CN108260193B - Joint resource allocation method and device based on channel aggregation in heterogeneous network - Google Patents

Joint resource allocation method and device based on channel aggregation in heterogeneous network Download PDF

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CN108260193B
CN108260193B CN201810032402.8A CN201810032402A CN108260193B CN 108260193 B CN108260193 B CN 108260193B CN 201810032402 A CN201810032402 A CN 201810032402A CN 108260193 B CN108260193 B CN 108260193B
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individual
channel
base station
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matrix
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CN108260193A (en
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贾杰
陈剑
洪庭贺
陈柯任
吉鹏硕
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Northeastern University China
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    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/02—Power saving arrangements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/30—TPC using constraints in the total amount of available transmission power
    • H04W52/34—TPC management, i.e. sharing limited amount of power among users or channels or data types, e.g. cell loading
    • H04W52/346—TPC management, i.e. sharing limited amount of power among users or channels or data types, e.g. cell loading distributing total power among users or channels
    • 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
    • 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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Abstract

The invention relates to a joint resource allocation method and a joint resource allocation device based on channel aggregation in a heterogeneous network, wherein the method comprises the following steps: determining an initial population G of a genetic algorithm, wherein each individual in G is a feasible channel allocation scheme; carrying out crossover and mutation genetic operations on each individual to generate a new population G1, aiming at each individual in G1, searching the optimal power distribution of the individual by adopting a Zoutendijk feasible direction method, determining the power consumption ratio of the individual, selecting m individuals with the lowest power consumption ratio as the initial population for next iteration, repeating the process until iteration is preset for times, obtaining the optimal individual with the lowest power consumption ratio, taking the channel distribution of the optimal individual as the channel distribution scheme of the whole network, and taking the power distribution scheme of the optimal individual as the power distribution scheme of the whole network. Through the iterative optimization searching capability, the optimal power distribution value under the optimal channel distribution condition can be continuously searched, and the power consumption of the user equipment minimization network is met.

Description

Joint resource allocation method and device based on channel aggregation in heterogeneous network
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a method and an apparatus for joint resource allocation based on channel aggregation in a heterogeneous network.
Background
With the increasing popularity of intelligent wireless devices and the dramatic increase in wireless data traffic demand, the limited spectrum resources in mobile communications become increasingly scarce and crowded, which has become a bottleneck limiting the development of wireless communications. The heterogeneous network can enhance the area spectrum multiplexing to effectively improve the spectrum utilization efficiency by deploying other various access nodes with different wireless access capabilities, such as micro base stations, relay nodes and the like, in the coverage area of the traditional macro cell. However, the reuse of the spectrum causes communication interference between co-channel users, and how to provide the communication rate of the users as much as possible through an efficient resource management method becomes an important research content in the heterogeneous network.
In addition to meeting the increasing user demand through resource management, resource management of heterogeneous networks also needs to consider the overall communication energy consumption of the network. Statistics show that the information and communication technology industry is becoming a main industry of global energy consumption, and in 2008, the energy consumption of the information and communication technology industry accounts for 8% of the global energy consumption, and is expected to be doubled by 2020. In particular, the mobile communication system occupies 0.5% of global energy consumption. Therefore, how to design an efficient resource management method is of great significance to minimize the overall energy consumption of communication on the basis of meeting the increasing user demands. Although there are some resource allocation methods for heterogeneous cellular networks, these methods ignore the ability of the ue to aggregate multiple different channels, and thus their ability to increase user rate is not sufficient.
Disclosure of Invention
Technical problem to be solved
In order to solve the problem that the user rate cannot be improved due to the fact that the aggregation capability of user equipment to different channels is neglected in the prior art, the invention provides a joint resource allocation method and device based on channel aggregation in a heterogeneous network.
(II) technical scheme
In order to achieve the purpose, the invention adopts the main technical scheme that:
a joint resource allocation method based on channel aggregation in a heterogeneous network comprises the following steps:
101. acquiring the position information of N user equipment, and constructing a combined optimization model based on spectrum aggregation according to the position information of all the user equipment;
102. randomly initializing m individuals by adopting an integer coding method, and taking the m individuals as an initial population G of a genetic algorithm;
wherein each individual is a feasible channel allocation scheme;
103. performing crossover and mutation genetic operations on each individual to generate a new population G1, wherein the number of individuals of the new population G1 is 2 m;
104. aiming at each individual in the new population G1, searching an optimal power distribution method corresponding to the channel distribution of the current individual, and taking the energy efficiency ratio of power and rate as the fitness evaluation value of the current individual;
105. sorting the individuals in the new population G1 from large to small according to the fitness evaluation value, and selecting the last m individuals in the sorting as the population used in the next iteration;
106. based on the population used in the next iteration, the process of step 103-105 is repeated until the iteration reaches the preset upper limit of the iteration times;
107. acquiring an optimal individual with the minimum fitness evaluation value, and taking the channel allocation of the acquired optimal individual as a channel allocation scheme of the whole network in the heterogeneous network and a power allocation scheme corresponding to the optimal individual as a power allocation scheme of the whole network in the heterogeneous network;
wherein the channel allocation scheme and the power allocation scheme are schemes obtained by processing the constructed joint optimization model by using a genetic algorithm.
Optionally, the step 101 includes:
1011. with pn,sExpressing the percentage of the power distributed to the channel S by the base station N for N for S, and constructing the power distribution constraint of a formula 1;
Figure GDA0002246836620000031
wherein N is the set of all heterogeneous base stations in all single macro cells, S is the set of all channels in each heterogeneous base station, and the power distribution constraint indicates that the power value distributed by each heterogeneous base station cannot exceed the maximum power of the base station;
1012. with an indicative function An,s,kIndicating whether a certain channel s of a certain heterogeneous base station n is allocated to a certain user equipment k,
Figure GDA0002246836620000032
formula 2;
the channel allocation constraints are constructed as:
Figure GDA0002246836620000033
Figure GDA0002246836620000034
wherein K is a set of all user equipments, and the channel allocation constraint indicates that each channel of each heterogeneous base station is occupied by at most one user equipment, and each user equipment can aggregate at most C available channels;
1013. constructing an overall transmission rate R for a single user kk：
Figure GDA0002246836620000035
Equation 5;
wherein Dn,kRepresenting the Euclidean distance, B, between the user equipment k and the heterogeneous base station ndRepresents the bandwidth of the base station;
1014. the energy efficiency ratio model of the whole heterogeneous network is constructed by
Figure GDA0002246836620000036
Equation 6 is constrained by equations 1, 3, and 4.
Optionally, the step 102 includes:
1021. the channel allocation scheme of the whole heterogeneous base station is abstractly expressed as a two-dimensional array C, in the two-dimensional array, different heterogeneous base stations are expressed by rows, and different channels are expressed by columns:
Figure GDA0002246836620000041
wherein, CijC represents the distribution state of the jth channel of the base station i, and the number of the user equipment is K e {1,2,3, …, K }, thenijK denotes that the jth channel of the base station i is allocated to the kth user equipment; thus, CijE {0,1,2,3, …, K }, where C ij0 means that the channel is not allocated to any user equipment, the channel is in an idle state, and obviously, the channel power allocation in the idle state is 0;
1022. randomly assigning a value from the set {0,1,2,3, …, K } for each element in C;
1023. substep 1021 and substep 1022 are repeated until m tuples C are generated, and the generated m tuples are used as an initial population of the genetic algorithm.
Optionally, the step 103 includes:
1031. randomly selecting two individuals A and B from an initial population G;
1032. generating random numbers in the range of (0,1), and if the random numbers are less than the crossover rate Pc, performing crossover operation steps 1033-1039;
1033. replicating individual a and individual B to produce individual a ', individual B' as a parent individual;
1034. selecting n numbers from the row number of the channel allocation scheme two-dimensional array C as cross point positions, and storing the cross point positions in the C;
1035. for each row i in the channel allocation scheme two-dimensional array C:
1036. selecting a cross point j in the row of genes;
1037. interchanging genes behind the ith row gene position j in the genes distributed by the channels of the two parents;
1038. interchanging genes behind the ith row gene position j in the genes distributed by the power of the two parent individuals;
1039. repeating the steps 1034 and 1038 until the whole two-dimensional array C is traversed, completing the cross operation of the genes between the individual A and the individual B, wherein the crossed individual A and the individual B are new filial generation individuals;
10310 removing newly obtained offspring individuals A and B from the population, and adding the offspring individuals A ' and B ' together with the father individuals A ' and B ' into a new population G ';
10311. for each individual in the population G' newly generated after crossing;
10312. generating random numbers within the range of 0-1 for each row of the two-dimensional array C, namely a channel allocation sequence of each base station, and if the random numbers are less than the variation rate Pm, performing variation operation on the row of the base stations;
10313. randomly selecting a gene position in the row, if the gene position is in an idle state, namely the value of the gene position is 0, randomly selecting a user device in the range of the base station, and distributing a channel corresponding to the gene position to the selected user device;
10314. if the channel corresponding to the gene position is in a non-idle state, generating another random number p within the range of 0-1, and if p is less than 0.5, emptying the channel corresponding to the gene position; otherwise, randomly selecting another user equipment within the range of the base station, and distributing the channel corresponding to the gene position to the selected user equipment;
10315. and repeating steps 10312 and 10314 until all the base stations complete the mutation operation, and then completing the mutation operation of the individual.
Optionally, the step 104 includes:
aiming at each individual in the new population G1, an optimal power distribution method corresponding to the channel distribution of the current individual is found by using the Zotendijk feasible direction method, and the energy efficiency ratio of power and speed is used as the fitness evaluation value of the current individual.
Optionally, the step 104 includes:
1041. based on the channel allocation result in the individual coding, the energy efficiency ratio model in the simplified formula 6 is:
Figure GDA0002246836620000061
Figure GDA0002246836620000062
wherein
Figure GDA0002246836620000063
A power allocation matrix representing a base station;
1042. taking an inverse number from the formula 7, and converting the inverse number into a standard type represented by a formula 8;
Figure GDA0002246836620000064
Figure GDA0002246836620000065
1043. the power distribution matrix is expanded and transposed by rows to obtain p ═ p (p)11,p12,…p1s,p21,p22,…p2s,pn1,pn2,…pns)TIt is clear that the vector p is an n × s dimensional vector;
1044. the constraint C1 is rewritten and can be converted into a matrix representation by
4.4.1: let s-dimensional vector m be all 1 row vectors and s-dimensional vector o be all 0 row vectors, i.e.:
Figure GDA0002246836620000066
4.4.2: let n hierarchical matrix A' be:
Figure GDA0002246836620000067
since the vectors m and o are both s-dimensional row vectors, the matrix a' is actually a matrix of n rows, n × s columns;
4.4.3: let n dimensional column vector b ═ (-1, -1, … -1)TThus, the C1 condition can be expressed as:
a 'p is not less than b' formula 11;
1045. the constraint C2 is rewritten by:
4.5.1: let matrix A' be an nxs order identity matrix:
Figure GDA0002246836620000071
4.5.2: let the n × s dimensional column vector b ″ ═ (0,0, …,0)TThus, constraint C2 may be normalized as:
formula 13, where A 'p is greater than or equal to b';
1046. the two constraints of C1 and C2 are integrated to re-express the constraint
4.6.1: let matrix A be:
Figure GDA0002246836620000072
4.6.2: the column vector b is:
b＝(b`,b``)T equation 15;
4.6.3 the constraint can be found to be:
ap is not less than b formula 16;
wherein matrix A is an n × (s +1) row, n × s column matrix, and the dimension of column vector b is n × (s +1)
1047. Restating the constraint optimization problem;
Figure GDA0002246836620000073
st:Ap≥b；
1048. setting a solving threshold value epsilon, setting the iteration number i to be 0, and simultaneously selecting one feasible solution p of the formula 17x＝p(0)And make it satisfy
Figure GDA0002246836620000081
And is
Figure GDA0002246836620000082
Figure GDA0002246836620000083
1049. Calculating partial derivatives of an objective function
Figure GDA0002246836620000084
Wherein
Figure GDA0002246836620000085
Figure GDA0002246836620000086
And
Figure GDA0002246836620000087
10410. solving a linear programming problem:
Figure GDA0002246836620000088
Figure GDA0002246836620000089
obtaining an optimal solution d;
10411. the construction can be in the descending direction if
Figure GDA00022468366200000810
Stopping iteration and outputting p(i)Turning 10414; otherwise, according to the feasible descending direction d, the step 10412 is carried out;
10412. let p be(i+1)＝p(i)+λkd(i)Wherein λ isi＝μ*λmax，
Figure GDA00022468366200000811
Mu is a real number between 0 and 1 as the maximum iteration step length;
10413. i is i +1, and the process returns to step 1049;
10414. outputting the optimal power allocation base p under the current channel allocation(i)And outputting the current energy efficiency ratio f (p) as a function of the individual adaptive value.
In a second aspect, the present invention further provides a joint resource allocation apparatus, including:
memory, a processor, a bus and a computer program stored on the memory and executable on the processor, which when executed performs the method according to any of the first aspect.
(III) advantageous effects
The invention has the beneficial effects that: different from the prior method in which each user equipment only can use one communication channel, each user equipment in the invention has the capability of simultaneously aggregating C different channels, and provides a joint optimization model based on energy efficiency ratio based on an aggregation model. And on the basis, a combined resource allocation method based on a genetic algorithm is provided, the method adopts an integer coding mode to represent a channel allocation scheme in an optimization model, and adopts a Zoutendijk (Zoutendijk) feasible direction method to solve the power of all individuals in a population so as to find an optimal power allocation scheme corresponding to the current channel allocation. Finally, through the iterative optimization capability of the genetic algorithm, the optimal power distribution value under the optimal channel distribution condition can be continuously searched, so that the power consumption of the network is minimized on the premise of meeting the communication rate of the user equipment, and the excellent joint optimization performance is obtained.
Drawings
Fig. 1 is a schematic flowchart of a joint resource allocation method based on channel aggregation in a heterogeneous network according to the present invention;
FIG. 2 is a schematic illustration of the convergence analysis of the method of the present invention in an experimental example;
FIG. 3 is a diagram illustrating the system power comparison of the method of the present invention at different rates in an experimental example.
Detailed Description
For the purpose of better explaining the present invention and to facilitate understanding, the present invention will be described in detail by way of specific embodiments with reference to the accompanying drawings.
Examples
As shown in fig. 1, fig. 1 is a schematic diagram illustrating a joint resource allocation method based on channel aggregation according to the present embodiment.
It should be noted that resource allocation in the heterogeneous network is an NP-hard problem, and a genetic algorithm may be used for solving in this embodiment. Firstly, modeling is needed to be carried out on the problem of joint resource allocation based on spectrum aggregation, and a joint optimization model based on spectrum aggregation is constructed according to the position information and the speed requirements of all user equipment. Aiming at a combined optimization model based on spectrum aggregation in a heterogeneous network, an integer coding method of a genetic algorithm is designed to represent a channel allocation scheme in the optimization model, an integer coding value is used for representing an individual in a population, an optimal power allocation value corresponding to the channel coding is searched by adopting a Zoutendijk optimization method, and the obtained energy efficiency ratio function value is used as an individual fitness value. And (3) solving the combined optimization model based on the spectrum aggregation through the iteration of 103 and 106, wherein the correspondingly obtained individual coding scheme is a channel allocation scheme of the optimization model, the correspondingly obtained individual fitness evaluation value is an optimal energy efficiency ratio, and the correspondingly obtained Zoutendijk optimization solution result is a power allocation value of the optimization model. The execution main body is a master control wireless resource management unit of the system and manages all heterogeneous base stations in a unified way.
The method shown in fig. 1 comprises the following steps:
and Step1, acquiring the position information of each user equipment, and constructing a spectrum aggregation-based joint optimization model based on the position information.
It should be noted that the number of acquired ues cannot exceed the sum of the number of channels of the entire base station. For example, there are N0 total bss, each bs has S channels, and the total number of users cannot exceed N0 × S.
Step2, adopting an integer coding method, taking a feasible channel allocation scheme as the representation of the individuals in the genetic algorithm, and randomly initializing m individuals as the initial population G of the genetic algorithm.
In this embodiment, the radio resource management module of the base station is implemented by using a resource allocation method based on a genetic algorithm. The genetic algorithm adopts a population mode to carry out iterative optimization. The population consists of m individuals, each individual representing a feasible channel allocation scheme within the base station.
And Step3, performing crossover and mutation genetic operation on the channel allocation scheme represented by each individual, and generating a new population with the number of individuals of 2 m.
Step4, based on the channel allocation represented by each individual in the new population, finding the optimal power allocation method corresponding to the channel allocation by using a Zoutendijk (Zoutendijk) feasible direction method, and taking the energy efficiency ratio of power to speed as the fitness evaluation value (i.e. the energy efficiency ratio of power to speed) of the individual.
And Step5, performing deterministic selection operation on the population based on the spectrum aggregation combined optimization model, sorting according to the fitness, sequentially selecting m individuals with the lowest power consumption ratio, and storing until the next iteration, namely as a new generation of population G.
And Step6, repeating the steps of Step3-Step5 until the iteration reaches the upper limit of the iteration number.
And Step7, returning the optimal individual with the lowest power consumption ratio (namely the optimal individual with the minimum fitness evaluation value), taking the channel allocation of the individual as a channel allocation scheme of the whole network, and taking the power allocation scheme corresponding to the individual as a power allocation scheme of the whole network.
1. Model construction method
Wherein the model construction method in the step1 comprises
Step1.1, with pn,sRepresenting the percentage of power allocated by the base station N e N to the channel S e S (the normalized percentage of power for all base stations used in this embodiment), and constructing a power allocation constraint of
Figure GDA0002246836620000111
Where N is the set of all heterogeneous base stations in all single macro cells and S is the set of all channels in a base station, the constraint indicating that the power value allocated by each base station cannot exceed the maximum power of the base station. Heterogeneous networks are traditional single macro cells with many small base stations added.
Step1.2, in a sexual function An,s,kIndicating whether a certain channel s of a certain base station n is allocated to a certain user k,
Figure GDA0002246836620000121
constructing a channel allocation constraint of
Figure GDA0002246836620000122
Figure GDA0002246836620000123
Where K is the set of all users, the constraint indicates that each base station is occupied by at most one user per channel, and that each user is able to aggregate up to C available channels.
Step1.3, construction of the overall Transmission Rate for a Single user k
Figure GDA0002246836620000124
Wherein Dn,kRepresents the distance between user k and base station n, defined in the present invention as the Euclidean distance between the user k and base station n, BdRepresenting the bandwidth of the base station.
Step1.4, constructing an energy efficiency ratio model of the whole system as
Figure GDA0002246836620000125
s.t.(1),(3)～(4).
Where the power is logarithmic to conform to the dimension of the rate. The formula (6) is simultaneously constrained by the formulas 1, 3 and 4, that is, the allocated power value of each heterogeneous base station cannot exceed the maximum power of the base station, and each heterogeneous base station is occupied by at most one user equipment per channel, and each user equipment is capable of aggregating at most C available channels.
2. Population initialization method
Wherein the integer coding method in step2 is
Step2.1, abstractly representing the channel allocation scheme of the whole heterogeneous base station into a two-dimensional array, wherein different base stations are represented by rows and different channels are represented by columns in the two-dimensional array:
Figure GDA0002246836620000131
wherein, CijC represents the distribution state of the jth channel of the base station i, and the user number is K e {1,2,3, …, K }, thenijK denotes that the jth channel of the base station i is allocated to the kth user. Thus, CijE {0,1,2,3, …, K }, where C ij0 means that the channel is not allocated to any user, the channel is in an idle state, and obviously, the channel power allocation in the idle state is 0.
Step2.2, randomly assigning values from the set {0,1,2,3, …, K } for each element in C;
and Step2.3, repeating Step2.1-Step2.2 until m groups C are generated, and taking the generated m groups as the initial population of the genetic algorithm. The individual represents one feasible channel allocation scheme in the optimization model.
3. Crossover and mutation methods
Wherein the crossover and mutation operations in step3 are:
step3.1, two individuals A, B were randomly selected from the population G.
And Step3.2, generating random numbers in the range of (0,1), and if the random numbers are less than the crossing rate Pc, executing the crossing operation steps Step3.3-Step3.9.
Step3.3, replication of individual A and individual B, resulting in individual A ', individual B' as the parent.
Step3.4, selecting n numbers from channel numbers, namely the number of rows of a channel allocation scheme two-dimensional array C (namely an individual gene matrix), as cross point positions, and storing the cross point positions in C.
Step3.5, for each row i in the individual gene matrix:
step3.6, select intersection j in the row of genes.
Step3.7, and interchanging genes after the ith row gene position j in the genes allocated by the two parent individual channels.
Step3.8, similarly, the genes in the ith row, gene position j and thereafter, of the genes assigned to the power of the two parents were interchanged.
And Step3.9, repeating the steps Step3.4-3.8 until the whole gene matrix is traversed, finishing the gene crossing operation between the individual A and the individual B, wherein the crossed individual A and the crossed individual B are new filial generation individuals.
Step3.10, removing the newly obtained offspring individuals A and B from the population, and adding the offspring individuals A ' and B ' together with the parents individuals A ' and B ' into a new population G '.
Step3.11 for each individual in the newly generated population G' after crossing.
Step3.12, generating random numbers in the range of (0,1) for each row in the channel allocation matrix in the individual gene, namely the channel allocation sequence of each base station, and if the random numbers are less than the variation rate Pm, performing variation operation on the row of genes.
Step3.13, randomly selecting a gene position in the row, if the gene position is in an idle state, namely the value of the gene position is 0, randomly selecting a user in the range of the base station, and distributing a channel corresponding to the gene position to the user.
Step3.14, if the channel corresponding to the gene position is in a non-idle state, namely the value of the gene position is not 0, generating another random number p in the range of (0,1), and if p is less than 0.5, emptying the channel, namely setting the value of the gene position to 0; otherwise, another user is randomly selected in the range of the base station, and the channel corresponding to the gene position is allocated to the user.
Step3.15, repeating the steps step3.12-3.14 until all rows in the individual channel allocation gene matrix, that is, all base stations, have performed mutation operation, and then completing the individual mutation operation.
4. Power distribution method based on Zoutendijk feasible direction method optimization
Wherein the power distribution method in step4 is
Step4.1, based on the channel allocation result in the individual coding, simplifying the optimization model into
Figure GDA0002246836620000141
Figure GDA0002246836620000151
Wherein
Figure GDA0002246836620000152
Representing the power allocation matrix of the base station.
Step4.2, taking the inverse number and expressing the problem as a standard type
Figure GDA0002246836620000153
Step4.3, spreading the power distribution matrix by rows and transposing to obtain p ═ p (p)11,p12,…p1s,p21,p22,…p2s,pn1,pn2,,…pns)TIt is clear that the vector p is an n × s dimensional vector;
step4.4, rewriting the constraint C1, and converting it into a matrix representation by
Step4.4.1, let s-dimensional vector m be all 1 row vector, s-dimensional vector o be all 0 row vector, namely:
Figure GDA0002246836620000154
step4.4.2, let n hierarchical matrix A' be:
Figure GDA0002246836620000155
since the vectors m and o are both s-dimensional row vectors, the matrix A' is effectively a matrix of n rows, n x s columns.
Step4.4.3, let n dimensional column vector b ═ (-1, -1, … -1)TThus, the C1 condition can be expressed as:
A`p≥b` (11)
step4.5, rewriting constraint C2, the method comprises:
step4.5.1, setting the matrix A' as an n × s order unit matrix:
Figure GDA0002246836620000161
step4.5.2, let n × s dimensional column vector b ″) (0,0, …,0)TThus, constraint C2 may be normalized as:
A``p≥b`` (13)
step4.6, and combining the above two constraints of C1 and C2 to re-express the constraints
Step4.6.1, let matrix A be:
Figure GDA0002246836620000162
step4.6.2, column vector b:
b＝(b`,b``)T(15)
step4.6.3, the constraints can be found to be:
Ap≥b (16)
wherein matrix A is an n × (s +1) row, n × s column matrix, and the dimension of column vector b is n × (s +1)
Step4.7, restating constraint optimization problem
Figure GDA0002246836620000163
st:Ap≥b
Step4.8, setting a solving threshold value epsilon, setting the iteration number i to be 0, and simultaneously selecting the formula (17)
A feasible solution px＝p(0)And make it satisfy
Figure GDA0002246836620000164
And is
Figure GDA0002246836620000165
Figure GDA0002246836620000166
Step4.9, calculating partial derivative of objective function
Figure GDA0002246836620000167
Wherein
Figure GDA0002246836620000171
Figure GDA0002246836620000172
And
Figure GDA0002246836620000173
step 4.10, solving the linear programming problem
Figure GDA0002246836620000174
Figure GDA0002246836620000175
And obtaining an optimal solution d.
Step 4.11, construction feasible descending direction if
Figure GDA0002246836620000176
Stopping iteration and outputting p(i)Turning to Step4.14; otherwise, turning to Step4.12 according to the feasible descending direction d.
Step4.12, order p(i+1)＝p(i)+λkb(i)Wherein λ isi＝μ*λmax，
Figure GDA0002246836620000177
Mu is a real number between 0 and 1 as the maximum iteration step.
Step 4.13, i is i +1, and the process returns to the Step step4.9;
step4.14, outputting the optimal power distribution under the current channel distribution according to p(i)And outputting the current optimal energy efficiency ratio f (P) as an individual adaptive value function.
Examples of the experiments
In the present invention, the experimental specific parameter settings are shown in table 1:
TABLE 1 simulation parameters
Figure GDA0002246836620000181
Experiment 1 investigation of convergence of the algorithm
The convergence rate and convergence analysis is one of the important investigation targets of the meta-heuristic algorithm, and the quality of the convergence determines whether the algorithm can find a global suboptimal solution and the time required by optimization, so that the convergence rate and the convergence rate of the algorithm are tested and analyzed in the experiment to investigate the advantages of the algorithm in the performances such as the convergence rate and the like compared with the traditional algorithm. The algorithm simultaneously performs combined optimization on power and a channel, and does not adopt a Zoutendijk method to optimize power distribution. The results of the experiment are shown in FIG. 2. Therefore, the algorithm in the chapter has higher convergence speed and is more energy-saving in the experiment.
Experiment 2 examines the performance of the method of the present invention under different speed requirements, finds the power consumption of feasible solutions by comparing algorithms, and repeats the average value of the experiment results for 24 times, as shown in the comparative schematic diagram of the experiment results shown in fig. 2.
As can be seen directly from fig. 2, the method of the present invention can find a more optimal solution at different rates, and when the system rate reaches 108 mbit, and the conventional genetic algorithm cannot search a suboptimal solution, the method of the present invention can still find a feasible solution.
It is to be understood that the invention is not limited to the specific arrangements and instrumentality described above and shown in the drawings. A detailed description of known methods is omitted herein for the sake of brevity. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present invention are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications and additions or change the order between the steps after comprehending the spirit of the present invention.
It should also be noted that the exemplary embodiments mentioned in this patent describe some methods or systems based on a series of steps or devices. However, the present invention is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be performed in an order different from the order in the embodiments, or may be performed simultaneously.
Finally, it should be noted that: the above-mentioned embodiments are only used for illustrating the technical solution of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (6)

1. A joint resource allocation method based on channel aggregation in a heterogeneous network is characterized by comprising the following steps:
101. acquiring the position information of N user equipment, and constructing a combined optimization model based on spectrum aggregation according to the position information of all the user equipment;
102. randomly initializing m individuals by adopting an integer coding method, and taking the m individuals as an initial population G of a genetic algorithm;
wherein each individual is a feasible channel allocation scheme;
103. performing crossover and mutation genetic operations on each individual to generate a new population G1, wherein the number of individuals of the new population G1 is 2 m;
104. aiming at each individual in the new population G1, searching an optimal power distribution method corresponding to the channel distribution of the current individual, and taking the energy efficiency ratio of power and rate as the fitness evaluation value of the current individual;
105. sorting the individuals in the new population G1 from large to small according to the fitness evaluation value, and selecting the last m individuals in the sorting as the population used in the next iteration;
106. based on the population used in the next iteration, the process of step 103-105 is repeated until the iteration reaches the preset upper limit of the iteration times;
107. acquiring an optimal individual with the minimum fitness evaluation value, and taking the channel allocation of the acquired optimal individual as a channel allocation scheme of the whole network in the heterogeneous network and a power allocation scheme corresponding to the optimal individual as a power allocation scheme of the whole network in the heterogeneous network;
wherein the channel allocation scheme and the power allocation scheme are schemes obtained by processing the constructed joint optimization model by adopting a genetic algorithm;
wherein the step 101 comprises:
1011. with pn,sExpressing the percentage of the power distributed to the channel S by the base station N for N for S, and constructing the power distribution constraint of a formula 1;
Figure FDA0002369720210000011
wherein N is the set of all heterogeneous base stations in all single macro cells, S is the set of all channels in each heterogeneous base station, and the power distribution constraint indicates that the power value distributed by each heterogeneous base station cannot exceed the maximum power of the base station;
1012. with an indicative function An,s,kIndicating whether a certain channel s of a certain heterogeneous base station n is allocated to a certain user equipment k,
Figure FDA0002369720210000021
the channel allocation constraints are constructed as:
Figure FDA0002369720210000022
Figure FDA0002369720210000023
wherein K is a set of all user equipments, and the channel allocation constraint indicates that each channel of each heterogeneous base station is occupied by at most one user equipment, and each user equipment can aggregate at most C available channels;
1013. constructing an overall transmission rate R for a single user kk：
Figure FDA0002369720210000024
Wherein Dn,kRepresenting the Euclidean distance, B, between the user equipment k and the heterogeneous base station ndRepresenting the bandwidth of the base station, B the total bandwidth, α the link loss factor, Pm,sThe percentage of power allocated to channel s for base station m (m ≠ n);
Dm,kindicating the Euclidean distance between the user equipment k and the heterogeneous base station m, wherein m is not equal to n;
1014. the energy efficiency ratio model of the whole heterogeneous network is constructed by
Figure FDA0002369720210000025
Equation 6 is constrained by equations 1, 3, and 4.
2. The method of claim 1, wherein the step 102 comprises:
1021. the channel allocation scheme of the whole heterogeneous base station is abstractly expressed as a two-dimensional array C, in the two-dimensional array, different heterogeneous base stations are expressed by rows, and different channels are expressed by columns:
Figure FDA0002369720210000031
wherein, CijC represents the distribution state of the jth channel of the base station i, and the number of the user equipment is K e {1,2,3, …, K }, thenijK denotes that the jth channel of the base station i is allocated to the kth user equipment; thus, CijE {0,1,2,3, …, K }, where Cij0 means that the channel is not allocated to any user equipment, the channel is in an idle state, and obviously, the channel power allocation in the idle state is 0;
1022. randomly assigning a value from the set {0,1,2,3, …, K } for each element in C;
1023. substep 1021 and substep 1022 are repeated until m tuples C are generated, and the generated m tuples are used as an initial population of the genetic algorithm.
3. The method of claim 2, wherein the step 103 comprises:
1031. randomly selecting two individuals A and B from an initial population G;
1032. generating random numbers in the range of (0,1), and if the random numbers are less than the crossover rate Pc, performing crossover operation steps 1033-1039;
1033. replicating individual a and individual B to produce individual a ', individual B' as a parent individual;
1034. selecting n numbers from the row number of the channel allocation scheme two-dimensional array C as cross point positions, and storing the cross point positions in the C;
1035. for each row i in the channel allocation scheme two-dimensional array C:
1036. selecting a cross point j in the row of genes;
1037. interchanging genes behind the ith row gene position j in the genes distributed by the channels of the two parents;
1038. interchanging genes behind the ith row gene position j in the genes distributed by the power of the two parent individuals;
1039. repeating the steps 1034 and 1038 until the whole two-dimensional array C is traversed, completing the cross operation of the genes between the individual A and the individual B, wherein the crossed individual A and the individual B are new filial generation individuals;
10310 removing newly obtained offspring individuals A and B from the population, and adding the offspring individuals A ' and B ' together with the father individuals A ' and B ' into a new population G ';
10311. for each individual in the population G' newly generated after crossing;
10312. generating random numbers within the range of 0-1 for each row of the two-dimensional array C, namely a channel allocation sequence of each base station, and if the random numbers are less than the variation rate Pm, performing variation operation on the row of the base stations;
10313. randomly selecting a gene position in the row, if the gene position is in an idle state, namely the value of the gene position is 0, randomly selecting a user device in the range of the base station, and distributing a channel corresponding to the gene position to the selected user device;
10314. if the channel corresponding to the gene position is in a non-idle state, generating another random number p within the range of 0-1, and if p is less than 0.5, emptying the channel corresponding to the gene position; otherwise, randomly selecting another user equipment within the range of the base station, and distributing the channel corresponding to the gene position to the selected user equipment;
10315. and repeating steps 10312 and 10314 until all the base stations complete the mutation operation, and then completing the mutation operation of the individual.
4. The method of claim 3, wherein the step 104 comprises:
aiming at each individual in the new population G1, an optimal power distribution method corresponding to the channel distribution of the current individual is found by using the Zotendijk feasible direction method, and the energy efficiency ratio of power and speed is used as the fitness evaluation value of the current individual.
5. The method of claim 4, wherein the step 104 comprises:
1041. based on the channel allocation result in the individual coding, the energy efficiency ratio model in the simplified formula 6 is:
Figure FDA0002369720210000051
Figure FDA0002369720210000052
wherein
Figure FDA0002369720210000053
A power allocation matrix representing a base station;
1042. taking an inverse number from the formula 7, and converting the inverse number into a standard type represented by a formula 8;
Figure FDA0002369720210000054
Figure FDA0002369720210000055
1043. the power distribution matrix is expanded and transposed by rows to obtain p ═ p (p)11,p12,…p1s,p21,p22,…p2s,pn1,pn2,…pns)TIt is clear that the vector p is an n × s dimensional vector;
1044. the constraint C1 is rewritten and can be converted into a matrix representation by
4.4.1: let s-dimensional vector m be all 1 row vectors and s-dimensional vector o be all 0 row vectors, i.e.:
Figure FDA0002369720210000061
4.4.2: let n hierarchical matrix A' be:
Figure FDA0002369720210000062
since the vectors m and o are both s-dimensional row vectors, the matrix a' is actually a matrix of n rows, n × s columns;
4.4.3: let n dimensional column vector b ═ (-1, -1, … -1)TThus, the C1 condition can be expressed as:
a 'p is not less than b' formula 11;
1045. the constraint C2 is rewritten by:
4.5.1: let matrix A' be an nxs order identity matrix:
Figure FDA0002369720210000063
4.5.2: let the n × s dimensional column vector b ″ ═ (0,0, …,0)TThus, constraint C2 may be normalized as:
formula 13, where A 'p is greater than or equal to b';
1046. the two constraints of C1 and C2 are integrated to re-express the constraint
4.6.1: let matrix A be:
Figure FDA0002369720210000064
4.6.2: the column vector b is:
b＝(b`,b``)Tequation 15;
4.6.3 the constraint can be found to be:
ap is not less than b formula 16;
the matrix A is an n x (s +1) row matrix, an n x s column matrix, the dimensionality of a column vector b is n x (s +1)1047, and a constraint optimization problem is expressed again;
Figure FDA0002369720210000071
st:Ap≥b；
1048. setting a solving threshold value epsilon, setting the iteration number i to be 0, and simultaneously selecting one feasible solution p of the formula 17x＝p(0)And make it satisfy
Figure FDA0002369720210000072
And is
Figure FDA0002369720210000073
Figure FDA0002369720210000074
Wherein epsilon is a threshold value;
1049. calculating partial derivatives of an objective function
Figure FDA0002369720210000075
Wherein
Figure FDA0002369720210000076
Figure FDA0002369720210000077
And
Figure FDA0002369720210000078
10410. solving a linear programming problem:
Figure FDA0002369720210000079
Figure FDA00023697202100000710
obtaining an optimal solution d;
10411. the construction can be in the descending direction if
Figure FDA00023697202100000711
Stopping iteration and outputting p(i)Turning 10414; otherwise, according to the feasible descending direction d, the step 10412 is carried out;
10412. let p be(i+1)＝p(i)+λkd(i)Wherein λ isi＝μ*λmax，
Figure FDA0002369720210000081
Mu is a real number between 0 and 1 as the maximum iteration step length;
10413. i is i +1, and the process returns to step 1049;
10414. outputting the optimal power allocation base p under the current channel allocation(i)And outputting the current energy efficiency ratio f (p) as a function of the individual adaptive value.
6. An integrated resource distribution apparatus, comprising:
memory, processor, bus and computer program stored on the memory and executable on the processor, which when executing the program implements the method according to any of claims 1 to 5.
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