CN105873216A - Resource allocation method for jointly optimizing energy efficiency and spectral efficiency by heterogeneous network multipoint collaboration - Google Patents

Abstract

The invention discloses a resource allocation method for jointly optimizing energy efficiency and spectral efficiency by heterogeneous network multipoint collaboration. Firstly, a joint optimization problem of the energy efficiency and the spectral efficiency is defined; then, under the given constraint conditions of the resource block and the power, the resource block, the power and allocation of the resource block and the power among the users are optimized, so that joint optimization of the energy efficiency and the spectral efficiency is realized; wherein the method for optimizing the resource block, the power and allocation of the resource block and the power among the users comprises the steps that the relationship between the user logarithmic speed and the power is simplified with a duality method, and resource allocation is performed to a CoMP user and a non-CoMP user with a greedy algorithm thought respectively. An effective and rapid power allocation method for jointly optimizing the energy efficiency and the spectral efficiency is provided, and the complexity caused by a violence solution method is avoided.

Description

The resource allocation methods of heterogeneous network multipoint cooperative efficiency spectrum effect combined optimization

Technical field

The present invention relates to efficiency spectrum effect combined optimization problem in GSM, belong to the network technology in radio communication Field.

Background technology

Along with the portable mobile terminal such as smart mobile phone and panel computer comes into vogue and gradually popularizes, wireless data service amount Swift and violent growth.But, the raising speed of present spectrum efficiency of communication system does not catches up with the speed that demand data increases, this problem The hot spot region intensive at flow of the people is the severeest.

The transmittability of communication network is had higher requirement by wireless communication system, the most limited for frequency spectrum resource GSM, need to promote the message transmission rate in unit transmission bandwidth, i.e. improve system spectrum effect (SE, Spectral Efficiency).Coordinated multipoint transmission technology (Coordinated multipoint Processing, CoMP) With heterogeneous network technology (heterogeneous net, HetNet) as the future wireless network received much concern improves frequency spectrum effect Rate and the technology of coverage rate, can support that the heterogeneous network of CoMP becomes study hotspot.Owing to wireless communications environment is complicated, user There is much difference in data rate, the fairness between user is of concern.

Along with energy resource consumption and the concern of environmental problem, the energy consumption problem of mobile communications network are the most increasingly caused by people The concern of numerous researchers.Therefore, the efficiency (EE, Energy Efficiency) of GSM how is improved, it has also become One study hotspot of current mobile communications research field, the efficiency of system also becomes to be weighed the important performance of communication network and refers to One of mark.It is but efficiency and spectrum effect both Measure Indexes are the most consistent changes, the most mutually contradictory, One of them index that covets will cause the dramatic decrease of another index.Then, balance between two kinds of indexs and compromise, from And realize combined optimization and become a hot issue.

Summary of the invention

It is an object of the invention to provide the resource allocation methods of a kind of heterogeneous network multipoint cooperative efficiency spectrum effect combined optimization, with Efficiency and spectrum to GSM are imitated and are balanced between two kinds of indexs and trade off, excellent to realize efficiency spectrum effect reasonably associating Change.

For achieving the above object, the technical solution used in the present invention is:

A kind of resource allocation methods of heterogeneous network multipoint cooperative efficiency spectrum effect combined optimization,

First, the combined optimization problem of definition efficiency and spectrum effect is:

$\underset{p,\mathrm{\ρ}}{max}\left({\mathrm{\β}}_1\underset{k=1}{\overset{K}{\Σ}}{\mathrm{lnR}}_k-{\mathrm{\β}}_2{P}_{total}\right)$

$\begin{array}{cc}s.t.& C1:\underset{m=1}{\overset{M}{\Σ}}\underset{k=1}{\overset{K_M}{\Σ}}{\mathrm{\ρ}}_{m,k}{p}_{m,k}\≤P_{TM},\end{array}$

$C2:\underset{m=1}{\overset{M}{\Σ}}\underset{k=1}{\overset{K_P}{\Σ}}{\mathrm{\ρ}}_{m,k}{p}_{m,k}\≤P_{TP},$

$C3:p_{m,k}\≥0,\∀m,\∀k,$

$C4:\underset{m=1}{\overset{M}{\Σ}}{\mathrm{\ρ}}_{m,k}\≥1,\∀k\∈K_M,$

$C5:\underset{m=1}{\overset{M}{\Σ}}{\mathrm{\ρ}}_{m,k}\≥1,\∀k\∈K_P,$

$C6:{\mathrm{\ρ}}_{m,k}\∈\{0,1\},\∀m,\∀k.$

Wherein, R_kAnd P_totalRepresent data rate and total system power consumption, the β of user k respectively₁With β₂Represent weight coefficient, β₁ With β₂Value is decided in its sole discretion according to network operation situation by operator；p_m,kRepresent that user k receives the signal power of Resource Block m, ρ_m,kRepresent the Resource Block m distribution condition to user k, work as ρ_m,k=0 represents that Resource Block is not yet assigned to user k, works as ρ_m,k=1 represents Resource Block is assigned to user k；P=(p_1,1,p_1,2,p_1,3,...,p_M,K), ρ=(ρ_1,1,ρ_1,2,ρ_1,3,...,ρ_M,K) represent respectively Power and the distribution of Resource Block；P_TMAnd P_TPRepresent macro base station and small station maximum transmission power, K that operator allows respectively_MWith K_PRepresent user's set of macro base station and small station service, K respectively_MWith K_PRepresent macro base station and the number of users of small station service respectively, K and M represents the total number of users of system and base station resource block number respectively；

Then, under conditions of Resource Block given herein above and power constraint, to Resource Block and power and each user Between distribution be optimized, thus realize efficiency and spectrum effect combined optimization；Wherein, to Resource Block and power and each user Between the method that is optimized of distribution be: simplify the relation between user's logarithm speed and power by Dual Method, re-use Greedy algorithm thought carries out resource distribution to CoMP user and non-CoMP user respectively.

To concretely comprising the following steps that Resource Block and power and the distribution between each user thereof are optimized:

Step one, finds user rate lower bound, approximates user rate:

Utilize Dual Method by problem reduction, resource allocation problem be converted into:

$\underset{p,\mathrm{\ρ},t}{max}{\mathrm{\β}}_1\underset{k=1}{\overset{K}{\Σ}}{\mathrm{lnt}}_k-{\mathrm{\β}}_2{P}_{total}$

s.t.C1,...,C6,

$C7:t_k\≤R_k,\∀k.$

Wherein, t_kFor user k data rate R_kLower bound, ensure t by C7_kWith R_kRelation, t_kCost function is used Carry out approximated user speed；Owing to logarithmic function is monotonically increasing function, cost function is lower bound t=(t₁,t₂,t₃,...,t_K) Increasing function, when cost function reaches maximum, each element in lower bound t also by the data rate equal to each user, because of Problem after this conversion has identical optimal solution with former problem；

Being arranged by above formula and solve for lagrange problem, Lagrangian is:

$L(p,\mathrm{\ρ},t,\mathrm{\λ})={\mathrm{\β}}_1\underset{k=1}{\overset{K}{\Σ}}\ln t_k-{\mathrm{\β}}_2{P}_{total}+\underset{k=1}{\overset{K}{\Σ}}{\mathrm{\λ}}_k(R_k-t_k)$

s.t.C1,...,C6.

Wherein, λ=(λ₁₁,λ₁₂,λ₁₃,...,λ_KM) it is Lagrange multiplier,Lagrange problem is decomposed For dual problemAnd dual equation:

$g\left(\mathrm{\λ}\right)=\underset{p,\mathrm{\ρ},t}{max}L(p,\mathrm{\ρ},t,\mathrm{\λ})$

s.t.C1,...,C6.

In dual problem, use gradient descent method renewal Lagrange multiplier:

λ_i=max [0, λ_i+s₁(R_k-t_k)]

Wherein, s₁For updating step-length；

For user k, Lagrangian carried out derivation and make it be equal to zero, obtaining:

$t_k^{*}=\frac{{\mathrm{\β}}_1}{{\mathrm{\λ}}_k}$

Step 2: use greedy algorithm distributing user resource:

Dual equation in step one is reduced to:

$\underset{p,\mathrm{\ρ}}{max}\underset{k=1}{\overset{K}{\Σ}}{\mathrm{\λ}}_k{R}_k-{\mathrm{\β}}_2({\mathrm{\ξ}}_1\underset{k=1}{\overset{K_M}{\Σ}}{p}_k^M+P_C^M)-{\mathrm{\β}}_2\underset{R}{\Σ}({\mathrm{\ξ}}_2\underset{k=1}{\overset{K_P}{\Σ}}{p}_k^P+P_C^P)$

s.t.C1,...,C6

Wherein, ξ₁And ξ₂Represent that the slope between power and power consumption is launched in macro base station and small station respectively,WithPoint Not Biao Shi the power that is assigned at macro base station and small station of user k,WithRepresent macro base station and the circuit power in small station, R respectively Represent small station number；Owing to the circuit loss of macro base station, small station is constant value in a given system, problem can be written as:

$\underset{p,\mathrm{\ρ}}{max}\underset{k=1}{\overset{K}{\Σ}}{\mathrm{\λ}}_k{R}_k-{\mathrm{\β}}_2\left({\mathrm{\ξ}}_1\underset{k=1}{\overset{K_M}{\Σ}}{p}_k^M\right)-{\mathrm{\β}}_2\underset{R}{\Σ}\left({\mathrm{\ξ}}_2\underset{k=1}{\overset{K_P}{\Σ}}{p}_k^P\right)$

s.t.C1,...,C6

The restriction launching power is added Lagrangian by macro base station, small station by the method for application Lagrange duality, Arrangement obtains:

$\begin{array}{c}Q\left(p,\mathrm{\ρ},\mathrm{\μ}\right)=\underset{k=1}{\overset{K}{\Σ}}{\mathrm{\λ}}_k{R}_k-{\mathrm{\β}}_2\mathrm{\ξ}\underset{k=1}{\overset{K_M}{\Σ}}{p}_k^M+{\mathrm{\μ}}_M\left(P_{TM}-\underset{m=1}{\overset{M}{\Σ}}\underset{k=1}{\overset{K_M}{\Σ}}{\mathrm{\ρ}}_{m,k}{p}_{m,k}\right)\\ +\underset{R}{\Σ}\left(-{\mathrm{\β}}_2{\mathrm{\ξ}}_2\underset{k=1}{\overset{K_P}{\Σ}}{p}_k^P+{\mathrm{\μ}}_P\left(P_{TP}-\underset{m=1}{\overset{M}{\Σ}}\underset{k=1}{\overset{K_P}{\Σ}}{\mathrm{\ρ}}_{m,k}{p}_{m,k}\right)\right)\\ =\underset{m=1}{\overset{M1}{\Σ}}\left(\underset{k=1}{\overset{K_{M1}}{\Σ}}\left({\mathrm{\λ}}_{mk}{r}_{mk}-\left({\mathrm{\β}}_2{\mathrm{\ξ}}_1+{\mathrm{\μ}}_M\right)p_{m,k}\right){\mathrm{\ρ}}_{m,k}\right)+{\mathrm{\μ}}_M{P}_{TM}\\ +\underset{R}{\Σ}\left(\underset{k=1}{\overset{M1}{\Σ}}\left(\underset{k=1}{\overset{K_{P1}}{\Σ}}\left({\mathrm{\λ}}_{mk}{r}_{mk}-\left({\mathrm{\β}}_2{\mathrm{\ξ}}_2+{\mathrm{\μ}}_P\right)p_{m,k}\right){\mathrm{\ρ}}_{m,k}\right)+{\mathrm{\μ}}_P{P}_{TP}\right)\\ +\underset{m=1}{\overset{M2}{\Σ}}\left(\underset{k=1}{\overset{K_C}{\Σ}}\left({\mathrm{\λ}}_{mk}{r}_{mk}-\left({\mathrm{\β}}_2\mathrm{\ξ}+\mathrm{\μ}\right)p_{m,k}\right){\mathrm{\ρ}}_{m,k}\right)\end{array}$

s.t.C1,...,C3,C6

Wherein, r_mkRepresent user k data rate on Resource Block m, K_M1、K_P1And K_CRepresent macro base station service respectively Non-CoMP user, the non-CoMP user of small station service and the CoMP user number of system, M1 and M2 represents non-CoMP and CoMP respectively User's available resource block number, μ=(μ_M,μ_P1,...,μ_PR) it is Lagrange multiplier；

Lagrange problem is decomposed into dual problem by the method using Lagrange dualityAnd dual equation:

$h\left(\mathrm{\μ}\right)=\underset{p,\mathrm{\ρ}}{\max }Q(p,\mathrm{\ρ},\mathrm{\μ})$

s.t.C1,...,C3,C6.

In dual problem, gradient descent method is used to update Lagrange multiplier, as a example by macro base station:

${\mathrm{\μ}}_M=max\[0,{\mathrm{\μ}}_M+s_2\left(\underset{m=1}{\overset{M}{\Σ}}\underset{k=1}{\overset{K_M}{\Σ}}{\mathrm{\ρ}}_{m,k}{p}_{m,k}-P_{TM}\right)\]$

Wherein, s₂For updating step-length；

For non-CoMP user, part relevant in Lagrangian Q (p, ρ, μ) is arranged:

$\begin{array}{c}q(\mathrm{\ρ},p)=\underset{m=1}{\overset{M1}{\Σ}}\left(\underset{k=1}{\overset{K_{M1}}{\Σ}}\left({\mathrm{\λ}}_{mk}{r}_{mk}-\left({\mathrm{\β}}_2{\mathrm{\ξ}}_1+{\mathrm{\μ}}_M\right)p_{m,k}\right){\mathrm{\ρ}}_{m,k}\right)\\ +\underset{R}{\Σ}\[\underset{m=1}{\overset{M1}{\Σ}}\left(\underset{k=1}{\overset{K_{P1}}{\Σ}}\left({\mathrm{\λ}}_{mk}{r}_{mk}-\left({\mathrm{\β}}_2{\mathrm{\ξ}}_2+{\mathrm{\μ}}_P\right)p_{m,k}\right){\mathrm{\ρ}}_{m,k}\right)\]\end{array}$

Above formula shows not have between Resource Block coupled relation；As a example by the non-CoMP user of macro base station, each money of base station Source block distributes to the non-CoMP user making following formula reach maximum:

$F(\mathrm{\ρ},p)=\underset{k=1}{\overset{K_{M1}}{\Σ}}({\mathrm{\λ}}_{mk}{r}_{mk}-({\mathrm{\β}}_2{\mathrm{\ξ}}_1+{\mathrm{\μ}}_M\left)p_{m,k}\right){\mathrm{\ρ}}_{m,k}$

Each user is first calculated to the optimum transmitting power on Resource Block m, obtain optimum transmitting power by derivation And most preferably subcarrier distribution

$p_{mk}^{*}=max\left(0,\frac{{\mathrm{\λ}}_{mk}W}{ln2({\mathrm{\β}}_2{\mathrm{\ξ}}_1+\mathrm{\μ})}-\frac{I_{mk}+N_0}{|h_m^{cn}{|}^2}\right)$

${\mathrm{\ρ}}_{mk}^{*}=\left\{\begin{array}{cc}1,& k=\arg \underset{k}{max}({\mathrm{\λ}}_{mk}{r}_{mk}-({\mathrm{\β}}_2{\mathrm{\ξ}}_1+{\mathrm{\μ}}_M)p_{mk}^{*}),\\ 0,& else.\end{array}\right.$

Wherein, I_mkRepresent the interference signal intensity that user k receives when receiving Resource Block m,Receive for user k in the n of community To the shock response from base station c signal institute channel on Resource Block m, N₀For thermal noise；

For each CoMP user, calculate the collection of base stations { C that user connects_k,1,C_k,2,...,C_k,k'Inner base station C_ki? Optimum transmitting power on Resource Block mDistribute with optimal subcarrier

$p_{\underset{m}{C_{ki}}}^{*}=\max \left(0,\frac{{\mathrm{\λ}}_{mk}W}{\ln 2\left({\mathrm{\β}}_2{\mathrm{\ξ}}_1+\mathrm{\μ}\right)}-\frac{I_{mk}+N_0}{|h_m^{C_{ki}n}{|}^2}-\frac{\underset{c\≠C_{ki}}{\underset{c\∈\left\{C_{k,1},C_{k,2},\mathrm{...},C_{k,k^{\′}}\right\}}{\Σ}}|h_m^{cn}{|}^2{p}_{m,k}^c}{|h_m^{C_{ki}n}{|}^2}\right)$

${\mathrm{\ρ}}_{mk}^{*}=\left\{\begin{array}{cc}1,& k=\arg \underset{k}{max}({\mathrm{\λ}}_{mk}{r}_{mk}-\underset{c\∈\{C_{k,1},C_{k,2},...,C_{k,k^{\′}}\}}{\Σ}({\mathrm{\β}}_2{\mathrm{\ξ}}_c+{\mathrm{\μ}}_c)p_{\underset{m}{C_{ki}}}^{*}),\\ 0,& else.\end{array}\right.$

Wherein,The base station c that expression user k connects transmitting power on Resource Block m, μ_cAnd ξ_cRepresent different respectively Lagrange multiplier corresponding to serving BS and launch the slope of power and base station power consumption；

Step 3: iteration judges

After step 2 obtains optimal resource allocation, it is judged that each user rate R_kWhether withEqual；As user rate R_kWith Think time unequal that algorithm is not converged, return step one and carry out next iteration；As user rate R_kWithEqual iteration constantly is tied Bundle, resource distribution now is the user resources distribution of optimum.

Beneficial effect: the present invention establishes CoMP HetNet system spectral effect and the combined optimization problem of efficiency, gives one Plant and can realize spectrum effect and the resource allocation methods of efficiency combined optimization.

Present invention have the advantage that

1. give a kind of power distribution method effectively and rapidly that can realize spectrum effect and efficiency combined optimization, it is to avoid The complexity that violence method for solving brings.

2. operator can be by regulation weight coefficient, the proportion between flexible configuration spectrum effect and efficiency, thus realizes energy It is compromise that effect and spectrum are imitated.

3. by the logarithm of user data rate is limited, it is ensured that the fairness between user.

Detailed description of the invention

Below in conjunction with detailed description of the invention, the present invention will be further described.

Efficiency and spectrum effect are not to increase the most simultaneously or reduce, but the increase of spectrum effect can bring in certain interval The drastically decline of efficiency, therefore can not obtain both maximums simultaneously, but should carry out efficiency and compose the compromise of effect, with Realize efficiency and the combined optimization of spectrum effect.

The combined optimization problem of definition efficiency and spectrum effect is:

$\begin{array}{c}\underset{p,\mathrm{\ρ}}{\max }\left({\mathrm{\β}}_1\underset{k=1}{\overset{K}{\Σ}}{\mathrm{lnR}}_k-{\mathrm{\β}}_2{P}_{total}\right)\\ \begin{array}{cc}s.t.& C1:\underset{m=1}{\overset{M}{\Σ}}\underset{k=1}{\overset{K_M}{\Σ}}{\mathrm{\ρ}}_{m,k}{p}_{m,k}\≤P_{TM},\end{array}\\ C2:\underset{m=1}{\overset{M}{\Σ}}\underset{k=1}{\overset{K_P}{\Σ}}{\mathrm{\ρ}}_{m,k}{p}_{m,k}\≤P_{TP},\\ C3:p_{m,k}\≥0,\∀m,\∀k,\\ C4:\underset{m=1}{\overset{M}{\Σ}}{\mathrm{\ρ}}_{m,k}\≥1,\∀k\∈K_M,\\ C5:\underset{m=1}{\overset{M}{\Σ}}{\mathrm{\ρ}}_{m,k}\≥1,\∀k\∈K_P,\\ C6:{\mathrm{\ρ}}_{m,k}\∈\left\{0,1\right\},\∀m,\∀k.\end{array}---\[1\]$

Wherein R_kAnd P_totalRepresent data rate and total system power consumption, the β of user k respectively₁With β₂Represent weight coefficient, β₁ With β₂Value is decided in its sole discretion according to network operation situation by operator.p_m,kRepresent that user k receives the signal power of Resource Block m, ρ_m,kRepresent the Resource Block m distribution condition to user k, work as ρ_m,k=0 represents that Resource Block is not yet assigned to user k, works as ρ_m,k=1 represents Resource Block is assigned to user k.P=(p_1,1, p_1,2,p_1,3,...,p_M,K), ρ=(ρ_1,1,ρ_1,2,ρ_1,3,...,ρ_M,K) represent respectively The distribution of power and Resource Block.P_TMAnd P_TPRepresent macro base station and small station maximum transmission power, K that operator allows respectively_M And K_PRepresent user's set of macro base station and small station service, K respectively_MWith K_PRepresent macro base station and the user of small station service respectively Number, K and M represents the total number of users of system and base station resource block number respectively.Under conditions of given Resource Block and power constraint, Resource Block and power and the distribution between each user thereof are optimized, thus realize efficiency and the combined optimization of spectrum effect.

Key step includes:

1) step one: find user rate lower bound, user rate is approximated

Between logarithm operation, equitable proportion speed and the base station transmitting power of user, there is complex mathematics close System, in order to reduce the complexity of problem, utilizes Dual Method by problem reduction, can be converted into by resource allocation problem:

$\begin{array}{c}\underset{p,\mathrm{\ρ},t}{\max }{\mathrm{\β}}_1\underset{k=1}{\overset{K}{\Σ}}\ln t_k-{\mathrm{\β}}_2{P}_{total}\\ \begin{array}{cc}s.t.& C1,\mathrm{...},C6\end{array}\\ C7:t_k\≤R_k,\∀k.\end{array}---\[2\]$

Wherein, t_kFor user k data rate R_kLower bound, ensure t by C7_kWith R_kRelation, t_kCost function is used Carry out approximated user speed.Owing to logarithmic function is monotonically increasing function, cost function is lower bound t=(t₁,t₂,t₃,...,t_K) Increasing function, when cost function reaches maximum_,Each element in lower bound t also by the data rate equal to each user, because of Problem after this conversion has identical optimal solution with former problem.

Formula [2] being arranged and solve for lagrange problem, Lagrangian is:

$\begin{array}{c}L\left(p,\mathrm{\ρ},t,\mathrm{\λ}\right)={\mathrm{\β}}_1\underset{k=1}{\overset{K}{\Σ}}\ln t_k-{\mathrm{\β}}_2{P}_{total}+\underset{k=1}{\overset{K}{\Σ}}{\mathrm{\λ}}_k\left(R_k-t_k\right)\\ \begin{array}{cc}s.t.& C1,\mathrm{...},C6.\end{array}\end{array}---\[3\]$

Wherein, λ=(λ₁₁,λ₁₂,λ₁₃,...,λ_KM) it is Lagrange multiplier,Lagrange problem is decomposed For dual problemAnd dual equation:

$\begin{array}{c}g\left(\mathrm{\λ}\right)=\underset{p,\mathrm{\ρ},t}{\max }L\left(p,\mathrm{\ρ},t,\mathrm{\λ}\right)\\ \begin{array}{cc}s.t.& C1,...,C6.\end{array}\end{array}---\[4\]$

In dual problem, use gradient descent method renewal Lagrange multiplier:

λ_i=max [0, λ_i+s₁(R_k-t_k)] [5]

Wherein, s₁For updating step-length.

For user k, Lagrangian carried out derivation and make it be equal to zero, can obtain:

$t_k^{*}=\frac{{\mathrm{\β}}_1}{{\mathrm{\λ}}_k}---\[6\]$

2) step 2: by greedy algorithm distributing user resource

The dual equation of formula [4] can be reduced to:

$\begin{array}{c}\underset{p,\mathrm{\ρ}}{max}\underset{k=1}{\overset{K}{\Σ}}{\mathrm{\λ}}_k{R}_k-{\mathrm{\β}}_2\left({\mathrm{\ξ}}_1\underset{k=1}{\overset{K_M}{\Σ}}{p}_k^M+P_C^M\right)-{\mathrm{\β}}_2\underset{R}{\Σ}\left({\mathrm{\ξ}}_2\underset{k=1}{\overset{K_P}{\Σ}}{p}_k^P+P_C^P\right)\\ \begin{array}{cc}s.t.& C1,\mathrm{...},C6\end{array}\end{array}---\[7\]$

Wherein, ξ₁And ξ₂Represent that the slope between power and power consumption is launched in macro base station and small station respectively,WithPoint Not Biao Shi the power that is assigned at macro base station and small station of user k,WithRepresent macro base station and the circuit power in small station, R respectively Represent small station number.Owing to the circuit loss of macro base station, small station is constant value in a given system, problem can be written as:

$\begin{array}{c}\underset{p,\mathrm{\ρ}}{\max }\underset{k=1}{\overset{K}{\Σ}}{\mathrm{\λ}}_k{R}_k-{\mathrm{\β}}_2\left({\mathrm{\ξ}}_1\underset{k=1}{\overset{K_M}{\Σ}}{p}_k^M\right)-{\mathrm{\β}}_2\underset{R}{\Σ}\left({\mathrm{\ξ}}_2\underset{k=1}{\overset{K_P}{\Σ}}{p}_k^P\right)\\ \begin{array}{cc}s.t.& C1,\mathrm{...},C6\end{array}\end{array}---\[8\]$

The restriction launching power is added Lagrangian by macro base station, small station by the method for application Lagrange duality, Arrangement can obtain:

$\begin{array}{c}Q\left(p,\mathrm{\ρ},\mathrm{\μ}\right)=\underset{k=1}{\overset{K}{\Σ}}{\mathrm{\λ}}_k{R}_k-{\mathrm{\β}}_2\mathrm{\ξ}\underset{k=1}{\overset{K_M}{\Σ}}{p}_k^M+{\mathrm{\μ}}_M\left(P_{TM}-\underset{m=1}{\overset{M}{\Σ}}\underset{k=1}{\overset{K_M}{\Σ}}{\mathrm{\ρ}}_{m,k}{p}_{m,k}\right)\\ +\underset{R}{\Σ}\left(-{\mathrm{\β}}_2{\mathrm{\ξ}}_2\underset{k=1}{\overset{K_P}{\Σ}}{p}_k^P+{\mathrm{\μ}}_P\left(P_{TP}-\underset{m=1}{\overset{M}{\Σ}}\underset{k=1}{\overset{K_P}{\Σ}}{\mathrm{\ρ}}_{m,k}{p}_{m,k}\right)\right)\\ =\underset{m=1}{\overset{M1}{\Σ}}\left(\underset{k=1}{\overset{K_{M1}}{\Σ}}\left({\mathrm{\λ}}_{mk}{r}_{mk}-\left({\mathrm{\β}}_2{\mathrm{\ξ}}_1+{\mathrm{\μ}}_M\right)p_{m,k}\right){\mathrm{\ρ}}_{m,k}\right)+{\mathrm{\μ}}_M{P}_{TM}\\ +\underset{R}{\Σ}\left(\underset{k=1}{\overset{M1}{\Σ}}\left(\underset{k=1}{\overset{K_{P1}}{\Σ}}\left({\mathrm{\λ}}_{mk}{r}_{mk}-\left({\mathrm{\β}}_2{\mathrm{\ξ}}_2+{\mathrm{\μ}}_P\right)p_{m,k}\right){\mathrm{\ρ}}_{m,k}\right)+{\mathrm{\μ}}_P{P}_{TP}\right)\\ +\underset{m=1}{\overset{M2}{\Σ}}\left(\underset{k=1}{\overset{K_C}{\Σ}}\left({\mathrm{\λ}}_{mk}{r}_{mk}-\left({\mathrm{\β}}_2\mathrm{\ξ}+\mathrm{\μ}\right)p_{m,k}\right){\mathrm{\ρ}}_{m,k}\right)\\ \begin{array}{cc}s.t.& C1,\mathrm{...},C3,C6\end{array}\end{array}---\[9\]$

Wherein, r_mkRepresent user k data rate on Resource Block m, K_M1、K_P1And K_CRepresent macro base station service respectively Non-CoMP user, the non-CoMP user of small station service and the CoMP user number of system, M1 and M2 represents non-CoMP and CoMP respectively User's available resource block number, μ=(μ_M,μ_P1,...,μ_PR) it is Lagrange multiplier.

Lagrange problem is decomposed into dual problem by the method using Lagrange dualityAnd dual equation:

$\begin{array}{c}h\left(\mathrm{\μ}\right)=\underset{p,\mathrm{\ρ}}{\max }Q\left(p,\mathrm{\ρ},\mathrm{\μ}\right)\\ \begin{array}{cc}s.t.& C1,\mathrm{...},C3,C6.\end{array}\end{array}---\left(10\right)$

In dual problem, gradient descent method is used to update Lagrange multiplier, as a example by macro base station:

${\mathrm{\μ}}_M=max\[0,{\mathrm{\μ}}_M+s_2\left(\underset{m=1}{\overset{M}{\Σ}}\underset{k=1}{\overset{K_M}{\Σ}}{\mathrm{\ρ}}_{m,k}{p}_{m,k}-P_{TM}\right)\]---\[11\]$

Wherein, s₂For updating step-length.

For non-CoMP user, part relevant in Lagrangian Q (p, ρ, μ) is arranged:

$\begin{array}{c}q(\mathrm{\ρ},p)=\underset{m=1}{\overset{M1}{\Σ}}\left(\underset{k=1}{\overset{K_{M1}}{\Σ}}\left({\mathrm{\λ}}_{mk}{r}_{mk}-\left({\mathrm{\β}}_2{\mathrm{\ξ}}_1+{\mathrm{\μ}}_M\right)p_{m,k}\right){\mathrm{\ρ}}_{m,k}\right)\\ +\underset{R}{\Σ}\[\underset{m=1}{\overset{M1}{\Σ}}\left(\underset{k=1}{\overset{M_{P1}}{\Σ}}\left({\mathrm{\λ}}_{mk}{r}_{mk}-\left({\mathrm{\β}}_2{\mathrm{\ξ}}_2+{\mathrm{\μ}}_P\right)p_{m,k}\right){\mathrm{\ρ}}_{m,k}\right)\]\end{array}---\left(12\right)$

Above formula shows not have between Resource Block coupled relation.As a example by the non-CoMP user of macro base station, each money of base station Source block can distribute to the non-CoMP user making following formula reach maximum:

$F(\mathrm{\ρ},p)=\underset{k=1}{\overset{K_{M1}}{\Σ}}({\mathrm{\λ}}_{mk}{r}_{mk}-({\mathrm{\β}}_2{\mathrm{\ξ}}_1+{\mathrm{\μ}}_M\left)p_{m,k}\right){\mathrm{\ρ}}_{m,k}---\[13\]$

Can first calculate the optimum transmitting power on Resource Block m for each user, optimum transmitting merit can be obtained by derivation RateAnd most preferably subcarrier distribution

$p_{mk}^{*}=max\left(0,\frac{{\mathrm{\λ}}_{mk}W}{ln2({\mathrm{\β}}_2{\mathrm{\ξ}}_1+\mathrm{\μ})}-\frac{I_{mk}+N_0}{|h_m^{cn}{|}^2}\right)---\[14\]$

${\mathrm{\ρ}}_{mk}^{*}=\left\{\begin{array}{cc}1,& k=\arg \underset{k}{max}({\mathrm{\λ}}_{mk}{r}_{mk}-({\mathrm{\β}}_2{\mathrm{\ξ}}_1+{\mathrm{\μ}}_M)p_{mk}^{*}),\\ 0,& else.\end{array}\right.---\left(15\right)$

Wherein, I_mkRepresent the interference signal intensity that user k receives when receiving Resource Block m,Receive for user k in the n of community To the shock response from base station c signal institute channel on Resource Block m, N₀For thermal noise.

For each CoMP user, calculate the collection of base stations { C that user connects_k,1,C_k,2,...,C_k,k'Inner base station C_ki? Optimum transmitting power on Resource Block mDistribute with optimal subcarrier

$p_{\underset{m}{C_{ki}}}^{*}=\max \left(0,\frac{{\mathrm{\λ}}_{mk}W}{\ln 2\left({\mathrm{\β}}_2{\mathrm{\ξ}}_1+\mathrm{\μ}\right)}-\frac{I_{mk}+N_0}{|h_m^{C_{ki}n}{|}^2}-\frac{\underset{c\≠C_{ki}}{\underset{c\∈\left\{C_{k,1},C_{k,2},\mathrm{...},C_{k,k^{\′}}\right\}}{\Σ}}|h_m^{cn}{|}^2{p}_{m,k}^c}{|h_m^{C_{ki}n}{|}^2}\right)---\[16\]$

${\mathrm{\ρ}}_{mk}^{*}=\left\{\begin{array}{cc}1,& k=\arg \underset{k}{max}({\mathrm{\λ}}_{mk}{r}_{mk}-\underset{c\∈\{C_{k,1},C_{k,2},...,C_{k,k^{\′}}\}}{\Σ}({\mathrm{\β}}_2{\mathrm{\ξ}}_c+{\mathrm{\μ}}_c)p_{\underset{m}{C_{ki}}}^{*}),\\ 0,& else.\end{array}\right.---\left(17\right)$

Wherein,The base station c that expression user k connects transmitting power on Resource Block m, μ_cAnd ξ_cRepresent different respectively Lagrange multiplier corresponding to serving BS and launch the slope of power and base station power consumption.

3) step 3: iteration judges

After step 2 obtains optimal resource allocation, it is judged that each user rate R_kWhether withEqual.As user rate R_kWith Think time unequal that algorithm is not converged, return step one and carry out next iteration；As user rate R_kWithEqual iteration constantly is tied Bundle, resource distribution now is the user resources distribution of optimum.

Embodiment

Below by embodiment to present invention embodiment of resource distribution in CoMP HetNet system

It is described further.

(1) weight coefficient that base station is selected according to operator, finds user rate lower bound t by formula [6].

(2) base station carries out resource distribution to CoMP user and non-CoMP user respectively according to formula [14]-[17].

(3) if the squared norm of user rate R and lower bound t difference is more than threshold value, then repeat (1) to (3)；It is otherwise Complete spectrum effect, efficiency optimal resource allocation when trading off.Threshold value is selected according to concrete network condition by operator.

The above is only the preferred embodiment of the present invention, it should be pointed out that: for the ordinary skill people of the art For Yuan, under the premise without departing from the principles of the invention, it is also possible to make some improvements and modifications, these improvements and modifications also should It is considered as protection scope of the present invention.

Claims (2)

1. the resource allocation methods of a heterogeneous network multipoint cooperative efficiency spectrum effect combined optimization, it is characterised in that: first, define energy The combined optimization problem of effect and spectrum effect is:

$\underset{p,\mathrm{\ρ}}{max}({\mathrm{\β}}_1\underset{k=1}{\overset{K}{\Σ}}\ln R_k-{\mathrm{\β}}_2{P}_{total})$

$\begin{array}{cc}s.t.& C1:\underset{m=1}{\overset{M}{\Σ}}\underset{k=1}{\overset{K_M}{\Σ}}{\mathrm{\ρ}}_{m,k}{p}_{m,k}\≤P_{TM}\end{array},$

$C2:\underset{m=1}{\overset{M}{\Σ}}\underset{k=1}{\overset{K_P}{\Σ}}{\mathrm{\ρ}}_{m,k}{p}_{m,k}\≤P_{TP},$

$C3:p_{m,k}\≥0,\∀m,\∀k,$

$C4:\underset{m=1}{\overset{M}{\Σ}}{\mathrm{\ρ}}_{m,k}\≥1,\∀k\∈K_M,$

$C5:\underset{m=1}{\overset{M}{\Σ}}{\mathrm{\ρ}}_{m,k}\≥1,\∀k\∈K_P,$

$C6:{\mathrm{\ρ}}_{m,k}\∈\{0,1\},\∀m,\∀k.$

Wherein, R_kAnd P_totalRepresent data rate and total system power consumption, the β of user k respectively₁With β₂Represent weight coefficient, β₁With β₂ Value is decided in its sole discretion according to network operation situation by operator；p_m,kRepresent that user k receives the signal power of Resource Block m, ρ_m,kTable Show the Resource Block m distribution condition to user k, work as ρ_m,k=0 represents that Resource Block is not yet assigned to user k, works as ρ_m,k=1 represents Resource Block It is assigned to user k；P=(p_1,1,p_1,2,p_1,3,...,p_M,K), ρ=(ρ_1,1,ρ_1,2,ρ_1,3,...,ρ_M,K) represent respectively power and The distribution of Resource Block；P_TMAnd P_TPRepresent macro base station and small station maximum transmission power, K that operator allows respectively_MAnd K_PRespectively Represent user's set of macro base station and small station service, K_MWith K_PRepresenting macro base station and the number of users of small station service respectively, K and M divides Biao Shi the total number of users of system and base station resource block number；

Then, under conditions of Resource Block given herein above and power constraint, to Resource Block and power and between each user Distribution is optimized, thus realizes efficiency and the combined optimization of spectrum effect；Wherein, to Resource Block and power and between each user The method that distribution is optimized is: simplifies the relation between user's logarithm speed and power by Dual Method, re-uses greed Algorithm idea carries out resource distribution to CoMP user and non-CoMP user respectively.

The resource allocation methods of heterogeneous network multipoint cooperative efficiency the most according to claim 1 spectrum effect combined optimization, its feature It is: to concretely comprising the following steps that Resource Block and power and the distribution between each user thereof are optimized:

Step one, finds user rate lower bound, approximates user rate:

Utilize Dual Method by problem reduction, resource allocation problem be converted into:

$\underset{p,\mathrm{\ρ},t}{max}{\mathrm{\β}}_1\underset{k=1}{\overset{K}{\Σ}}\ln t_k-{\mathrm{\β}}_2{P}_{total}$

s.t.C1,...,C6,

$C7:t_k\≤R_k,\∀k.$

Wherein, t_kFor user k data rate R_kLower bound, ensure t by C7_kWith R_kRelation, t_kCost function is used near Like user rate；Owing to logarithmic function is monotonically increasing function, cost function is lower bound t=(t₁,t₂,t₃,...,t_K) increasing Function, when cost function reaches maximum, each element in lower bound t, also by the data rate equal to each user, therefore becomes Problem after changing and former problem have identical optimal solution；

Being arranged by above formula and solve for lagrange problem, Lagrangian is:

$L(p,\mathrm{\ρ},t,\mathrm{\λ})={\mathrm{\β}}_1\underset{k=1}{\overset{K}{\Σ}}\ln t_k-{\mathrm{\β}}_2{P}_{total}+\underset{k=1}{\overset{K}{\Σ}}{\mathrm{\λ}}_k(R_k-t_k)$

s.t.C1,...,C6.

Wherein, λ=(λ₁₁,λ₁₂,λ₁₃,...,λ_KM) it is Lagrange multiplier,It is right to be decomposed into by lagrange problem Even problemAnd dual equation:

$g\left(\mathrm{\λ}\right)=\underset{p,\mathrm{\ρ},t}{max}L(p,\mathrm{\ρ},t,\mathrm{\λ})$

s.t.C1,...,C6.

In dual problem, use gradient descent method renewal Lagrange multiplier:

λ_i=max [0, λ_i+s₁(R_k-t_k)]

Wherein, s₁For updating step-length；

For user k, Lagrangian carried out derivation and make it be equal to zero, obtaining:

$t_k^{*}=\frac{{\mathrm{\β}}_1}{{\mathrm{\λ}}_k}$

Step 2: use greedy algorithm distributing user resource:

Dual equation in step one is reduced to:

$\underset{p,\mathrm{\ρ}}{max}\underset{k=1}{\overset{K}{\Σ}}{\mathrm{\λ}}_k{R}_k-{\mathrm{\β}}_2({\mathrm{\ξ}}_1\underset{k=1}{\overset{K_M}{\Σ}}{p}_k^M+P_C^M)-{\mathrm{\β}}_2\underset{R}{\Σ}({\mathrm{\ξ}}_2\underset{k=1}{\overset{K_P}{\Σ}}{p}_k^P+P_C^P)$

s.t.C1,...,C6

Wherein, ξ₁And ξ₂Represent that the slope between power and power consumption is launched in macro base station and small station respectively,WithTable respectively Show the power that user k is assigned at macro base station and small station,WithRepresenting macro base station and the circuit power in small station respectively, R represents Small station number；Owing to the circuit loss of macro base station, small station is constant value in a given system, problem can be written as:

$\underset{p,\mathrm{\ρ}}{max}\underset{k=1}{\overset{K}{\Σ}}{\mathrm{\λ}}_k{R}_k-{\mathrm{\β}}_2\left({\mathrm{\ξ}}_1\underset{k=1}{\overset{K_M}{\Σ}}{p}_k^M\right)-{\mathrm{\β}}_2\underset{R}{\Σ}\left({\mathrm{\ξ}}_2\underset{k=1}{\overset{K_P}{\Σ}}{p}_k^P\right)$

s.t.C1,...,C6

The restriction launching power is added Lagrangian by macro base station, small station by the method for application Lagrange duality, arranges Obtain:

$\begin{array}{c}Q(p,\mathrm{\ρ},\mathrm{\μ})=\underset{k=1}{\overset{K}{\Σ}}{\mathrm{\λ}}_k{R}_k-{\mathrm{\β}}_2\mathrm{\ξ}\underset{k=1}{\overset{K_M}{\Σ}}{p}_k^M+{\mathrm{\μ}}_M(P_{TM}-\underset{m=1}{\overset{M}{\Σ}}\underset{k=1}{\overset{K_M}{\Σ}}{\mathrm{\ρ}}_{m,k}{p}_{m,k})\\ +\underset{R}{\Σ}(-{\mathrm{\β}}_2{\mathrm{\ξ}}_2\underset{k=1}{\overset{K_P}{\Σ}}{p}_k^P+{\mathrm{\μ}}_P(P_{TP}-\underset{m=1}{\overset{M}{\Σ}}\underset{k=1}{\overset{K_P}{\Σ}}{\mathrm{\ρ}}_{m,k}{p}_{m,k}))\\ =\underset{m=1}{\overset{M1}{\Σ}}\left(\underset{k=1}{\overset{K_{M1}}{\Σ}}\right({\mathrm{\λ}}_{mk}{r}_{mk}-({\mathrm{\β}}_2{\mathrm{\ξ}}_1+{\mathrm{\μ}}_M)p_{m,k}\left){\mathrm{\ρ}}_{m,k}\right)+{\mathrm{\μ}}_M{P}_{TM}\\ +\underset{R}{\Σ}\left(\underset{m=1}{\overset{M1}{\Σ}}\right(\underset{k=1}{\overset{K_{P1}}{\Σ}}({\mathrm{\λ}}_{mk}{r}_{mk}-({\mathrm{\β}}_2{\mathrm{\ξ}}_2+{\mathrm{\μ}}_P)p_{m,k}\left){\mathrm{\ρ}}_{m,k}\right)+{\mathrm{\μ}}_P{P}_{TP})\\ +\underset{m=1}{\overset{M2}{\Σ}}\left(\underset{k=1}{\overset{K_C}{\Σ}}\right({\mathrm{\λ}}_{mk}{r}_{mk}-({\mathrm{\β}}_2\mathrm{\ξ}+\mathrm{\μ})p_{m,k}\left){\mathrm{\ρ}}_{m,k}\right)\end{array}$

s.t.C1,...,C3,C6

Wherein, r_mkRepresent user k data rate on Resource Block m, K_M1、K_P1And K_CRepresent the non-CoMP of macro base station service respectively User, the non-CoMP user of small station service and the CoMP user number of system, M1 and M2 represents that non-CoMP and CoMP user can respectively With resource block number, μ=(μ_M,μ_P1,...,μ_PR) it is Lagrange multiplier；

Lagrange problem is decomposed into dual problem by the method using Lagrange dualityAnd dual equation:

$h\left(\mathrm{\μ}\right)=\underset{p,\mathrm{\ρ}}{max}Q(p,\mathrm{\ρ},\mathrm{\μ})$

s.t.C1,...,C3,C6.

In dual problem, gradient descent method is used to update Lagrange multiplier, as a example by macro base station:

${\mathrm{\μ}}_M=max\[0,{\mathrm{\μ}}_M+s_2(\underset{m=1}{\overset{M}{\Σ}}\underset{k=1}{\overset{K_M}{\Σ}}{\mathrm{\ρ}}_{m,k}{p}_{m,k}-P_{TM})\]$

Wherein, s₂For updating step-length；

For non-CoMP user, part relevant in Lagrangian Q (p, ρ, μ) is arranged:

$\begin{array}{c}q(\mathrm{\ρ},p)=\underset{m=1}{\overset{M1}{\Σ}}\left(\underset{k=1}{\overset{K_{M1}}{\Σ}}\right({\mathrm{\λ}}_{mk}{r}_{mk}-({\mathrm{\β}}_2{\mathrm{\ξ}}_1+{\mathrm{\μ}}_M)p_{m,k}\left){\mathrm{\ρ}}_{m,k}\right)\\ +\underset{R}{\Σ}\[\underset{m=1}{\overset{M1}{\Σ}}\left(\underset{k=1}{\overset{K_{P1}}{\Σ}}\right({\mathrm{\λ}}_{mk}{r}_{mk}-({\mathrm{\β}}_2{\mathrm{\ξ}}_2+{\mathrm{\μ}}_P)p_{m,k}\left){\mathrm{\ρ}}_{m,k}\right)\]\end{array}$

Above formula shows not have between Resource Block coupled relation；As a example by the non-CoMP user of macro base station, each Resource Block of base station Distribute to the non-CoMP user making following formula reach maximum:

$F(\mathrm{\ρ},p)=\underset{k=1}{\overset{K_{M1}}{\Σ}}({\mathrm{\λ}}_{mk}{r}_{mk}-({\mathrm{\β}}_2{\mathrm{\ξ}}_1+{\mathrm{\μ}}_M\left)p_{m,k}\right){\mathrm{\ρ}}_{m,k}$

Each user is first calculated to the optimum transmitting power on Resource Block m, obtain optimum transmitting power by derivationAnd Good subcarrier distributes

$p_{mk}^{*}=max(0,\frac{{\mathrm{\λ}}_{mk}W}{ln2({\mathrm{\β}}_2{\mathrm{\ξ}}_1+\mathrm{\μ})}-\frac{I_{mk}+N_0}{|h_m^{cn}{|}^2})$

${\mathrm{\ρ}}_{mk}^{*}=\left\{\begin{array}{cc}1,& k=\arg \underset{k}{max}({\mathrm{\λ}}_{mk}{r}_{mk}-({\mathrm{\β}}_2{\mathrm{\ξ}}_1+{\mathrm{\μ}}_M)p_{mk}^{*}),\\ 0,& else.\end{array}\right.$

Wherein, I_mkRepresent the interference signal intensity that user k receives when receiving Resource Block m,Receive for user k in the n of community From the shock response of base station c signal institute channel on Resource Block m, N₀For thermal noise；

For each CoMP user, calculate the collection of base stations { C that user connects_k,1,C_k,2,...,C_k,k'Inner base station C_kiIn resource Optimum transmitting power on block mDistribute with optimal subcarrier

$p_{\underset{m}{C_{ki}}}^{*}=\max (0,\frac{{\mathrm{\λ}}_{mk}W}{\ln 2({\mathrm{\β}}_2{\mathrm{\ξ}}_1+\mathrm{\μ})}-\frac{I_{mk}+N_0}{|h_m^{C_{ki}n}{|}^2}-\frac{\underset{c\≠C_{ki}}{\underset{c\∈\{C_{k,1},C_{k,2},...,C_{k,k^{\′}}\}}{\mathrm{\Σ}}}|h_m^{cn}{|}^2{p}_{m,k}^c}{|h_m^{C_{ki}n}{|}^2})$

${\mathrm{\ρ}}_{mk}^{*}=\left\{\begin{array}{cc}1,& k=\arg \underset{k}{max}({\mathrm{\λ}}_{mk}{r}_{mk}-\underset{c\∈\{C_{k,1},C_{k,2},...,C_{k,k^{\′}}\}}{\Σ}({\mathrm{\β}}_2{\mathrm{\ξ}}_c+{\mathrm{\μ}}_c)p_{\underset{m}{C_{ki}}}^{*}),\\ 0,& else.\end{array}\right.$

Wherein,The base station c that expression user k connects transmitting power on Resource Block m, μ_cAnd ξ_cRepresent different services respectively Lagrange multiplier corresponding to base station and launch the slope of power and base station power consumption；

Step 3: iteration judges

After step 2 obtains optimal resource allocation, it is judged that each user rate R_kWhether withEqual；As user rate R_kWithNot phase Deng time think that algorithm is not converged, return step one carry out next iteration；As user rate R_kWithEqual iteration constantly terminates, Resource distribution now is the user resources distribution of optimum.