CN104023401A - Cluster structure-based backhaul capacity resource allocation limitation method - Google Patents

Abstract

The embodiment of the invention relates to a cluster structure-based backhaul capacity resource allocation limitation method. In a CoMP (Coordinated Multi Point Transmission/Reception) system, due to coordination, considerable data and signaling messages need to be transmitted among base stations, considerable overhead is brought to the system, the overhead is regarded as energy consumed by system backhaul, weighting system capacity is then adopted to be compared with the overhead, weighting system energy benefits are obtained, optimal resource allocation is obtained through optimizing the weighting system energy benefits, user fairness can also be ensured, and the method can be used in a next-generation long term evolution (LTE) mobile communication system. A cluster structure-based backhaul capacity resource allocation limitation method in the CoMP scene is brought forward so as to overcome defects of prior resource allocation and ensure resource allocation effectiveness and user fairness.

Description

A kind of backhaul capacity restriction resource allocation methods based on clustering architecture

Technical field

The present invention relates to wireless communication technology field, especially, the present invention is for a kind of base station multi-point cooperative (CoMP) network of high-performance processor is provided, backhaul capacity restriction resource allocation methods based on clustering architecture, comprise that power division and subcarrier distribute, can be used in evolution Long Term Evolution (LTE-A) mobile communication system of future generation.

Background technology

Along with increasing user starts with its communication equipment uploading data, this just requires mobile communication to have larger power system capacity, higher transmission rate and better service quality.But because Cell Edge User is vulnerable to serious presence of intercell interference (ICIC), there is very big-difference at center of housing estate and cell edge in the data rate that cellular system provides, has greatly affected the throughput of whole system.Therefore, Next-Generation Wireless Communication Systems (as 3GPP LTE, IEEE802.16m etc.) is all using cell-edge performance as one of main performance index.

For improving the performance of Cell Edge User, new thinking has been proposed, also adopt the technology of many base stations associated treatment to eliminate ICIC, such as the multi-point cooperative technology proposing in LTE-A (CoMP, Coordinated Multi Point Transmission/Reception).CoMP technology is by sharing some necessary information between a plurality of base stations separated on geographical position, as schedule information, channel condition information, data message etc., the collaborative data (PDSCH) that participate in a terminal are transmitted or are combined and receive the data (PUSCH) that a terminal sends.By CoMP technology, can significantly reduce presence of intercell interference, even turn waste into wealth, change and disturb as useful signal, improve on the whole cell throughout, especially edge customer throughput.

The document of allocation of research resources method is a lot of at present, but great majority are all the resource distribution in the situation of research single subdistrict, or in CoMP system, studies simple resource and distribute.We this proposition under CoMP scene, backhaul capacity based on clustering architecture restriction resource allocation methods, it as target function, can complete weighted energy benefit efficiently like this power division and subcarrier distribution and guarantee user's fairness.

Summary of the invention

The present invention is intended to distribute for existing resource not enough, proposes under a kind of CoMP scene, and the backhaul capacity restriction resource allocation methods based on clustering architecture, it guarantees the high efficiency of resource distribution and user's fairness.

The execution step of the method:

Step 1. is selected a sub-clustering, and in bunch, base station manifold is combined into U _b, and total frequency band B of each base station is divided into n subcarrier; In bunch, user's set is U _s;

Step 2. definition channel model, adopts precoding technique and considers path loss and shadow effect, in can obtaining bunch on subcarrier i, the acknowledge(ment) signal of base station b to user k.And suppose that Pc is the summation of base station to the power calculation of signaling, P _bhby each winding is consumed average power;

$Y_{i,k}=\left(\underset{b\∈U_b}{\mathrm{\Σ}}{h}_{i,k,b}{w}_{i,k,b}\sqrt{P_{i,k,b}{g}_{i,k,b}{l}_{i,k,b}}\right)u_{i,k}+\underset{b\∈U_b}{\mathrm{\Σ}}\underset{\underset{j\≠k}{j\∈S\left(i\right)}}{\mathrm{\Σ}}{h}_{i,k,b}{w}_{i,j,b}\sqrt{P_{i,j,b}{g}_{i,k,b}{l}_{i,k,b}}{u}_{i,j}+z_{i,k}$

Step 3. is calculated instant channel capacity, and then obtains weighting system capacity, thereby weighted factor is expressed as different users, provides priority to guarantee its fairness.Define the gross power of system consumption under this model, and then obtain the weighted energy efficiency of this system;

$C_{i,k}=\frac{B}{n}{\log }_2(1+{\mathrm{\Γ}}_{i,k})$

Instant channel capacity: ${\mathrm{\Γ}}_{i,k}=\frac{{\left|\underset{b\∈U_b}{\mathrm{\Σ}}{h}_{i,k,b}{w}_{i,k,b}\sqrt{P_{i,k,b}{l}_{i,k}{g}_{i,k}}\right|}^2}{{\mathrm{\σ}}_z^2+I_{i,k}}$

$I_{i,k}=\underset{\underset{j\≠k}{j\∈S\left(i\right)}}{\mathrm{\Σ}}{\left|\underset{b\∈U_b}{\mathrm{\Σ}}{h}_{i,k,b}\sqrt{l_{i,k}{g}_{i,k,}}{w}_{i,j,b}\sqrt{P_{i,j,b}}\right|}^2$

Weighting system capacity: $U(P,W,S)=\underset{b\∈U_b}{\mathrm{\Σ}}\underset{k\∈A_b}{\mathrm{\Σ}}{\mathrm{\γ}}_k\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{s}_{i,k}{C}_{i,k}$

The system gross energy consuming under this model: $U_{\mathrm{pc}}(P,W,S)=P_C+l\×P_{\mathrm{bh}}+\underset{b\∈U_b}{\mathrm{\Σ}}\underset{k\∈A_b}{\mathrm{\Σ}}\mathrm{\δ}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{p}_{i,k,b}{\left|w_{i,k,b}\right|}^2{s}_{i,k}$

Weighted energy benefit: $U_{\mathrm{EE}}=\frac{U(P,W,S)}{U_{\mathrm{pc}}(P,W,S)}$

Step 4. is in weighted energy efficiency, and we provide optimization method and qualifications;

$\underset{P,W,S}{\max }{U}_{\mathrm{EE}}(P,W,S)$

$C1:P_{i,k,b}\≥0,\∀i,k,b$

$C2:s_{i,k}=\left\{\mathrm{0,1}\right\},\∀i,k$

$C3:\underset{b\∈U_b}{\mathrm{\Σ}}{s}_{i,k}\≤1,\∀i$

$C4:\underset{k\∈A_b}{\mathrm{\Σ}}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{s}_{i,k}{C}_{i,k}\≤R_{{\max }_b}$

$C5:\underset{k\∈U_b}{\mathrm{\Σ}}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{\left|w_{i,k,b}\right|}^2{P}_{i,k,b}{s}_{i,k}\≤P_{{\max }_b}$

Step 5. is converted to target equation equivalence another objective optimization function that contains parameter q;

$\underset{P,W,S}{\max }U(P,W,S)-q{U}_{\mathrm{pc}}(P,W,S)$

s.t.C1,C2,C3,C4,C5.

Step 6. is regarded the subcarrier of discretization as the subcarrier of serialization, and optimized power is expressed as to the form that subcarrier and power multiplies each other further changes majorized function;

$\underset{P,W,S}{\max }U(P,W,S)-q{U}_{\mathrm{pc}}(P,W,S)$

s.t.C1,C3,C4

$C2:0\≤s_{i,k}\≤1,\∀i,k$

$C5:\underset{k\∈U_b}{\mathrm{\Σ}}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{\left|w_{i,k,b}\right|}^2{\tilde{P}}_{i,k,b}\≤P_{{\max }_b}$

Step 7. adopts Lagrange to solve above-mentioned target function.Target function is divided into two-layer, internal layer is tried to achieve the maximum of Lagrangian under power and subcarrier, and skin is tried to achieve the minimum value under Lagrange multiplier vector under internal layer;

$\begin{array}{c}L(\mathrm{\γ},\mathrm{\α},\mathrm{\β},P,S)=\underset{b\∈U_b}{\mathrm{\Σ}}\underset{k\∈A_b}{\mathrm{\Σ}}{\mathrm{\γ}}_k\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{s}_{i,k}{C}_{i,k}-q[P_C+l\×P_{\mathrm{bh}}+\underset{b\∈U_b}{\mathrm{\Σ}}\underset{k\∈A_b}{\mathrm{\Σ}}\mathrm{\δ}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{\left|w_{i,k,b}\right|}^2{\tilde{P}}_{i,k,b}]\\ -\underset{b\∈U_b}{\mathrm{\Σ}}{\mathrm{\λ}}_b(\underset{i=1}{\overset{n}{\mathrm{\Σ}}}\underset{k\∈A_b}{\mathrm{\Σ}}{\left|w_{i,k,b}\right|}^2{\tilde{P}}_{i,k,b}-P_{{\max }_b})-\underset{b\∈U_b}{\mathrm{\Σ}}{\mathrm{\β}}_b(\underset{k\∈A_b}{\mathrm{\Σ}}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{\left|w_{i,k,b}\right|}^2{C}_{i,k}{s}_{i,k}-R_{{\max }_b})+(\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{\α}}_i-\underset{k\∈A_b}{\mathrm{\Σ}}\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{\mathrm{\α}}_i{s}_{i.k})\end{array}$ Two-layer solution: $D=\underset{\mathrm{\λ},\mathrm{\β}\≥0}{\min }\underset{P,S}{\max }L(\mathrm{\λ},\mathrm{\α},\mathrm{\β},P,S)$

Illustrate:

In described method step 1, in same bunch, base station sends identical signal to user, can well reduce presence of intercell interference like this, and the interference signal of minizone is converted into useful signal.The total bandwidth B of each base station is divided into n subcarrier, like this can be for subsequent power is distributed and subcarrier distribution is provided convenience in each subcarrier.

In described method step 2, definition channel model, considers path loss and shadow effect; And employing precoding technique, can well reduce presence of intercell interference like this; Further define backhaul model, defined average power and Dui Wei base station, the base station summation to the power calculation of signaling that winding consumes.

In described method step 3, calculate instant channel capacity, and then obtain weighting system capacity, thereby weighted factor is expressed as different users, provide priority to guarantee its fairness.Define the gross power of system consumption under this model, and then obtain the weighted energy efficiency of this system.

In described method step 4, in weighted energy efficiency, we provide optimization method and qualifications.

In described method step 5, target equation equivalence is converted to another objective optimization function that contains parameter q, can simplifies system model like this, and be turned to optimizable model.

In described method step 6, the subcarrier of discretization is regarded as to the subcarrier of serialization, and optimized power is expressed as to the form that subcarrier and power multiplies each other and further changes majorized function, can use like this Lagrangian directly power and subcarrier to be optimized.

In described method step 7, adopt Lagrange to solve above-mentioned target function.Target function is divided into two-layer, internal layer is tried to achieve the maximum of Lagrangian under power and subcarrier, and skin is tried to achieve the minimum value under Lagrange multiplier vector under internal layer, so just can obtain the optimal value of total Lagrangian.

Below by the drawings and specific embodiments, technical method of the present invention is further described.

Accompanying drawing explanation

In order more clearly to set forth embodiments of the invention and existing technical method, the explanation accompanying drawing of using in technical method explanation accompanying drawing of the present invention and description of the Prior Art is done to simple introduction below, obviously, do not paying under the prerequisite of creative work, those of ordinary skills can obtain by this accompanying drawing other accompanying drawing.

Fig. 1 is CoMP system and clustering architecture schematic diagram in the embodiment of the present invention.

Fig. 2 is the backhaul capacity based on clustering architecture restriction resource allocation flow figure in CoMP in the embodiment of the present invention.

Embodiment

Clearer for what technical method advantage of the present invention was described, below in conjunction with accompanying drawing, the specific embodiment of the present invention is described in further detail, obvious described embodiment is part embodiment of the present invention, rather than whole embodiment.Embodiments of the invention can be expanded on this basis, in the situation that overall architecture is consistent, be obtained more optimization methods.According to embodiments of the invention, those of ordinary skill in the art can realize every other embodiment of the present invention on the basis without creative work, all belongs to protection scope of the present invention.

Main thought of the present invention is:

In CoMP system, a first selected sub-clustering, in bunch, base station manifold is combined into U _b, and total frequency band B of each base station is divided into n subcarrier; In bunch, user's set is U _s; Next defines channel model, adopts precoding technique and considers path loss and shadow effect, in can obtaining bunch on subcarrier i, the acknowledge(ment) signal of base station b to user k.And suppose that Pc is the summation of base station to the power calculation of signaling, P _bhby each winding is consumed average power; And then calculate instant channel capacity, and then obtain weighting system capacity, thereby being expressed as different users, weighted factor provide priority to guarantee its fairness.Define the gross power of system consumption under this model, and then obtain the weighted energy efficiency of this system; In weighted energy efficiency, we provide optimization method and qualifications subsequently; Then target equation equivalence is converted to another objective optimization function that contains parameter q; Again the subcarrier of discretization is regarded as to the subcarrier of serialization, and optimized power is expressed as to the form that subcarrier and power multiplies each other further changes majorized function; Finally adopt Lagrange to solve above-mentioned target function.Target function is divided into two-layer, internal layer is tried to achieve the maximum of Lagrangian under power and subcarrier, and skin is tried to achieve the minimum value under Lagrange multiplier vector under internal layer;

Fig. 1 is CoMP system and clustering architecture schematic diagram in the embodiment of the present invention.

Fig. 2 is the backhaul capacity based on clustering architecture restriction resource allocation flow figure in CoMP in the embodiment of the present invention.

Specifically describe as follows:

Step 1. is selected a sub-clustering, and in bunch, base station manifold is combined into U _b, and total frequency band B of each base station is divided into n subcarrier; In bunch, user's set is U _s;

Step 2. definition channel model, adopts precoding technique and considers path loss and shadow effect, in can obtaining bunch on subcarrier i, the acknowledge(ment) signal of base station b to user k.And suppose that Pc is the summation of base station to the power calculation of signaling, P _bhby each winding is consumed average power;

Step 3. is calculated instant channel capacity, and then obtains weighting system capacity, thereby weighted factor is expressed as different users, provides priority to guarantee its fairness.Define the gross power of system consumption under this model, and then obtain the weighted energy efficiency of this system;

Step 4. is in weighted energy efficiency, and we provide optimization method and qualifications;

Step 5. is converted to target equation equivalence another objective optimization function that contains parameter q;

Step 6. is regarded the subcarrier of discretization as the subcarrier of serialization, and optimized power is expressed as to the form that subcarrier and power multiplies each other further changes majorized function;

Step 7. adopts Lagrange to solve above-mentioned target function.Target function is divided into two-layer, internal layer is tried to achieve the maximum of Lagrangian under power and subcarrier, and skin is tried to achieve the minimum value under Lagrange multiplier vector under internal layer.

Claims (8)

1. the backhaul capacity based on clustering architecture limits a resource allocation methods, it is characterized in that comprising the following steps:

Step 1. is selected a sub-clustering, and in bunch, base station manifold is combined into U _b, and total frequency band B of each base station is divided into n subcarrier; In bunch, user's set is U _s;

Step 2. definition channel model, adopts precoding technique and considers path loss and shadow effect, in can obtaining bunch on subcarrier i, the acknowledge(ment) signal of base station b to user k.And suppose that Pc is the summation of base station to the power calculation of signaling, P _bhby each winding is consumed average power;

Step 3. is calculated instant channel capacity, and then obtains weighting system capacity, thereby weighted factor is expressed as different users, provides priority to guarantee its fairness.Define the gross power of system consumption under this model, and then obtain the weighted energy efficiency of this system;

Step 4. is in weighted energy efficiency, and we provide optimization method and qualifications;

Step 5. is converted to target equation equivalence another objective optimization function that contains parameter q;

Step 6. is regarded the subcarrier of discretization as the subcarrier of serialization, and optimized power is expressed as to the form that subcarrier and power multiplies each other further changes majorized function;

Step 7. adopts Lagrange to solve above-mentioned target function.Target function is divided into two-layer, internal layer is tried to achieve the maximum of Lagrangian under power and subcarrier, and skin is tried to achieve the minimum value under Lagrange multiplier vector under internal layer.

2. the backhaul capacity based on clustering architecture according to claim 1 limits resource allocation methods, it is characterized in that:

In described method step 1, in same bunch, base station sends identical signal to user, can well reduce presence of intercell interference like this, and the interference signal of minizone is converted into useful signal.The total bandwidth B of each base station is divided into n subcarrier, like this can be for subsequent power is distributed and subcarrier distribution is provided convenience in each subcarrier.

3. the backhaul capacity based on clustering architecture according to claim 1 limits resource allocation methods, it is characterized in that:

In described method step 2, definition channel model, considers path loss and shadow effect; And employing precoding technique, can well reduce presence of intercell interference like this; Further define backhaul model, defined average power and Dui Wei base station, the base station summation to the power calculation of signaling that winding consumes.

4. the backhaul capacity based on clustering architecture according to claim 1 limits resource allocation methods, it is characterized in that:

In described method step 3, calculate instant channel capacity, and then obtain weighting system capacity, thereby weighted factor is expressed as different users, provide priority to guarantee its fairness.Define the gross power of system consumption under this model, and then obtain the weighted energy efficiency of this system.

5. the backhaul capacity based on clustering architecture according to claim 1 limits resource allocation methods, it is characterized in that:

In described method step 4, in weighted energy efficiency, we provide optimization method and qualifications.

6. the backhaul capacity based on clustering architecture according to claim 1 limits resource allocation methods, it is characterized in that:

In described method step 5, target equation equivalence is converted to another objective optimization function that contains parameter q, can simplifies system model like this, and be turned to optimizable model.

7. the backhaul capacity based on clustering architecture according to claim 1 limits resource allocation methods, it is characterized in that:

In described method step 6, the subcarrier of discretization is regarded as to the subcarrier of serialization, and optimized power is expressed as to the form that subcarrier and power multiplies each other and further changes majorized function, can use like this Lagrangian directly power and subcarrier to be optimized.

8. the backhaul capacity based on clustering architecture according to claim 1 limits resource allocation methods, it is characterized in that:

In described method step 7, adopt Lagrange to solve above-mentioned target function.Target function is divided into two-layer, internal layer is tried to achieve the maximum of Lagrangian under power and subcarrier, and skin is tried to achieve the minimum value under Lagrange multiplier vector under internal layer, so just can obtain the optimal value of total Lagrangian.