CN104320850A - Large-scale MISO (Multi Input Single Output) multi-community collaborative power allocation method - Google Patents

Abstract

The invention discloses a large-scale MISO (Multi Input Single Output) multi-community collaborative power allocation method and accordingly the objective of the relevant channel based maximization of the worst signal interference noise ratio under the preset single base station power constraints in a large-scale system is implemented. The upstream and downstream transmitted duality is introduced into the optimization problem of the maximization of the worst signal interference noise ratio to obtain the corresponding optimization problem, the virtual upstream problem is introduced, the virtual upstream problem and the downstream are dual, the solution is performed on the virtual upstream transmission problem through GP optimization, the corresponding upstream optimization result is converted into a solution of the original downstream transmission problem, and accordingly the problem of the downstream multi-user power transmission and beam design is solved. According to the large-scale MISO multi-community collaborative power allocation method, the beam design and the power allocation can be obtained only through the statistical channel information and accordingly the computing complexity is low, the implementation is easy, the required feedback quantity under the large-scale system is small, and the performance of the large-scale MISO multi-community collaborative power allocation method is almost equivalent to an algorithm for obtaining the perfect channel information feedback when antennas are more.

Description

Extensive MISO multi-cell coordination power distribution method

Technical field

The invention belongs to wireless communication technology field, be specifically related to a kind of MISO multi-cell coordination power distribution method under large scale system.

Background technology

Along with the data volume demand of exponential growth and the continuous growth of number of users, traditional cellular communication system has seemed very painstaking to the high-quality service of the resources such as wireless data, and the performance of Cell Edge User receives and has a strong impact on, therefore large scale system obtains as the key technology of next generation communication system and studies widely.Meanwhile, in descending multi-user multiaerial system, utilize cells beam shaping and Poewr control method to suppress presence of intercell interference, improve community marginal user performance, become a large study hotspot of wireless communication field.The poorest Signal to Interference plus Noise Ratio problem of maximization under single base station power constraint is the problem of a non-convex, solve more difficult, the design of current beam forming and power distribution method mainly concentrates on by iteration and gets the methods such as boundary to change into the convex problem being easy to accordingly solve, and only utilizes large-scale channel information to carry out the method for solving of power division under not considering the Dual properties of up-downgoing and correlated channels model.So the feature that invention introduces up-downgoing duality simplifies problem.Large scale system is because it is when only needing less feedback information in addition, just can significantly the characteristic of capacity aspect obtain and pay close attention to widely, for this reason, the present invention devises a kind of multiple cell beam designing under large scale system and power distribution method.

Summary of the invention

The invention provides based on correlated channels model, only need large-scale channel information, effectively improve MISO (multiple input single output) the multi-cell coordination power distribution method of community marginal user performance.

Extensive MISO multi-cell coordination power distribution method provided by the invention comprises the following steps:

(1) the virtual noise variance that initialization is up

Wherein K is base station number, and each antenna for base station has M _jroot, the number of users that I serves for each base station, the numbering of user, P _jfor the transmitting power of a jth base station; for the uplink virtual noise variance of base station m;

(2) the uplink transmit power λ of the poorest Signal to Interference plus Noise Ratio optimization problem of maximization under satisfied single base station constraint is solved,

Wherein it is the virtual ascending power of user m; with for downwards and round up;

(3) by λ, and v ⁽⁰⁾, utilize the duality of up-downgoing, calculate expansion coupling matrix Q:

$Q=\left[\begin{array}{cc}\mathrm{DG}& {D1}_{\stackrel{\‾}{I}}\\ \frac{1}{\underset{m=1}{\overset{\stackrel{\‾}{I}}{\mathrm{\Σ}}}{\mathrm{\λ}}_m}{\tilde{v}}^T\mathrm{DG}& \frac{1}{\underset{m=1}{\overset{\stackrel{\‾}{I}}{\mathrm{\Σ}}}{\mathrm{\λ}}_m}{\tilde{v}}^T{D1}_{\stackrel{\‾}{I}}\end{array}\right],$

Wherein its element is the uplink virtual noise variance at each station;

$G_{m,n}=\left\{\begin{array}{cc}0& m=n\\ \frac{\frac{1}{\stackrel{\‾}{I}}\mathrm{tr}\left({\stackrel{\‾}{\mathrm{\Φ}}}_{n,n}{T}_n{\stackrel{\‾}{\mathrm{\Φ}}}_{n,m}{T}_n\right)}{\mathrm{tr}\left({\stackrel{\‾}{\mathrm{\Φ}}}_{n,n}{T}_n^{\′}\right){(1+{\mathrm{\λ}}_m\frac{1}{\stackrel{\‾}{I}}\mathrm{tr}\left({\stackrel{\‾}{\mathrm{\Φ}}}_{n,m}{T}_n\right))}^2}& m\≠n\end{array}\right.$

$D_{m,n}=\left\{\begin{array}{cc}\frac{{\stackrel{\‾}{\mathrm{\γ}}}_m\mathrm{tr}\left({\stackrel{\‾}{\mathrm{\Φ}}}_{m,m}{T}_m^{\′}\right)}{\frac{1}{\stackrel{\‾}{I}}{\left(\mathrm{tr}\left({\stackrel{\‾}{\mathrm{\Φ}}}_{m,m}{T}_m\right)\right)}^2}& m=n\\ 0& m\≠n\end{array}\right.$

represent T _mderivative,

represent δ _nderivative,

Solve the characteristic vector corresponding to expansion coupling matrix Q eigenvalue of maximum, and this characteristic vector is done normalization, before obtaining to last element individual element is exactly required descending power p;

Wherein

represent the downlink transmission power of user m;

represent the noise of user m;

it is the virtual up Signal to Interference plus Noise Ratio of user m;

D _n,mfor base station with in individual base station large scale decline between individual user; R _n,mfor base station

with in individual base station channel covariance matrices between individual user;

for the antenna number of base station n; z _n,mfor base station with in individual base station multipath fading between individual user;

for base station with in individual base station channel coefficients between individual user;

(4) up virtual noise variance v is optimized by subgradient algorithm ^(*): v ^(*)=v ⁽⁰⁾-ζ g, if | v ^(*)-v ⁽⁰⁾| > δ, then return step (2), otherwise iteration stopping;

Wherein ζ is for upgrading step-length, and g is the subgradient of v: δ is the threshold value preset;

(5) by λ, and v, compute beam vector

Wherein it is base station to user beam vector;

In the step (2) of said method, solving virtual ascending power method comprises the steps:

1. initialization ascending power meet

2. ascending power λ is upgraded: ${\mathrm{\λ}}_m=\frac{1}{{\stackrel{\←}{\mathrm{\γ}}}_m(v^{\left(0\right)},{\mathrm{\λ}}^{\left(0\right)})}{\mathrm{\λ}}_m^{\left(0\right)}\∀m$

3. normalization uplink transmit power λ:

If 4. | λ-λ ⁽⁰⁾| 2. > ε, then return step, otherwise stop.

The object of the inventive method application is multi-BS (base station) multi-user communication system, and each base station comprises I user, and there is M each base station _jtransmit antennas.

The inventive method with compared based on the power distribution method of correlated channels model in the past, computation complexity is low, feedback quantity is little, only need large-scale channel information, do not need transient channel information and renewable downstream transmission power, and increasing along with antenna number, its performance is more and more close to the algorithm of perfect channel information.

Accompanying drawing explanation

Fig. 1 is the system model of the inventive method;

Fig. 2 is extensive MISO multi-cell coordination power distribution method flow chart;

Fig. 3 is the user average Signal to Interference plus Noise Ratio of algorithms of different under different single base station power constraints.

Fig. 4 is the average Signal to Interference plus Noise Ratio of the user of algorithms of different under different antennae number.

Embodiment

The present invention based on basic theory illustrate: the multi-user downlink system (system model is as shown in Figure 1) retrained for single base station power, to maximize the poorest Signal to Interference plus Noise Ratio for optimization aim,

Descending Signal to Interference plus Noise Ratio is defined as follows:

${\stackrel{\‾}{\mathrm{\γ}}}_m=\frac{p_m{\left|\right|{\stackrel{\‾}{h}}_{m,m}^H{w}_m\left|\right|}^2}{\underset{n\≠m}{\mathrm{\Σ}}{p}_n{\left|\right|{\stackrel{\‾}{h}}_{n,m}^H{w}_n\left|\right|}^2+1}\text{---}\left(1\right)$

Wherein therefore corresponding optimization aim is defined as follows:

$\left\{\begin{array}{c}\underset{\{w_m,p_m\}}{\max }\underset{m}{\min }{\stackrel{\→}{\mathrm{\γ}}}_m\\ s.t.\underset{m=(j-1)I+1}{\overset{\mathrm{jI}}{\mathrm{\Σ}}}{p}_m\≤P_j,p_m\≥0,\left|\right|w_m\left|\right|=1\end{array}\right.---\left(2\right)$

Utilize mark optimization, introduce up virtual noise variance after, when it is given, above optimization problem is transformed:

Utilize the duality of up-downgoing, above-mentioned optimization problem can be converted into following virtual up optimization problem:

$\left\{\begin{array}{c}\underset{\{w_m,p_m\}}{\max }\underset{m}{\min }{\stackrel{\→}{\mathrm{\γ}}}_m\\ s.t.\underset{m=(j-1)I+1}{\overset{\mathrm{jI}}{\mathrm{\Σ}}}{p}_m\≤P_j,p_m\≥0,\left|\right|w_m\left|\right|=1\end{array}\right.---\left(2\right)$

Large-scale channel information can be only utilized to carry out approximate up Signal to Interference plus Noise Ratio in large scale system, approximate up Signal to Interference plus Noise Ratio of deriving below.

Assuming that for the matrix of spectral norm bounded, then c is a constant all had nothing to do with U, M, then can obtain further when p>=2,

According to above-mentioned character, if supposition the separate random matrix of each column vector, and then for any z > 0, have

wherein Σ (z)=(HH ^h-zI _m) ^-1,

$T\left(z\right)={(\frac{1}{\stackrel{\‾}{I}}\underset{n=1}{\overset{\stackrel{\‾}{I}}{\mathrm{\Σ}}}\frac{{\mathrm{\Φ}}_{m,n}}{1+{\mathrm{\δ}}_n\left(z\right)}-z{I}_M)}^{-1},{\mathrm{\δ}}_n\left(z\right)=\frac{1}{\stackrel{\‾}{I}}\mathrm{tr}\left({\mathrm{\Φ}}_{m,n}{(\frac{1}{\stackrel{\‾}{I}}\underset{k=1}{\overset{\stackrel{\‾}{I}}{\mathrm{\Σ}}}\frac{{\mathrm{\Φ}}_{m,k}}{1+{\mathrm{\δ}}_k\left(z\right)}-z{I}_M)}^{-1}\right).$

Can by virtual up Signal to Interference plus Noise Ratio according to above-mentioned derivation carry out following conversion:

Wherein

Virtual ascending power after can being optimized by iteration

Utilize the duality of up-downgoing, up-downgoing Signal to Interference plus Noise Ratio is equal, then it can be expressed as:

Therefore can obtain the conversion method of descending power, design and the expansion coupling matrix Q be calculated as follows:

$Q=\left[\begin{array}{cc}\mathrm{DG}& {D1}_{\stackrel{\‾}{I}}\\ \frac{1}{\underset{m=1}{\overset{\stackrel{\‾}{I}}{\mathrm{\Σ}}}{\mathrm{\λ}}_m}{\tilde{v}}^T\mathrm{DG}& \frac{1}{\underset{m=1}{\overset{\stackrel{\‾}{I}}{\mathrm{\Σ}}}{\mathrm{\λ}}_m}{\tilde{v}}^T{D1}_{\stackrel{\‾}{I}}\end{array}\right]$

Wherein

$G_{m,n}=\left\{\begin{array}{cc}0& m=n\\ {\left|\right|{\stackrel{\‾}{h}}_{n,m}^H{w}_n\left|\right|}^2& m\≠n\end{array}\right.---\left(6\right)$

$D_{m,n}=\left\{\begin{array}{cc}\frac{{\stackrel{\←}{\mathrm{\γ}}}_m}{{\left|\right|{\stackrel{\‾}{h}}_{m,m}^H{w}_m\left|\right|}^2}& m=n\\ 0& m\≠n\end{array}\right.$

Thus the characteristic vector of expansion coupling matrix Q can be utilized, obtain final power allocation scheme.

Due to a large amount of antennas and user in extensive MISO system, amount of channel feedback is increased, even cannot accept, this just requires that descending power allocation scheme also only adopts large-scale channel information, and therefore in Q matrix, the form of G, D also will change to some extent.

Derivation can be carried out known to the parameter on the equal equation both sides of up-downgoing Signal to Interference plus Noise Ratio:

Wherein T' _mfor T _mderivative, δ ' _nδ _nderivative, and

And the related definition of distracter is as follows:

Wherein

Therefore according to the duality of up-downgoing, G, the D that can be expanded in coupling matrix is defined as follows:

$G_{m,n}=\left\{\begin{array}{cc}0& m=n\\ \frac{\frac{1}{\stackrel{\‾}{I}}\mathrm{tr}\left({\stackrel{\‾}{\mathrm{\Φ}}}_{n,n}{T}_n{\stackrel{\‾}{\mathrm{\Φ}}}_{n,m}{T}_n\right)}{\mathrm{tr}\left({\stackrel{\‾}{\mathrm{\Φ}}}_{n,n}{T}_n^{\′}\right){(1+{\mathrm{\λ}}_m\frac{1}{\stackrel{\‾}{I}}\mathrm{tr}\left({\stackrel{\‾}{\mathrm{\Φ}}}_{n,m}{T}_n\right))}^2}& m\≠n\end{array}\right.$

$D_{m,n}=\left\{\begin{array}{cc}\frac{{\stackrel{\‾}{\mathrm{\γ}}}_m\mathrm{tr}\left({\stackrel{\‾}{\mathrm{\Φ}}}_{m,m}{T}_m^{\′}\right)}{\frac{1}{\stackrel{\‾}{I}}{\left(\mathrm{tr}\left({\stackrel{\‾}{\mathrm{\Φ}}}_{m,m}{T}_m\right)\right)}^2}& m=n\\ 0& m\≠n\end{array}\right.$

By solving the characteristic vector of expansion coupling matrix and after being normalized, the numerical value of descending power can being obtained.

By solving the poorest Signal to Interference plus Noise Ratio optimization problem of maximization, obtain now beam designing and follow MMSE criterion, the optimal receiver beam designing under MMSE receiving filter is as follows:

Based on above-mentioned theory, the extensive MISO multi-cell coordination power distribution method that the present invention considers, as shown in Figure 2, the method comprises the following steps:

(1) the virtual noise variance that initialization is up

Wherein K is base station number, and each antenna for base station has M _jroot, the number of users that I serves for each base station, the numbering of user, P _jfor the transmitting power of a jth base station; for the uplink virtual noise variance of base station m;

(2) the uplink transmit power λ of the poorest Signal to Interference plus Noise Ratio optimization problem of maximization under satisfied single base station constraint is solved,

Wherein it is the virtual ascending power of user m; with for downwards and round up;

(3) by λ, and v ⁽⁰⁾, utilize the duality of up-downgoing, calculate expansion coupling matrix Q:

$Q=\left[\begin{array}{cc}\mathrm{DG}& {D1}_{\stackrel{\‾}{I}}\\ \frac{1}{\underset{m=1}{\overset{\stackrel{\‾}{I}}{\mathrm{\Σ}}}{\mathrm{\λ}}_m}{\tilde{v}}^T\mathrm{DG}& \frac{1}{\underset{m=1}{\overset{\stackrel{\‾}{I}}{\mathrm{\Σ}}}{\mathrm{\λ}}_m}{\tilde{v}}^T{D1}_{\stackrel{\‾}{I}}\end{array}\right],$

Wherein its element is the uplink virtual noise variance at each station, the number of users that I serves for each base station;

$G_{m,n}=\left\{\begin{array}{cc}0& m=n\\ \frac{\frac{1}{\stackrel{\‾}{I}}\mathrm{tr}\left({\stackrel{\‾}{\mathrm{\Φ}}}_{n,n}{T}_n{\stackrel{\‾}{\mathrm{\Φ}}}_{n,m}{T}_n\right)}{\mathrm{tr}\left({\stackrel{\‾}{\mathrm{\Φ}}}_{n,n}{T}_n^{\′}\right){(1+{\mathrm{\λ}}_m\frac{1}{\stackrel{\‾}{I}}\mathrm{tr}\left({\stackrel{\‾}{\mathrm{\Φ}}}_{n,m}{T}_n\right))}^2}& m\≠n\end{array}\right.$

$D_{m,n}=\left\{\begin{array}{cc}\frac{{\stackrel{\‾}{\mathrm{\γ}}}_m\mathrm{tr}\left({\stackrel{\‾}{\mathrm{\Φ}}}_{m,m}{T}_m^{\′}\right)}{\frac{1}{\stackrel{\‾}{I}}{\left(\mathrm{tr}\left({\stackrel{\‾}{\mathrm{\Φ}}}_{m,m}{T}_m\right)\right)}^2}& m=n\\ 0& m\≠n\end{array}\right.$

represent T _mderivative,

represent δ _nderivative,

Solve the characteristic vector corresponding to expansion coupling matrix Q eigenvalue of maximum, and this characteristic vector is done normalization, before obtaining to last element individual element is exactly required descending power p;

Wherein

represent the downlink transmission power of user m;

represent the noise of user m;

it is the virtual up Signal to Interference plus Noise Ratio of user m;

D _n,mfor base station with in individual base station large scale decline between individual user; R _n,mfor base station

with in individual base station channel covariance matrices between individual user;

for the antenna number of base station n; z _n,mfor base station with in individual base station multipath fading between individual user;

for base station with in individual base station channel coefficients between individual user;

(4) up virtual noise variance v is optimized by subgradient algorithm ^(*): v ^(*)=v ⁽⁰⁾-ζ g, if | v ^(*)-v ⁽⁰⁾| > δ, then return step (2), otherwise iteration stopping;

Wherein ζ is for upgrading step-length, and g is the subgradient of v: δ is the threshold value preset;

(5) by λ, and v, compute beam vector

Wherein it is base station to user beam vector;

In the step (2) of said method, solving virtual ascending power method comprises the steps:

1. initialization ascending power meet

2. ascending power λ is upgraded: ${\mathrm{\λ}}_m=\frac{1}{{\stackrel{\←}{\mathrm{\γ}}}_m(v^{\left(0\right)},{\mathrm{\λ}}^{\left(0\right)})}{\mathrm{\λ}}_m^{\left(0\right)}\∀m$

3. normalization uplink transmit power λ:

If 4. | λ-λ ⁽⁰⁾| 2. > ε, then return step, otherwise stop.

Below the performance comparison of the inventive method and additive method is made an explanation:

In analogous diagram below, asymptotically optimal beamformer represents the algorithm that the present invention carries, and optimal beamformer represents that the duality that utilizes known under perfect channel information maximizes the algorithm of the poorest Signal to Interference plus Noise Ratio, MRT represents the user's Signal to Interference plus Noise Ratio sent according to high specific under wave beam.Fig. 3 considers 3 communities, each base station is furnished with 64 antennas, when each cell serves 4 users, carry the behavior pattern of algorithm under different single base station power constraint, as can be seen from Figure 3: the algorithm that the present invention carries is compared with optimal beamformer, gap in performance is little, and the Signal to Interference plus Noise Ratio loss of average each user is very little, and is better than the poorest Signal to Interference plus Noise Ratio that MRT obtains under constant power.And when obtaining similar performance, the algorithm that the present invention carries only needing large-scale channel information to realize the distribution of descending power, amount of calculation and feedback being obtained for and greatly simplifying, having fully demonstrated the advantage of extensive antenna system.Fig. 4 also considers 3 communities under being reflected in different antenna number, each cell serves 4 users, single base station power is constrained to the situation of 46dBm, the increase along with antenna number of algorithm that the present invention proposes can be found from figure, the present invention put forward performance that algorithm obtains more and more close to optimal beamformer, and gap therebetween tends towards stability gradually.

Claims (2)

1. an extensive MISO multi-cell coordination power distribution method, it is characterized in that, the method comprises the following steps:

(1) the virtual noise variance that initialization is up

Wherein K is base station number, and each antenna for base station has M _jroot, the number of users that I serves for each base station,

The numbering of user, P _jfor the transmitting power of a jth base station; for the uplink virtual noise variance of base station m;

(2) the uplink transmit power λ of the poorest Signal to Interference plus Noise Ratio optimization problem of maximization under satisfied single base station constraint is solved, wherein it is the virtual ascending power of user m; with for downwards and round up;

(3) by λ, and v ⁽⁰⁾, utilize the duality of up-downgoing, calculate expansion coupling matrix Q:

$Q=\left[\begin{array}{cc}\mathrm{DG}& {D1}_{\stackrel{\‾}{I}}\\ \frac{1}{\underset{m=1}{\overset{\stackrel{\‾}{I}}{\mathrm{\Σ}}}{\mathrm{\λ}}_m}{\tilde{v}}^T\mathrm{DG}& \frac{1}{\underset{m=1}{\overset{\stackrel{\‾}{I}}{\mathrm{\Σ}}}{\mathrm{\λ}}_m}{\tilde{v}}^T{D1}_{\stackrel{\‾}{I}}\end{array}\right],$

Wherein its element is the uplink virtual noise variance at each station;

$G_{m,n}=\left\{\begin{array}{cc}0& m=n\\ \frac{\frac{1}{\stackrel{\‾}{I}}\mathrm{tr}\left({\stackrel{\‾}{\mathrm{\Φ}}}_{n,n}{T}_n{\stackrel{\‾}{\mathrm{\Φ}}}_{n,m}{T}_n\right)}{\mathrm{tr}\left({\stackrel{\‾}{\mathrm{\Φ}}}_{n,n}{T}_n^{\′}\right){(1+{\mathrm{\λ}}_m\frac{1}{\stackrel{\‾}{I}}\mathrm{tr}\left({\stackrel{\‾}{\mathrm{\Φ}}}_{n,m}{T}_n\right))}^2}& m\≠n\end{array}\right.$

represent T _mderivative,

represent δ _nderivative,

Solve the characteristic vector corresponding to expansion coupling matrix Q eigenvalue of maximum, and this characteristic vector is done normalization, before obtaining to last element individual element is exactly required descending power p;

Wherein

represent the downlink transmission power of user m;

represent the noise of user m;

it is the virtual up Signal to Interference plus Noise Ratio of user m;

D _n,mfor base station with in individual base station large scale decline between individual user; R _n,mfor base station

with in individual base station channel covariance matrices between individual user;

for the antenna number of base station n; z _n,mfor base station with in individual base station multipath fading between individual user;

for base station with in individual base station channel coefficients between individual user;

(4) up virtual noise variance v is optimized by subgradient algorithm ^(*): v ^(*)=v ⁽⁰⁾-ζ g, if | v ^(*)-v ⁽⁰⁾| > δ, then return step (2), otherwise iteration stopping;

Wherein ζ is for upgrading step-length, and g is the subgradient of v: δ is the threshold value preset;

(5) by λ, and v, compute beam vector

Wherein it is base station to user beam vector.

2. extensive MISO multi-cell coordination power distribution method according to claim 1, is characterized in that, in the step (2) of said method, solving virtual ascending power method comprises the steps:

1. initialization ascending power meet

2. ascending power λ is upgraded:

3. normalization uplink transmit power λ:

If 4. | λ-λ ⁽⁰⁾| 2. > ε, then return step, otherwise stop.