CN105873216B - The resource allocation methods of heterogeneous network multipoint cooperative efficiency spectrum effect combined optimization - Google Patents

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

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CN105873216B
CN105873216B CN201610302348.5A CN201610302348A CN105873216B CN 105873216 B CN105873216 B CN 105873216B CN 201610302348 A CN201610302348 A CN 201610302348A CN 105873216 B CN105873216 B CN 105873216B
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user
resource block
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CN105873216A (en
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潘志文
聂阳宁
刘楠
尤肖虎
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White Box Shanghai Microelectronics Technology Co ltd
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Southeast University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0453—Resources in frequency domain, e.g. a carrier in FDMA
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0473—Wireless resource allocation based on the type of the allocated resource the resource being transmission power
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/51—Allocation or scheduling criteria for wireless resources based on terminal or device properties
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/53—Allocation or scheduling criteria for wireless resources based on regulatory allocation policies
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention discloses the resource allocation methods that a kind of heterogeneous network multipoint cooperative efficiency composes effect combined optimization, firstly, defining the combined optimization problem of efficiency and spectrum effect；Then, under conditions of resource block and power constraint given herein above, resource block and power and its distribution between each user are optimized, to realize the combined optimization of efficiency and spectrum effect；Wherein, the method that the distribution to resource block and power and its between each user optimizes are as follows: the relationship between user's logarithm rate and power is simplified by Dual Method, greedy algorithm thought is reused and resource allocation is carried out to CoMP user and non-CoMP user respectively.The present invention gives a kind of effectively and rapidly power distribution methods for being able to achieve spectrum effect and efficiency combined optimization, avoid violence method for solving bring complexity.

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 problems in mobile communication system, belong to the network technology in wireless communication Field.
Background technique
As the portable mobile terminals such as smart phone and tablet computer come into vogue and gradually popularize, wireless data service amount It is swift and violent to increase.However, the raising speed of spectrum efficiency of communication system does not catch up with the speed of data requirements growth, this problem now It is especially severe in the intensive hot spot region of flow of the people.
More stringent requirements are proposed for transmittability of the wireless communication system to communication network, extremely limited for frequency spectrum resource Mobile communication system, need to be promoted the message transmission rate in unit transmission bandwidth, i.e., raising system spectrum effect (SE, Spectral Efficiency).Coordinated multipoint transmission technology (Coordinated multipoint Processing, CoMP) With isomery network technology (heterogeneous net, HetNet) as raising frequency spectrum effect in the future wireless network being concerned The technology of rate and coverage rate can support the heterogeneous network of CoMP to become research hotspot.Due to wireless communications environment complexity, user There are many differences, the fairness between user is of concern data rate.
Concern with people to energy consumption and environmental problem, the energy consumption problem of mobile communications network also increasingly cause The concern of numerous researchers.Therefore, the efficiency (EE, Energy Efficiency) of mobile communication system how is improved, it has also become One research hotspot of current mobile communications research fields, the efficiency of system, which also becomes, to be measured the important performance of communication network and refers to One of mark.However efficiency with spectrum imitate both Measure Indexes be not always it is consistent change, it is sometimes even mutually contradictory, One of index that covets will will cause the dramatic decrease of another index.Then, the balance and compromise between two kinds of indexs, from And realizing combined optimization becomes a hot issue.
Summary of the invention
The resource allocation methods for imitating combined optimization are composed the object of the present invention is to provide a kind of heterogeneous network multipoint cooperative efficiency, with It is balanced and trades off between efficiency to mobile communication system and spectrum two kinds of indexs of effect, to realize that it is excellent that efficiency spectrum effect is reasonably combined Change.
To achieve the above object, the technical solution adopted by the present invention are as follows:
A kind of resource allocation methods of heterogeneous network multipoint cooperative efficiency spectrum effect combined optimization,
Firstly, defining the combined optimization problem of efficiency and spectrum effect are as follows:
Wherein, RkAnd PtotalRespectively indicate the data rate and total system power consumption of user k, β1With β2Indicate weighting coefficient, β1 With β2Value is decided in its sole discretion by operator according to network operation situation；pm,kIndicate that user k receives the signal power of resource block m, ρm,kIndicate that resource block m to the distribution condition of user k, works as ρm,k=0 expression resource block is not yet assigned to user k, works as ρm,k=1 indicates Resource block is assigned to user k；P=(p1,1,p1,2,p1,3,...,pM,K), ρ=(ρ1,1,ρ1,2,ρ1,3,...,ρM,K) respectively represent The distribution of power and resource block；PTMAnd PTPThe macro base station and small station maximum transmission power of operator's permission, K are respectively representedMWith KPUser's set of macro base station and small station service, K are respectively representedMWith KPThe number of users of macro base station and small station service is respectively indicated, K and M respectively indicates the total number of users of system and base station resource block number；
Then, under conditions of resource block and power constraint given herein above, to resource block and power and its in each user Between distribution optimize, thus realize efficiency and spectrum effect combined optimization；Wherein, to resource block and power and its in each user Between the method that optimizes of distribution are as follows: the relationship between user's logarithm rate and power is simplified by Dual Method, is reused Greedy algorithm thought carries out resource allocation to CoMP user and non-CoMP user respectively.
The specific steps that distribution to resource block and power and its between each user optimizes are as follows:
Step 1 finds user rate lower bound, carries out to user rate approximate:
Using Dual Method by problem reduction, resource allocation problem is converted are as follows:
s.t.C1,...,C6,
Wherein, tkFor user's k data rate RkLower bound, t is guaranteed by C7kWith RkRelationship, tkIt is used in cost function Carry out approximated user rate；Since logarithmic function is the function of monotonic increase, cost function is lower bound t=(t1,t2,t3,...,tK) Increasing function, when cost function reaches maximum value, each element in lower bound t will also be equal to the data rate of each user, because This transformed problem and former problem have identical optimal solution；
Above formula is arranged and is solved for lagrange problem, Lagrangian are as follows:
s.t.C1,...,C6.
Wherein, λ=(λ11,λ12,λ13,...,λKM) it is Lagrange multiplier,Lagrange problem is decomposed For dual problemAnd dual equation:
s.t.C1,...,C6.
In dual problem, Lagrange multiplier is updated using gradient descent method:
λi=max [0, λi+s1(Rk-tk)]
Wherein, s1To update step-length；
For user k, derivation is carried out to Lagrangian and it is enabled to be equal to zero, is obtained:
Step 2: greedy algorithm distributing user resource is used:
Dual equation in step 1 is simplified are as follows:
s.t.C1,...,C6
Wherein, ξ1And ξ2It respectively indicates macro base station and small station transmission power and consumes the slope between power,WithPoint Not Biao Shi the power that is assigned in macro base station and small station of user k,WithThe circuit power in macro base station and small station is respectively indicated, R indicates small station number；Since the circuit loss of macro base station, small station is constant value in a given system, problem can be written as:
s.t.C1,...,C6
Lagrangian is added into the limitation of transmission power in macro base station, small station using the method for Lagrange duality, Arrangement obtains:
s.t.C1,...,C3,C6
Wherein, rmkIndicate data rate of the user k on resource block m, KM1、KP1And KCRespectively indicate macro base station service The CoMP user number of non-CoMP user, the non-CoMP user of small station service and system, M1 and M2 respectively indicate non-CoMP and CoMP User's available resource block number, μ=(μM,μP1,...,μPR) it is Lagrange multiplier；
Lagrange problem is decomposed into dual problem using the method for Lagrange dualityAnd dual equation:
s.t.C1,...,C3,C6.
In dual problem, Lagrange multiplier is updated using gradient descent method, by taking macro base station as an example:
Wherein, s2To update step-length；
For non-CoMP user, relevant part in Lagrangian Q (p, ρ, μ) is arranged:
Above formula shows that there is no coupled relations between resource block；By taking the non-CoMP user of macro base station as an example, each money of base station Source block distributes to the non-CoMP user for enabling following formula reach maximum value:
For the optimum transmission power on each user elder generation computing resource block m, optimum transmission power is obtained by derivation And best subcarrier distribution
Wherein, ImkIndicate the interference signal intensity received when user k receives resource block m,It is received for user k in cell n To the signal institute channel from base station c on resource block m shock response, N0For thermal noise；
For each CoMP user, the collection of base stations { C of user's connection is calculatedk,1,Ck,2,...,Ck,k'Inner base station Cki? Optimum transmission power on resource block mIt is distributed with best subcarrier
Wherein,Indicate transmission power of the base station c of user k connection on resource block m, μcAnd ξcRespectively indicate difference Serving BS corresponding Lagrange multiplier and transmission power and base station power consumption slope；
Step 3: iteration judgement
After step 2 finds out optimal resource allocation, each user rate R is judgedkWhether withIt is equal；As user rate RkWith Think that algorithm is not converged when unequal, return step one carries out next iteration；As user rate RkWithEqual constantly iteration knot Beam, resource allocation at this time are optimal user resources distribution.
The utility model has the advantages that the present invention establishes the combined optimization problem of CoMP HetNet system spectral effect and efficiency, one is given Kind is able to achieve the resource allocation methods of spectrum effect and efficiency combined optimization.
The present invention has the advantage that
1. giving a kind of effectively and rapidly power distribution method for being able to achieve spectrum effect and efficiency combined optimization, avoid Violence method for solving bring complexity.
2. operator can be by adjusting weighting coefficient, flexible configuration spectrum imitates the specific gravity between efficiency, to realize energy The compromise of effect and spectrum effect.
3. limiting by the logarithm to user data rate, the fairness between user ensure that.
Specific embodiment
The present invention will be further described With reference to embodiment.
Efficiency and spectrum effect are not always while to increase or reduce, but the increase that effect is composed in certain section can be brought The sharply decline of efficiency, therefore the maximum value of the two can not be obtained simultaneously, but the compromise of efficiency and spectrum effect should be carried out, with Realize the combined optimization of efficiency and spectrum effect.
Define the combined optimization problem of efficiency and spectrum effect are as follows:
Wherein RkAnd PtotalRespectively indicate the data rate and total system power consumption of user k, β1With β2Indicate weighting coefficient, β1 With β2Value is decided in its sole discretion by operator according to network operation situation.pm,kIndicate that user k receives the signal power of resource block m, ρm,kIndicate that resource block m to the distribution condition of user k, works as ρm,k=0 expression resource block is not yet assigned to user k, works as ρm,k=1 indicates Resource block is assigned to user k.P=(p1,1, p1,2,p1,3,...,pM,K), ρ=(ρ1,1,ρ1,2,ρ1,3,...,ρM,K) respectively represent The distribution of power and resource block.PTMAnd PTPThe macro base station and small station maximum transmission power of operator's permission, K are respectively representedM And KPUser's set of macro base station and small station service, K are respectively representedMWith KPRespectively indicate the user of macro base station and small station service Number, K and M respectively indicate the total number of users of system and base station resource block number.Under conditions of given resource block and power constraint, Resource block and power and its distribution between each user are optimized, to realize the combined optimization of efficiency and spectrum effect.
Key step includes:
1) step 1: finding user rate lower bound, carries out to user rate approximate
By logarithm operation, there are more complicated mathematics passes between the ratio fair rate and base station transmitting power of user System, in order to reduce the complexity of problem, using Dual Method by problem reduction, resource allocation problem can be converted are as follows:
Wherein, tkFor user's k data rate RkLower bound, t is guaranteed by C7kWith RkRelationship, tkIt is used in cost function Carry out approximated user rate.Since logarithmic function is the function of monotonic increase, cost function is lower bound t=(t1,t2,t3,...,tK) Increasing function, when cost function reaches maximum value,Each element in lower bound t will also be equal to the data rate of each user, because This transformed problem and former problem have identical optimal solution.
Formula [2] is arranged and is solved for lagrange problem, Lagrangian are as follows:
Wherein, λ=(λ11,λ12,λ13,...,λKM) it is Lagrange multiplier,Lagrange problem is decomposed For dual problemAnd dual equation:
In dual problem, Lagrange multiplier is updated using gradient descent method:
λi=max [0, λi+s1(Rk-tk)] [5]
Wherein, s1To update step-length.
For user k, derivation is carried out to Lagrangian and it is enabled to be equal to zero, can be obtained:
2) step 2: greedy algorithm distributing user resource is used
The dual equation of formula [4] can simplify are as follows:
Wherein, ξ1And ξ2It respectively indicates macro base station and small station transmission power and consumes the slope between power,WithPoint Not Biao Shi the power that is assigned in macro base station and small station of user k,WithThe circuit power in macro base station and small station is respectively indicated, R indicates small station number.Since the circuit loss of macro base station, small station is constant value in a given system, problem be can be written as:
Lagrangian is added into the limitation of transmission power in macro base station, small station using the method for Lagrange duality, Arrangement can obtain:
Wherein, rmkIndicate data rate of the user k on resource block m, KM1、KP1And KCRespectively indicate macro base station service The CoMP user number of non-CoMP user, the non-CoMP user of small station service and system, M1 and M2 respectively indicate non-CoMP and CoMP User's available resource block number, μ=(μM,μP1,...,μPR) it is Lagrange multiplier.
Lagrange problem is decomposed into dual problem using the method for Lagrange dualityAnd dual equation:
In dual problem, Lagrange multiplier is updated using gradient descent method, by taking macro base station as an example:
Wherein, s2To update step-length.
For non-CoMP user, relevant part in Lagrangian Q (p, ρ, μ) is arranged:
Above formula shows that there is no coupled relations between resource block.By taking the non-CoMP user of macro base station as an example, each money of base station Source block can distribute to the non-CoMP user for enabling following formula reach maximum value:
For each user can optimum transmission power on first computing resource block m, optimal transmitting function can be obtained by derivation RateAnd best subcarrier distribution
Wherein, ImkIndicate the interference signal intensity received when user k receives resource block m,It is received for user k in cell n To the signal institute channel from base station c on resource block m shock response, N0For thermal noise.
For each CoMP user, the collection of base stations { C of user's connection is calculatedk,1,Ck,2,...,Ck,k'Inner base station Cki? Optimum transmission power on resource block mIt is distributed with best subcarrier
Wherein,Indicate transmission power of the base station c of user k connection on resource block m, μcAnd ξcRespectively indicate difference Serving BS corresponding Lagrange multiplier and transmission power and base station power consumption slope.
3) step 3: iteration judgement
After step 2 finds out optimal resource allocation, each user rate R is judgedkWhether withIt is equal.As user rate RkWith Think that algorithm is not converged when unequal, return step one carries out next iteration；As user rate RkWithEqual constantly iteration knot Beam, resource allocation at this time are optimal user resources distribution.
Embodiment
Below by embodiment to the embodiment of present invention resource allocation in CoMP HetNet system
It is described further.
(1) weighting coefficient that base station is selected according to operator finds user rate lower bound t by formula [6].
(2) base station carries out resource allocation to CoMP user and non-CoMP user respectively according to formula [14]-[17].
(3) if user rate R and the squared norm of lower bound t difference are greater than threshold value, (1) to (3) is repeated；Otherwise it is Optimal resource allocation when completing spectrum effect, efficiency compromise.Threshold value is selected by operator according to specific network condition.
The above is only a preferred embodiment of the present invention, it should be pointed out that: for the ordinary skill people of the art For member, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also answered It is considered as protection scope of the present invention.

Claims (1)

1. a kind of resource allocation methods of heterogeneous network multipoint cooperative efficiency spectrum effect combined optimization, it is characterised in that:
Firstly, defining the combined optimization problem of efficiency and spectrum effect are as follows:
Wherein, RkAnd PtotalRespectively indicate the data rate and total system power consumption of user k, β1With β2Indicate weighting coefficient, β1With β2 Value is decided in its sole discretion by operator according to network operation situation；pm,kIndicate that user k receives the signal power of resource block m, ρm,kTable Show that resource block m to the distribution condition of user k, works as ρm,k=0 expression resource block is not yet assigned to user k, works as ρm,k=1 indicates resource block It is assigned to user k；P=(p1,1,p1,2,p1,3,...,pM,K), ρ=(ρ1,1,ρ1,2,ρ1,3,...,ρM,K) respectively represent power and The distribution of resource block；PTMAnd PTPThe macro base station and small station maximum transmission power of operator's permission, K are respectively representedMAnd KPRespectively Represent user's set of macro base station and small station service, KMWith KPThe number of users of macro base station and small station service is respectively indicated, K and M divide It Biao Shi not the total number of users of system and base station resource block number；
Then, under conditions of resource block and power constraint given herein above, to resource block and power and its between each user Distribution optimizes, to realize the combined optimization of efficiency and spectrum effect；Wherein, to resource block and power and its between each user Distribute the method optimized are as follows: the relationship between user's logarithm rate and power is simplified by Dual Method, reuses greed Algorithm idea carries out resource allocation to CoMP user and non-CoMP user respectively；
The specific steps that distribution to resource block and power and its between each user optimizes are as follows:
Step 1 finds user rate lower bound, carries out to user rate approximate:
Using Dual Method by problem reduction, resource allocation problem is converted are as follows:
Wherein, tkFor user's k data rate RkLower bound, t is guaranteed by C7kWith RkRelationship, tkIt is used in cost function close Like user rate；Since logarithmic function is the function of monotonic increase, cost function is lower bound t=(t1,t2,t3,...,tK) increasing Function, when cost function reaches maximum value, each element in lower bound t will also be equal to the data rate of each user, therefore become Problem and former problem after changing have identical optimal solution；
Above formula is arranged and is solved for lagrange problem, Lagrangian are as follows:
s.t.C1,...,C6.
Wherein, λ=(λ11,λ12,λ13,...,λmk) it is Lagrange multiplier,Lagrange problem is decomposed into pair Even problemAnd dual equation:
s.t.C1,...,C6.
In dual problem, Lagrange multiplier is updated using gradient descent method:
λi=max [0, λi+s1(Rk-tk)]
Wherein, s1To update step-length；
For user k, derivation is carried out to Lagrangian and it is enabled to be equal to zero, is obtained:
Step 2: greedy algorithm distributing user resource is used:
Dual equation in step 1 is simplified are as follows:
s.t.C1,...,C6
Wherein, ξ1And ξ2It respectively indicates macro base station and small station transmission power and consumes the slope between power,WithTable respectively Show the power that user k is assigned in macro base station and small station,WithThe circuit power in macro base station and small station is respectively indicated, R is indicated Small station number；Since the circuit loss of macro base station, small station is constant value in a given system, problem can be written as:
s.t.C1,...,C6
Lagrangian is added into the limitation of transmission power in macro base station, small station using the method for Lagrange duality, arranged It obtains:
s.t.C1,...,C3,C6
Wherein, rmkIndicate data rate of the user k on resource block m, KM1、KP1And KCRespectively indicate the non-CoMP of macro base station service User, the non-CoMP user of small station service and system CoMP user number, M1 and M2 respectively indicates non-CoMP and CoMP user can With resource block number, μ=(μM, μp1..., μPR) it is Lagrange multiplier；
Lagrange problem is decomposed into dual problem using the method for Lagrange dualityAnd dual equation:
s.t.C1,...,C3,C6.
In dual problem, Lagrange multiplier is updated using gradient descent method, by taking macro base station as an example:
Wherein, s2To update step-length；
For non-CoMP user, relevant part in Lagrangian Q (p, ρ, μ) is arranged:
Above formula shows that there is no coupled relations between resource block；By taking the non-CoMP user of macro base station as an example, each resource block of base station Distribute to the non-CoMP user for enabling following formula reach maximum value:
For the optimum transmission power on each user elder generation computing resource block m, optimum transmission power is obtained by derivationAnd most Good subcarrier distribution
Wherein, ImkIndicate the interference signal intensity received when user k receives resource block m,It is received for user k in cell n The shock response of signal institute channel from base station c on resource block m, N0For thermal noise；
For each CoMP user, the collection of base stations { C of user's connection is calculatedK, 1, CK, 2..., CK, iInner base station CK, iIn resource Optimum transmission power on block mIt is distributed with best subcarrier
Wherein,Indicate transmission power of the base station c of user k connection on resource block m, μcAnd ξcRespectively indicate different services The slope of base station corresponding Lagrange multiplier and transmission power and base station power consumption；
Step 3: iteration judgement
After step 2 finds out optimal resource allocation, each user rate R is judgedkWhether withIt is equal；As user rate RkWithNot phase Think that algorithm is not converged whens equal, return step one carries out next iteration；As user rate RkWithEqual constantly iteration terminates, Resource allocation at this time is optimal user resources distribution.
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