in Γ_s. This solution considers that the number of the unit coherence time is N_c, and N_c is at least greater than Q.

Step 3: Within the first unit coherence time T, all the users in the L cells first perform uplink pilot transmission simultaneously, and ρ_k is used to indicate average pilot transmit power of the kth user. Then, in a channel estimation phase, a signal received by the base station of the ith cell is as follows:

$\begin{array}{cc}{Y}_{\mathrm{Bi}}=\sum _{j=1}^L\sum _{k=1}^K\sqrt{{\mathrm{\tau \rho }}_k{\beta }_{\mathrm{jk}}^i}{h}_{\mathrm{jk}}^i{\phi }_k+Z& \left(1\right)\end{array}$

where √{square root over (τ)}φ_k is a pilot signal of the kth user, φ_k is a unit orthogonal pilot sequence matrix, τ is a pilot length, τ≧K, it is set herein that τ=K, Z is additive white Gaussian noise, each element of Z conforms to CN (0, 1), β_jkⁱ represents a slow fading coefficient from the kth user terminal in the jth cell to the base station of the ith cell, and h_jkⁱ represents a complex fast fading vector from the kth user terminal in the jth cell to the base station of the ith cell. The following formula may be obtained by minimum mean square error (MMSE) estimation:

$\begin{array}{cc}{\hat{h}}_{\mathrm{ik}}^i=\frac{\sqrt{{\mathrm{\tau \rho }}_k{\beta }_{\mathrm{ik}}^i}}{1+\sum _{j=1}^L{\mathrm{\tau \rho }}_k{\beta }_{\mathrm{jk}}^i}{Y}_{\mathrm{Bi}}{\phi }_k^H.& \left(2\right)\end{array}$

A channel vector g_ik^j from the kth user terminal in the ith cell to the base station of the ith cell may be decomposed into g_ik^j=ĝ_ikⁱ+{tilde over (g)}_ikⁱ, and a channel estimation vector is ĝ_ikⁱ=√{square root over (β_ikⁱ)}ĥ_ikⁱ, where β_ikⁱ is a slow fading factor from the kth user terminal in the ith cell to the base station of the ith cell, and ĥ_ikⁱ is a fast fading estimation vector from the kth user terminal in the ith cell to the base station of the ith cell. According to the nature of MMSE estimation, ĝ_ikⁱ˜CN (0, σ_ik²I_M) and {tilde over (g)}_ikⁱ˜CN (0, ε_ik²I_M) are mutually independent channel estimation error vectors, where I_M is an M-dimensional unit matrix,

${\sigma }_{\mathrm{ik}}^2=\frac{{{\mathrm{\tau \rho }}_k\left({\beta }_{\mathrm{ik}}^i\right)}^2}{1+\sum _{j=1}^L{\mathrm{\tau \rho }}_k{\beta }_{\mathrm{jk}}^i}$ is a variance of each element of the channel estimation vector, and ε_ik²=β_ikⁱ−σ_ik² is a variance of each element of the channel estimation error vector.

Step 4: Afterwards, the base station performs downlink data transmission, and then a downlink signal y_ik received by the kth user in the ith cell is as follows:

$\begin{array}{cc}{y}_{\mathrm{ik}}=\sum _{j=1}^L\sum _{t=1}^K\sqrt{P_d}{\left(g_{\mathrm{ik}}^j\right)}^H{P}_{\mathrm{jt}}{S}_{\mathrm{jt}}+{\upsilon }_{\mathrm{ik}}& \left(3\right)\end{array}$

where s_jt is a signal to be transmitted to the tth user in the jth cell, and E[|s_jt|²]=1. The base station performs, by using channel estimation information, linear precoding on the signal to be transmitted, where P_jt is a precoding vector of the tth user in the jth cell, P_d is downlink data power, and υ_ik is a unit additive noise. It can be seen from the formula (3) above that, the downlink signal received by the kth user in the ith cell is interfered by downlink data of other users.

Step 5: A downlink achievable rate of the kth user is calculated, and it is set that a_ik^jt(g_ikⁱ)^HP_jt and α_ik^ik=(g_ik^j)^HP_ik, where a_ik^jt and a_ik^jt are temporary variables and have no specific meaning. The formula (3) is rewritten into:

$\begin{array}{cc}{y}_k=\underset{\mathrm{signal}}{\underbrace{\sqrt{P_d}E\left[a_{\mathrm{ik}}^{\mathrm{ik}}\right]S_{\mathrm{ik}}}}+\underset{\mathrm{interference}}{\underbrace{\sqrt{P_d}\left(a_{\mathrm{ik}}^{\mathrm{ik}}-E\left[a_{\mathrm{ik}}^{\mathrm{ik}}\right]\right)S_{\mathrm{ik}}+\sum _{j=1t=1}^L\sum _{\left(j,t\right)\ne \left(i,k\right)}^K\sqrt{P_d}{a}_{\mathrm{ik}}^{\mathrm{jt}}{S}_{\mathrm{jt}}}}+\underset{\mathrm{noise}}{\underbrace{v_{\mathrm{ik}}}}& \left(4\right)\end{array}$

where p_ik is a precoding vector expression of the kth user in the ith cell.

The formula (4) shows the signal, the interference, and the noise, and thus the downlink achievable rate of the kth user in the ith cell is obtained as follows:

$\begin{array}{cc}{R}_{\mathrm{ik}}={\log }_2\left(1+\frac{P_d{E}^2\left[a_{\mathrm{ik}}^{\mathrm{ik}}\right]}{P_d\mathrm{var}\left[a_{\mathrm{ik}}^{\mathrm{ik}}\right]+\sum _{j=1t=1}^L\sum _{\left(j,t\right)\ne \left(i,k\right)}^K{P}_{d}E\left[{a_{\mathrm{ik}}^{\mathrm{jt}}}^2\right]+1}\right)& \left(5\right)\end{array}$

Step 6: A system downlink achievable rate is calculated, and then a precoding vector based on MF is as follows:

$\begin{array}{cc}{p}_{\mathrm{ik}}=\frac{{\hat{g}}_{\mathrm{ik}}^i}{\sqrt{K}{\hat{g}}_{\mathrm{ik}}^i}=\frac{{\hat{g}}_{\mathrm{ik}}^i}{{\alpha }_{\mathrm{ik}}\sqrt{\mathrm{MK}}}& \left(6\right)\end{array}$

where

${\alpha }_{\mathrm{ik}}=\frac{{\hat{g}}_{\mathrm{ik}}^i}{\sqrt{M}}$ is a normalization factor, and

$\underset{M\to \infty }{\lim }{\alpha }_{\mathrm{ik}}^2=\underset{M\to \infty }{\lim }\frac{{\left({\hat{g}}_{\mathrm{ik}}^i\right)}^H{\hat{g}}_{\mathrm{ik}}^i}{M}={\sigma }_{\mathrm{ik}}^2.$ Therefore, the following formulas are obtained:

$\begin{array}{cc}E\left[a_{\mathrm{ik}}^{\mathrm{ik}}\right]=\frac{1}{{\alpha }_{\mathrm{ik}}\sqrt{\mathrm{MK}}}E\left[{\left(g_{\mathrm{jk}}^i\right)}^H{\hat{g}}_{\mathrm{ik}}^{}\right]=\sqrt{\frac{M}{K}{\sigma }_{\mathrm{ik}}}& \left(7\right)\\ \mathrm{var}\left[a_{\mathrm{ik}}^{\mathrm{ik}}\right]=E\left[{a_{\mathrm{ik}}^{\mathrm{ik}}}^2\right]-E^2\left[a_{\mathrm{ik}}^{\mathrm{ik}}\right]=\frac{{\beta }_{\mathrm{ik}}^i}{K}& \left(8\right)\end{array}$

If t≠k, the following formula is obtained:

$\begin{array}{cc}E\left[{a_{\mathrm{ik}}^{\mathrm{jt}}}^2\right]=\frac{1}{{\alpha }_{\mathrm{jt}}^2\mathrm{MK}}E\left[{{\left(g_{\mathrm{ik}}^j\right)}^H{\hat{g}}_{\mathrm{jt}}^j}^2\right]=\frac{{\beta }_{\mathrm{ik}}^j}{K}& \left(9\right)\end{array}$

If t=k, and j≠i, the following formula is obtained:

$\begin{array}{cc}E\left[{a_{\mathrm{ik}}^{\mathrm{jk}}}^2\right]=\frac{1}{a_{\mathrm{jk}}^2\mathrm{MK}}E\left[{{\left(g_{\mathrm{ik}}^j\right)}^H{\hat{g}}_{\mathrm{jk}}^j}^2\right]=\frac{{\beta }_{\mathrm{ik}}^j}{K}+\frac{M{{\mathrm{\tau \rho }}_k\left({\beta }_{\mathrm{ik}}^j\right)}^2}{\left(1+\sum _{l=1}^L\tau {\rho }_k{\beta }_{\mathrm{lk}}^j\right)K}& \left(10\right)\end{array}$

Therefore, the downlink achievable rate of the user k in the ith cell is as follows:

$\begin{array}{cc}{R}_{\mathrm{ik}}-{\log }_2\left(1+\frac{P_{d}M{\sigma }_{\mathrm{ik}}^2}{P_d\sum _{j=1}^L{\beta }_{\mathrm{ik}}^j+P_d\sum _{j=1,}^L\frac{M\tau {{\rho }_k\left({\beta }_{\mathrm{ik}}^j\right)}^2}{\left(1+\sum _{l=1}^L{\mathrm{\tau \rho }}_k{\beta }_{\mathrm{ik}}^j\right)k}+K}\right)& \left(11\right)\end{array}$

Then, when M is infinite, the system downlink achievable rate is as follows:

$\begin{array}{cc}{C}_1\underset{M\to \infty }{\lim }{R}_{\mathrm{ik}}=\frac{T-K}{T}\sum _{k=1}^K{\log }_2\left(1+\frac{{\sigma }_2^{}}{\sum _{j=1,j\ne i}^L\frac{\tau {{\rho }_k\left({\beta }_{\mathrm{ik}}^j\right)}^2}{\left(1+\sum _{l=1}^L{\mathrm{\tau \rho }}_k{\beta }_{\mathrm{lk}}^j\right)}}\right)& \left(12\right)\end{array}$

Step 7: Within the nth unit coherence time, it is determined, according to whether mod(n,Q) is equal to 1 or whether Q is equal to 1, whether pilot estimation is needed for the users in Γ_s, where n≦N_c, and mod( ) herein represents a modulo operation. If mod(n,Q)=1 or Q=1, all the users in the L cells are allocated with pilots, that is, the users in Γ_s update the channel estimates, and following the process within the first unit coherence time, calculation of the system downlink achievable rate is performed according to Step 3 to Step 6; or otherwise, the users only in Γ_f update the channel estimates, that is, it is not required to allocate pilots for the users in Γ_s and channel estimation is performed according to Step 3, provided that L in the formulas (1) and (2) is replaced with L_f. In calculating the system downlink achievable rate, the process from the formula (3) to the formula (9) is repeated. For calculation using the formula (10), two cases where iεΓ_f and iεΓ_s are taken into consideration:

$\mathrm{If}i\in {\Gamma }_f,E\left[{a_{\mathrm{ik}}^{\mathrm{jk}}}^2\right]=\frac{{\beta }_{\mathrm{ik}}^j}{K}\frac{\mathrm{M\tau }{{\rho }_k\left({\beta }_{\mathrm{ik}}^j\right)}^2}{\left(1+\sum _{l=1}^{\stackrel{\_}{L}}{\mathrm{\tau \rho }}_k{\beta }_{\mathrm{lk}}^j\right)k},\mathrm{where}$ $\stackrel{\_}{L}=\{\begin{array}{c}{L}_f,j\in {\Gamma }_f\\ L,j\in {\Gamma }_s\end{array}.$

If iεΓ_s, and when jεΓ_s,

$E\left[{a_{\mathrm{ik}}^{\mathrm{jk}}}^2\right]=\frac{{\beta }_{\mathrm{ik}}^j}{K}+\frac{\mathrm{M\tau }{{\rho }_k\left({\beta }_{\mathrm{ik}}^j\right)}^2}{\left(1+\sum _{l=1}^L{\mathrm{\tau \rho }}_k{\beta }_{\mathrm{lk}}^j\right)K};$ or otherwise,

$E\left[{a_{\mathrm{ik}}^{\mathrm{jk}}}^2\right]=\frac{{\beta }_{\mathrm{ik}}^j}{K}.$ Corresponding downlink system achievable rates may be obtained after substitution. After the determination within the nth unit coherence time is done, the process enters next unit coherence time, and Step 7 is repeated to perform the determination, till the determination within the Nth unit coherence time is done.

Step 8: the downlink achievable rates calculated within the Nc unit coherence times are added to obtain a total downlink achievable rate: C=Σ_n=1^Nc C_n.

Simulation Test 1

Parameters in a simulation scenario are as follows: it is set that, there are L=4 cells, a cell radius is 500 m, a base station is located in the center of the cell, users are evenly distributed within a cell range that is at least 35 cm away from the base station, and a large-scale fading factor model includes geometric fading with an average fading exponent γ=3.8 dB and log-normally distributed shadow fading with a standard deviation σ_shadow=8 dB, where Γ_f=L_s=2, and L_f is corresponding to a set Γ_f and L_s is corresponding to a set Γ_s. A moving speed of users in Γ_f ranges from 35 km/h to 120 km/h, and a moving speed of users in Γ_s ranges from 1 km/h to 15 km/h. Coherence time of a user having the maximum moving speed is set as unified coherence time T of all the users in Γ_f, the minimum coherence time length in Γ_s is recorded as a unit coherence time T_m, and it is set that

$Q=\lfloor \frac{T_m}{T}\rfloor .$ Then, unified coherence time of all the users in Γ_s is QT. The Monte Carlo method is used in the test, 5000 times of independent distribution of users is randomly generated for simulation, and the simulation result is an average of the 5000 times.

As shown in FIG. 1, comparison of downlink achievable rate versus user number between a pilot allocation method of the present invention and a conventional method without allocation is shown. In FIG. 1, the horizontal coordinate indicates a user number, and the vertical coordinate indicates a downlink achievable rate in bps/Hz. In the FIGURE, a solid line indicates an achievable rate curve for the pilot allocation method of the present invention, and a dotted line indicates an achievable rate curve for the conventional method without allocation. It can be seen from FIG. 1 that, the user number is from 5 to 20 in this simulation scenario, and downlink achievable rates obtained by using the pilot allocation method of the present invention are all higher than those obtained by using the conventional method without allocation. As the user number increases, a performance gain also rises.

Many variations and modifications can be made by those skilled in the art from the forgoing description according to preferred embodiments of the present invention, without departing from the scope of technical concept of the present invention. The technical scope of the present invention is not limited to the content of the specification and should be determined according to the scope of claims.

Claims

1. A pilot allocation method based on coherence time for a large-scale multiple-input multiple-output (MIMO) system, comprising the following steps: step 1: grouping L cells into Lf cells formed by a plurality of rapidly moving users and Ls cells formed by a plurality of slowly moving users, wherein each cell has K randomly distributed users, each user undergoes independent channel information, the Lf cells form a set Γf, and the Ls cells form a set Γs;step 2: calculating coherence time length of each user at a carrier frequency of the system;step 3: setting a first minimum coherence time length of the users in the set Γf as a unit coherence time T, wherein T is a channel estimation interval for each of the plurality of users in the set Γf; selecting a second minimum coherence time length Tm of the users in the set Γs, and setting that

2. The pilot allocation method according to claim 1, wherein a speed of the plurality of rapidly moving users in the step 1 ranges from 35 km/h to 120 km/h, and a speed of the plurality of slowly moving users ranges from 1 km/h to 15 km/h.

3. The pilot allocation method according to claim 1, wherein the number of the unit coherence time Nc in the step 3 is equal to Q.