CN107070583B - A kind of efficiency optimization method of heterogeneous network enhancement type district interference coordination - Google Patents

A kind of efficiency optimization method of heterogeneous network enhancement type district interference coordination Download PDF

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CN107070583B
CN107070583B CN201710463799.1A CN201710463799A CN107070583B CN 107070583 B CN107070583 B CN 107070583B CN 201710463799 A CN201710463799 A CN 201710463799A CN 107070583 B CN107070583 B CN 107070583B
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base station
user
abs
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efficiency
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CN107070583A (en
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郑杰
高岭
王海
杨旭东
张晓�
任杰
高全力
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Northwest University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04J—MULTIPLEX COMMUNICATION
    • H04J11/00—Orthogonal multiplex systems, e.g. using WALSH codes
    • H04J11/0023—Interference mitigation or co-ordination
    • H04J11/005—Interference mitigation or co-ordination of intercell interference
    • H04J11/0056—Inter-base station aspects
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/02—Resource partitioning among network components, e.g. reuse partitioning
    • H04W16/10—Dynamic resource partitioning
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/54—Allocation or scheduling criteria for wireless resources based on quality criteria
    • H04W72/541—Allocation or scheduling criteria for wireless resources based on quality criteria using the level of interference
    • 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)
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Abstract

A kind of efficiency optimization method of heterogeneous network enhancement type district interference coordination, 3GPP proposes the Inter-Cell Interference Coordination (eICIC) of enhancing, it is intended to keep the silent interference reduced to low power base station (such as microcellulor, Pico cell etc.) in the subframe (ABS) of almost blank by macrocellular.Energy efficiency (Energy-Efficient, EE) is very crucial for the deployment of the densification of low power nodes.In the present invention, EE-eICIC algorithm accesses come the efficiency distribution for solving almost blank subframe (ABS) and the efficiency of user equipment, since the optimization problem of modeling is a non-smooth and mixed-integer problem, proposes a kind of sub-optimal algorithm.The sub-optimal algorithm uses broad sense fractional programming theory and convex optimum theory, is rounded algorithm by iteration and relaxation to solve.Simulation result shows that EE-eICIC algorithm all has preferable performance in terms of system and the efficiency of user.The present invention can be used for realization of the practical isomery cellular network based on the load distribution of eICIC efficiency.

Description

A kind of efficiency optimization method of heterogeneous network enhancement type district interference coordination
Technical field
The invention belongs to cordless communication network technical field, relate generally in isomery cellular network based on enhancement type district The efficiency optimization problem of interference coordination (eICIC), and in particular to eICIC almost blank subframe (ABS) distribution, user access and The implementation method of user's time-domain resource co-allocation.
Background technique
In order to meet the needs of wireless data rapid growth, advanced evolution (LTE-A) defines in existing height 4G for a long time Low power nodes (such as Home eNodeB or micro-base station) is disposed under power macrocellular network.Due to isomery cellular network (HetNets) there are biggish differences, especially macrocellular to have than microcellulor for transmission power between the base station in downlink (BS) With bigger overlay area.Intensive isomery cellular network can be improved in load balancing by assigning the user to microcellulor (HetNet) capacity.It is disposed in addition, the interference of macro base station high power and intensive microcellulor will seriously limit 5G heterogeneous network The application of scene system.
In order to reduce the interference of macrocellular, 3GPP standard proposes the concept of enhancing Inter-Cell Interference Coordination (eICIC), macro Honeycomb is by keeping the silent interference reduced to microcellulor of downlink in part subframe, referred to as almost blank subframe (almost blank subframes, ABS).There are two important features in eICIC: user's access problem and almost blank The distribution of subframe (ABS).Firstly, user is distributed to macrocellular or microcellulor, it is ensured that microcellulor owes utilization or excessively benefit With.Secondly, reducing the interference of macrocellular in the interference coordination of time domain by macrocellular and microcellulor to assist microcellulor to transmit. It therefore, will be by less interference, so that the user of access microcellulor obtains higher biography in ABS subframe for microcellulor Defeated rate.
EICIC allocation problem is combined with user's access, i.e. ABS and user's access determine the available resource in base station and bear Carry distribution.Most of existing researchs focus principally on different dynamic ABS allocation plans, it is main consider macrocellular and microcellulor it Between load distribute corresponding dynamic change.But concern is primarily with the capacity of system and handling capacities, have ignored system Energy efficiency (EE).And the same layer for only considering that ABS configuration is not able to satisfy when microcellulor quantity is larger, between microcellulor is dry It disturbs.Some studies pointed out that different layers be arranged different biasing (bias) rule be not for load balancing and energy conservation it is optimal, be based on User's access of efficiency optimization and the load balancing based on interference management are different.It should be total in following isomery cellular network With consideration interference management and energy optimization.Therefore, it is necessary to study that EE-eICIC (Energy Efficiency how is arranged Optimization Enhanced Inter-cell Interference Coordination) configuration parameter, joint use Family access is energy-optimised with ABS distribution, these specifications are also not yet provided in eICIC standard.
Summary of the invention
In order to overcome the above-mentioned deficiencies of the prior art, the purpose of the present invention is to propose to a kind of heterogeneous network enhancement type districts Between interference coordination efficiency optimization method.While the capacity and energy efficiency of lifting system, macrocellular and pico- bee are kept Load balancing between nest proposes iterative algorithm by fractional programming theory first, then by simplifying user's access and ABS Assignment problem further decreases calculating cost using the relaxation and rounding algorithm of two steps.Numerical result of the invention demonstrates The constringency performance of the program, under certain and rate cost, the available significant improvement of the energy efficiency of system and user.
To achieve the goals above, the technical solution adopted by the present invention is that:
A kind of efficiency optimization method of heterogeneous network enhancement type district interference coordination, which is characterized in that including following step It is rapid:
1) a double-deck TDD-LTE isomery beehive network system being made of macrocellular and microcellulor is constructed, wherein user It only can choose one base station of access: macro base station or micro-base station；
2) in the region of each macro base station covering, micro-base station in the area and user measure channel status and Disturbance state, sends it to macro base station, and macro base station carries out unified scheduling of resource after calculating；
3) the downlink signal intensity that user receives according to whole bandwidth, select a macrocellular and a microcellulor as Candidate collection, then the iteration that is optimized by efficiency, relaxation be converted into convex programming problem, be rounded for former problem feasible solution i.e. referred to as Iteration-relaxation-rounding algorithm, and then select which base station accessed；
4) initialization of efficiency Optimized Iterative algorithm, Select Error ε > 0 and maximum the number of iterations K are carried out firstmaxIf It sets in EEWith the number of iterations k=0；
5) efficiency Optimized Iterative algorithm is executed, as k≤KmaxAnd convergence=false, given for oneSolve problems OP2 obtains in EE-eICIC the almost allocation strategy of blank subframe (ABS)IfThen convergence=true indicates convergence, returns to the optimal ABS subframe distribution of EE-eICIC StrategyWith it is maximumOtherwise it is arrangedWith enable k=k+1；
6) macro base is calculated according to the iteration-relaxation-rounding algorithm in the configuration EE-ABS subframe agreement The EE-ABS number of subframes used by its micro-base station interfered can be supplied to by standing；
7) according to the relaxation-rounding algorithm, the transmission time resource of allocation of downlink, and it is average to obtain user's downlink Transmission time determines the transmission rate and energy consumption of user, calculates user and system energy efficiency；
8) after calculating, the result notice of resource management and scheduling is given to each micro-base station and user, thus each use Family carries out access and data transmission based on efficiency.
The step 4), step 5), step 6), iteration-relaxation-rounding algorithm early-stage preparations used in step 7) And its process is as follows:
A, establish SINR model, the same base station only exists three classes interference using time division multiplexing in cell: macro base station with it is macro Interference between base station, macro base station and micro-base station and micro-base station and micro-base station obtains SINR model according to interference and ABS agreement；
B, according to SINR model, using the available use of corresponding table of SINR in Shannon capacity formula or LTE and rate The average transmission rate r at familyu；
C, the efficiency Optimized model based on eICIC is established；
And then establish primal problem (OP1, Optimization Problem1), OP1 be with formula (1) rate and energy consumption it The efficiency of ratio is optimized for target and the optimization problem of constraint condition (2)-(10) composition is as follows
xu(yu,A+yu,nA)=0 (4)
xu≥0,yu,A≥0,yu,nA≥0 (9)
In formula, N+Indicate the set of nonnegative integer.
D, since constraint formula (4) and formula (10), OP1 are a mixed integer programming problems, however, even if eliminating constraint formula (4) and nonnegative real number R is arrived in relaxation formula (10)+, but due to the nonconvex property of objective function, OP1 is not still convex problem, first with Fractional programming is re-converted into optimization problem OP2 to the optimization problem of OP1；
s.t.(2)-(10)
E, the optimization problem OP2 after the conversion solved by the iterative algorithm of EE-eICIC described in right 1, including it is following Step:
1. Select Error ε > 0 and maximum the number of iterations Kmax；
2. being arranged in EEWith the number of iterations k=0；
3. as k≤KmaxAnd convergence=false；
Given for oneSolve problems OP2 obtains in EE-eICIC the almost distribution plan of blank subframe (ABS) SlightlyIfOptimal value is then converged to, the optimal ABS subframe allocation strategy of EE-eICIC is returnedWith it is maximumOtherwise it is arrangedWith enable k=k+1, continue to run step 3.；
F, relaxation-rounding algorithm solves OP2；
, convenient for solving, then, pass through for nonnegative real number to obtain convex programming problem firstly, integer variable can relax Floor operation obtains the feasible solution of OP3, and detailed description are as follows；
3) relaxation: OP2 is relaxed and is solved for OP3；By ignoring N in constraint formula (4) and loose constraint formula (10)mWith ApOn feas ible space, obtain OP3, after relaxed constraints formula (10), NmAnd ApNonnegative real number can be taken, removes constraint formula (6) to original The influence of beginning problem OP1 essentially consists in user and receives radio resource from macrocellular and microcellulor simultaneously in downlink transmission, For optimized variableOP3 modeling are as follows:
S.t. (2)-(3) and (5)-(9)
Wherein R+It is the set of nonnegative real number.
4) be rounded: in second step, to first step solution as a result, being suitably rounded, progress floor operation obtains OP2 Feasible solution.
Floor operation detailed step in the step F is as follows:
1. being rounded NmAnd Ap: by NmAnd ApInteger is become using following operation from positive real number:
Wherein N'mAnd A'pFor the output of algorithm 1.
2. calculating user from the available downlink transmission rate of macro base station and energy consumption:
With
3. calculating user from the available downlink transmission rate of micro-base station and energy consumption:
With
WhereinWithFor the output of algorithm 1.
4. calculating the efficiency of userWithCompare energy of the user from macro base station and micro-base station Effect, ifThe then access macro base station of user, otherwise user accesses micro-base station.
5. calculating each macro base station and micro-base station, the utilization rate of its downlink is calculated.
XmIndicate the non-ABS utilization rate of macro base station, Yp,AAnd Yp,nAIndicate the utilization rate of the ABS and non-ABS of micro-base station,It is the user's set for accessing macro base station,Indicate the set of access micro-base station.
6. calculating each user, the time that downlink can be transmitted.
7. calculating the user's energy consumption and available transmission rate of access macro base station:
With
8. calculating the user's energy consumption and available transmission rate of access micro-base station:
With
9. calculating user's efficiency:
SINR concrete model in the step B is as follows:
For accessing the user of micro-base station, the downlink SINR modeling of user u are as follows:
For accessing the user of macro base station, the downlink SINR of user u:
Then, the average transmission rate of user u is obtained according to SINR model:
In formula, PRx(u) power arrived for user u downlink reception；Ppico(u) dry for the downlink by other micro-base stations It disturbs；Pmacro(u) for by the interference of the downlink of other macro base stations；For the downlink obtained in non-ABS subframe from macro base station Transmission rate；For the downlink transmission rate obtained in ABS subframe from micro-base station；For in non-ABS subframe from micro- base Stand obtain downlink transmission rate.
Each optimized variable, major parameter and constraint specifically describe such as in the primal problem OP1 established in the step C Under:
Constraint (2) indicates the time scale that the transmission rate of user is no more than obtained from macrocellular or microcellulor, and The minimum-rate requirement of user；
Constraining (3) indicates, the energy consumption of user cannot be greater than the time scale obtained from macrocellular or microcellulor, Yi Jiyong The maximum power at family limits；
Constraint (4) indicates that user is only capable of accessing single macrocellular or microcellulor, and cannot access simultaneously；
Constraining (5) indicates in interference set IBSMiddle ABS subframe and non-ABS subframe sum limit, wherein IBS, BS ∈ { macro, pico } indicates the set of all interference microcellulors and all interference macrocellulars；
Constraint (6) indicates that the time scale that user's subframe is distributed to from macrocellular is less than total non-ABS number of sub frames Nm；
Constraint (7) indicates that the ABS subframe time ratio that user is distributed to from microcellulor is less than total ABS subframe Ap；
Constraint (8) indicate from microcellulor distribute to user subframe time ratio be less than total ABS period Nsf, also illustrate that from The number of sub frames that macrocellular or microcellulor distribute to user is less than total available number of sub frames；
Wherein, U is the set of user u；M is the acronym of macro base station macro；M is the set of the m of macro base station；P is The acronym of micro-base station pico；P is the set of micro-base station p；muFor the user's set for accessing macro base station；puTo access micro- base The user's set stood；NsfThe number of sub frames for including for the period of ABS, an ABS frame；NmThe non-ABS that can be used for macro base station Subframe, i.e. macro base station can carry out the number of sub frames of downlink transfer；ApFor the available ABS subframe of micro-base station, macro base station keeps downlink It is silent；xuFor muDownlink times of the middle user in non-ABS；yu,AFor puDownlink times of the middle user in ABS； yu,nAFor puDownlink times of the middle user in ABS；yu,nAFor puDownlink times of the middle user in non-ABS； For the transimission power of macro base station；For the transimission power of macro base station broadcast singal；For the transimission power of micro-base station；The maximal received power allowed for user.
The beneficial effects of the present invention are:
Compared with the existing Access Algorithm based on ABS, the present invention considers the smallest rate limit of user first, with efficiency Maximum turns to optimization aim, is modeled as efficiency optimization problem.Secondly, by considering base station to base station interference figure and network topology Structure proposes the efficiency optimization mixed integer nonlinear programming problem of a kind of joint ABS distribution and user's access.Third uses Broad sense fractional programming theory and convex optimum theory, the hair method that the iteration and relaxation for having invented a kind of simplification are rounded.Finally, of the invention Using certain and rate as cost, the available significant improvement in terms of system and the energy efficiency of user.
Detailed description of the invention
Fig. 1 is the interference model of eICIC in isomery cellular network of the invention；
Fig. 2 is the ABS frame structure of eICIC in isomery cellular network of the invention；
Fig. 3 is the number of iterations of EE-eICIC in isomery cellular network of the invention；
Fig. 4 is the situation of change of energy efficiency of the invention with number of users；
Fig. 5 is the situation of change of energy efficiency of the invention with microcellulor number；
Fig. 6 is energy efficiency of the invention in network, the comparison of best user and worst user；
Fig. 7 is capacity of the invention in network, the comparison of best user and worst user；
Fig. 8 is the situation of change of optimal almost blank subframe ratio of the invention with microcellulor number.
Specific embodiment
The present invention is further discussed below below in conjunction with attached drawing:
Consider two layers of isomery beehive network system of TDD-LTE, wherein the subframe of eICIC and ABS can be with dynamic configurations.UE It refers to user equipment (for example, mobile device), and evolved base station (eNodeB) executes the wireless resource management of base station, connects Receive control and other dispatch coordination functions.
User model: for user's downlink transmission, by UE according to the received signal strength measured in whole bandwidth come Determine the optimal candidate base station set of macrocellular and microcellulor.Assuming that base station uses maximum transmission power, and user is according to including The received signal strength of all base stations of macrocellular and microcellulor in whole bandwidth, which determines, accesses that base station.Therefore, this hair It is bright to be optimized from energy efficiency of the worst angle to eICIC.
Interference model: dl interference is divided into macrocellular and macrocellular and interfered by SINR in order to obtain, macrocellular with The interference three types of the interference of microcellulor, microcellulor and microcellulor, as shown in Figure 1.The interference of macrocellular and macrocellular be by 1:1 channeling in most of LTE networks causes.For the double-deck TDD-LTE, macrocellular and microcellulor use identical band Width, there are the interference of macrocellular and microcellulor.Microcellulor using identical channel there is also interfering with each other, i.e. microcellulor and micro- bee The interference of nest.
EICIC ABS: the scene that macrocellular and microcellulor coexist, the eICIC of proposition are mainly used for macrocellular and micro- Interference coordination between honeycomb.In order to assist microcellulor downlink transmission, macrocellular almost blank subframe holding under Line link is silent, only Broadcasting Control signal.In this invention, the present invention designs the ABS configuration of EE-eICIC, improves ABS's Efficiency utilization rate, and determine user's access from the angle of efficiency and distribute to the subframe of user, as shown in Figure 2.
SINR model: it is based on interference model, obtains SINR model.Then being averaged for user is calculated by Shannon capacity formula Transmission rate.It, can be by being greater than given threshold value or physics from signal strength received between base station for inter base station interference figure Distance is determined.
Such as following table of symbol used in SINR model
Since user only accesses single macrocellular or microcellulor in the downlink, and macrocellular and micro- cannot be accessed simultaneously Honeycomb.Therefore, user can be classified are as follows: access the user of macrocellular and user's two types of access microcellulor.For connecing User cellular in a subtle way, user can transmit on ABS and non-ABS.For accessing the user of microcellulor, interference is from micro- Honeycomb is rather than from macrocellular, because macrocellular downlink in ABS keeps silent.For accessing the user of macrocellular, User is only transmitted on ABS, wherein interfering mainly from microcellulor and macrocellular.Therefore, the SINR of user is available.
For accessing the user of micro-base station, the downlink SINR of user u:
For accessing the user of macro base station, the downlink SINR of user u:
Then, the average transmission rate of user u is obtained according to SINR model.
Optimized variable and major parameter are expressed as follows:
The optimization problem of modeling: the present invention models in terms of three
1) the efficiency access of user's downlink, i.e., from the angle of efficiency, user accesses best macrocellular or microcellulor.
2) optimal EE-ABS distribution, i.e., the microcellulor being interfered in each macrocellular are distributed by interference coordination ABS reduces interference of the microcellulor by macrocellular, to improve the efficiency of system.
3) average transmission time of user, i.e., the time scale of each ABS period allocated user's subframe, determines each ABS User's transmission rate and energy consumption in period.
Consider the minimum-rate requirement of user, the efficiency and then foundation optimization primal problem of maximization network of the present invention (OP1, Optimization Problem1), wherein optimized variablePass through optimization Primal problem OP1 obtains EE-eICIC algorithm.
xu(yu,A+yu,nA)=0 (4)
xu≥0,yu,A≥0,yu,nA≥0 (9)
Wherein N+Indicate the set of nonnegative integer.
Constraint (2) indicates the time scale that the transmission rate of user is no more than obtained from macrocellular or microcellulor, and The minimum-rate requirement of user.Constraining (3) indicates, the energy consumption of user cannot be greater than the time obtained from macrocellular or microcellulor Ratio and the limitation of the maximum power of user.Constraint (4) indicates that user is only capable of accessing single macrocellular or microcellulor, and cannot It accesses simultaneously.Constraining (5) indicates in interference set IBSMiddle ABS subframe and non-ABS subframe sum limit, wherein IBS, BS ∈ { macro, pico } indicates the set of all interference microcellulors and all interference macrocellulars.Constraining (6) indicates to distribute from macrocellular It is less than total non-ABS number of sub frames N to the time scale of user's subframem.Constraint (7) indicates to distribute to user's from microcellulor ABS subframe time ratio is less than total ABS subframe Ap.Constraint (8) indicates the subframe time ratio that user is distributed to from microcellulor Less than total ABS period Nsf, also illustrate that the number of sub frames for distributing to user from macrocellular or microcellulor is less than total available number of sub frames.
Remarks 1: constraint (4) ensures that user only accesses single macrocellular or microcellulor, then OP1 is integer programming problem.This Outside, due to the continuous variable R in (9)u,Pu,xu,yu,A,yu,nAIt (10) include non-negative integer variable Ap,Nm, OP1 is that mixing is whole Number planning problem.Solution for OP1 is usually NP-hard problem.In the present invention, the present invention designs the suboptimum of low complex degree Method.
First to problem conversion is carried out, due to constraint (4) and (9), OP1 is a mixed integer programming problem.However, this Constraint (4) is eliminated in invention and nonnegative real number R is arrived in relaxation (9)+, but due to the nonconvex property of objective function, OP1 is not still convex asks Topic.The invention firstly uses fractional programmings, re-start transformation to (11) structure.
In the case where without loss of generality, present invention assumes that Ru> 0 and Pu> 0.In order to simple, the present invention indicates OP1 with ψ In area of feasible solutions.So
WhereinWithIt is the optimal solution of OP1 respectively.
In order to solve OP1, the present invention provides following proposition 1, and proof procedure is theoretical using generalized fractional programming.
Proposition 1: the optimal solution of existence anduniquessAnd if only if
Proposition 1 indicates that the present invention can solve OP1, i.e. OP2 by problem (21) of equal value.However,Usually not Know.It is replaced by solution (11) with undated parameter ηTo obtain the optimal solution of OP1.Note that for given η (for example, Iteration k times ηk), pass through the optimization problem OP2 after the conversion that solves in algorithm 1
s.t.(2)-(10)
The iterative algorithm of algorithm 1EE-eICIC
Step 1: Select Error ε > 0 and maximum the number of iterations Kmax；
Step 2: in setting EEWith the number of iterations k=0；
Step 3: as k≤KmaxAnd convergence=false
Given for oneSolve problems OP2.Obtain in EE-eICIC the almost distribution plan of blank subframe (ABS) SlightlyIfOptimal value is then converged to, the optimal ABS subframe allocation strategy of EE-eICIC is returnedWith it is maximumOtherwise it is arrangedWith enable k=k+1.
Secondly using the algorithm of relaxation-rounding: in the present invention, the present invention is asked using the two-stage process of polynomial time Solve OP2.It is convenient for solving to obtain convex programming problem for nonnegative real number firstly, integer variable can relax.Then, pass through house The floor operation for entering relaxation problem obtains the feasible solution of OP3.Detailed description are as follows.
Relaxation: OP2 is relaxed and is solved for OP3.By ignoring constraint (14) and relaxing NmAnd ApOn feas ible space, Obtain OP3.After relaxed constraints (9), NmAnd ApNonnegative real number can be taken.Note that influence of the removal constraint (4) to the problem is main It is that user receives radio resource from macro base station and micro-base station simultaneously in downlink transmission.For optimized variableOP3 modeling are as follows:
S.t. (2)-(3) and (5)-(9)
Wherein R+It is the set of nonnegative real number.
Be rounded: in second step, to first step solution as a result, being suitably rounded, progress floor operation obtains OP3's Feasible solution.
Relaxed algorithm
Relaxation OP3 is a convex programming problem, and there are a variety of effective methods to solve.In invention, the present invention uses CVX Tool solves OP3, is defined as algorithm 2.
It is rounded algorithm
Different from relaxation problem, the downlink of user is only capable of accessing single macrocellular or microcellulor, and cannot be simultaneously Access.In order to obtain the N of OP2mAnd ApFeasible solution, the present invention using rounding-off be rounded method, it is specific as follows
Wherein floor indicates to be rounded downwards, and ceil expression rounds up.
It is then possible to determine approximately feasible solution by the output for being rounded algorithm 2.It is rounded the detailed mistake with efficiency access Journey such as algorithm 3.
Algorithm 3 includes three parts.Firstly, the bracket function by definition obtains NmAnd ApInteger value.Secondly, passing through user The ratio between rate and energy consumption obtain the efficiency of user, compare to determine that user's downlink efficiency accesses.Third passes through filling Come the available average time ratio of each user that calculates downlink, the rate and energy for obtaining user disappear available subframe Consumption.Calculate the efficiency of system
Simulation result of the invention
The method that the present invention is proposed using the method validation of computer simulation.Consider that the overlapping of macrocellular and microcellulor is covered It covers in the region of 300m × 300m.The center positioned at region of macro base station, the density of macro base station areMicro- base It stands and the density of user is based on different scene settings, generated according to being uniformly distributed, wherein the most narrow spacing between user and base station From not less than 10m.Simulation result of the invention passes through 100 average generations.
Parameters are as shown in the table:
Parameter Numerical value
The transmission power of macro base station 36dBm(4W)
The transmission power of micro-base station 30dBm(1W)
The transmission power of macro base station broadcast singal 23dBm(200mW)
Thermal noise power (Thermal noise power) -174dBm/Hz
Path-loss factor (Path-loss exponent) 3.25
NsfFrame 40
Bandwidth 10MHz
The minimum-rate requirement of user 10kbps
Convergence
A macrocellular is shown in Fig. 3, under the scene of two microcellulors and 30 users, is followed outside EE-eICIC algorithm The convergence number of ring observes that usual ten step of iteration can be completed to restrain.For the global convergence speed of EE-eICIC algorithm, by It is realized in using centralized approach, as CVX (i.e. algorithm 2) is given to solveTherefore, the convergence that Fig. 3 is shown is namely The convergence rate of EE-eICIC algorithm totality.
The performance of EE-eICIC
For the ease of comparing, the following two kinds method is compared.
1) maximum and rate optimized based on eICIC, it is expressed as MaxSUMRate
2) the ceiling effect optimization based on eICIC, effect function are ln (Ru), it is expressed as MaxSUMlogRate
Method proposed by the present invention: optimized based on eICIC maximum efficiency, be expressed as MaxEE.
Fig. 4 shows that in macro base station quantity be 1, and under the scene that microcellulor quantity is 2, number of users is to system energy efficiency Influence.From fig. 4, it can be seen that based on eICIC maximum efficiency optimization (MaxEE) compared to MaxSUMlogRate and MaxSUMRate has significant energy efficiency gains.Compared with MaxSUMRate and MaxSUMlogRate, MaxEE can be incited somebody to action Network energy efficiency averagely promotes 9.52% and 11.29%.Particularly, with the increase of number of users, the MaxEE of proposition can be obtained Gains more more than MaxSUMlogRate and MaxSUMRate.It is therefore found that eICIC needs the angle from energy efficiency It is redesigned.
Fig. 5 shows in 1 macro base station, the system of 30 users, influence of the microcellulor quantity to system energy efficiency. From fig. 5, it can be seen that efficiency of the scheme MaxEE proposed compared to MaxSUMRate and MaxSUMlogRate increases 23.44% With 64.71%.In addition, efficiency gain increases with the increase of microcellulor, it means that for following super-intensive isomery bee Nest network can obtain bigger efficiency gain by further resource allocation, wherein main reason is that base station and user The distance between shorten, the adjustment of the dynamic of load.
In Fig. 6, energy efficiency of the present invention from network, three aspect three kinds of schemes of comparison of optimal user and worst user System energy efficiency performance.From fig. 6, it can be seen that it can be seen that MaxEE still has the best and worst user in system Biggish energy efficiency gains.
In Fig. 7, energy efficiency of the present invention from network, three aspect three kinds of schemes of comparison of optimal user and worst user Throughput of system.Compared with other two kinds of algorithms, MaxEE obtains lower network rate.This is because MaxSUMRate is most The rate of bigization system, MaxSUMlogRate achieve good folding between throughput of system and user throughput fairness In.As can be seen from Figures 6 and 7, compared with MaxSUMRate and MaxSUMlogRate scheme, MaxEE needs certain system to hold Cost is measured, to obtain system energy efficiency raising.
Fig. 8 gives in 30 custom systems, the microcellulor of different densities to MaxSUMlogRate and MaxEE most The influence of excellent almost blank subframe (ABS) ratio.As can be seen from Figure 8, when microcellulor becomes more dense, The ABS ratio of MaxSUMLogRate and MaxEE increases, but the ABS ratio of algorithm MaxEE proposed by the invention is greater than MaxSUMLogRate.This is because microcellulor has higher energy efficiency.MaxEE increases the transmission time of microcellulor, ABS will become larger.When microcellulor closeness increases, users more for MaxEE are accessed in microcellulor.Therefore, EE-eICIC The advantages of microcellulor can more be made full use of.

Claims (4)

1. a kind of efficiency optimization method of heterogeneous network enhancement type district interference coordination, which comprises the following steps:
1) a double-deck isomery beehive network system being made of macrocellular and microcellulor is constructed, wherein user only can choose and connect Enter base station: macro base station or micro-base station；
2) in the region of each macro base station covering, micro-base station and user in the area measures channel status and interference State sends it to macro base station, carries out unified scheduling of resource after calculating in macro base station；
3) the downlink signal intensity that user receives according to whole bandwidth selects a macrocellular and a microcellulor as candidate Set, then the iteration that is optimized by efficiency, relaxation are converted into convex programming problem, are rounded and are known as changing for the feasible solution of former problem Generation-relaxation-rounding algorithm, determines which base station user accesses；
4) initialization of EE-eICIC iterative algorithm, Select Error ε > 0 and maximum number of iterations Kmax, initial efficiency is setWith the number of iterations k=0；
5) EE-eICIC iterative algorithm, as k≤Kmax, the efficiency given for oneSolving will be former by broad sense fractional programming The optimization problem OP2 that beginning problem OP1 simplifies obtains in EE-eICIC the almost allocation strategy of blank subframe (ABS)IfOptimal value is then converged to, the optimal ABS subframe allocation strategy of EE-eICIC is returnedAnd maximum 'sOtherwise it is arrangedWith enable k=k+1；In formula, RuIndicate the transmission rate of user u, puIndicate the power consumption of user u, The worst error that ε convergence allows；
6) the ABS subframe agreement of the efficiency is configured, according to the iteration-relaxation-rounding algorithm, macro base station is calculated can To be supplied to the ABS number of subframes that the micro-base station interfered by it uses efficiency；
7) according to the relaxation-rounding algorithm, the transmission time resource of allocation of downlink, and user's downlink average transmission is obtained Time determines the transmission rate and energy consumption of user, calculates user and system energy efficiency；
8) calculate after, by the result notice of resource management and scheduling give each micro-base station and user, thus each user into Access and data transmission of the row based on efficiency.
2. a kind of efficiency optimization method of heterogeneous network enhancement type district interference coordination according to claim 1, special Sign is that iteration-relaxation-rounding algorithm early-stage preparations and its process used in the step 3) are as follows:
A, Signal to Interference plus Noise Ratio (SINR) model is established, the same base station only exists three classes interference: macro base using being time-multiplexed in cell The interference stood between macro base station, macro base station and micro-base station and micro-base station and micro-base station is obtained according to interference and ABS agreement SINR model；
B, according to SINR model, using SINR in Shannon capacity formula or LTE with the available user's of corresponding table of rate Average transmission rate ru；
C, the efficiency Optimized model based on eICIC is established；
Solve the set of variableWherein RuIndicate the transmission rate of user u, puIt indicates The power consumption of user u, xuDownlink times of the user in non-ABS, yu,ADownlink times of the user in ABS, yu,nAWith Downlink times of the family in non-ABS, ApIndicate that the available ABS subframe of micro-base station and macro base station keep downlink silent, NmIt indicates The non-ABS number of sub frames that macro base station can be used, and then establish primal problem (OP1, Optimization Problem1), OP1 It is to be optimized for target with the efficiency of the ratio between formula (1) rate and energy consumption and optimization problem that constraint condition (2)-(10) are constituted is as follows:
xu(yu,A+yu,nA)=0 (4)
xu≥0,yu,A≥0,yu,nA≥0 (9)
Constraint (2) indicates the transmission rate of user no more than the time scale and user obtained from macrocellular or microcellulor Minimum-rate requirementConstraining (3) indicates, the energy consumption of user cannot be greater than the time obtained from macrocellular or microcellulor Ratio and the limitation of the maximum power of user；Constraint (4) indicates that user is only capable of accessing single macrocellular or microcellulor, and cannot It accesses simultaneously；Constraining (5) indicates in interference set IBSMiddle ABS subframe and non-ABS subframe sum limit, wherein IBS, BS ∈ { macro, pico } indicates the set of all interference microcellulors and all interference macrocellulars；Constraining (6) indicates to distribute from macrocellular It is less than total non-ABS number of sub frames N to the time scale of user's subframem；Constraint (7) indicates to distribute to user's from microcellulor ABS subframe time ratio is less than total ABS subframe Ap；Constraint (8) indicates the subframe time ratio that user is distributed to from microcellulor Less than total ABS period Nsf, also illustrate that the number of sub frames for distributing to user from macrocellular or microcellulor is less than total available number of sub frames；
In formula, N+Indicate the set of nonnegative integer；U is the set of user u；M is the acronym of macro base station macro；M is macro The set of the m of base station；P is the acronym of micro-base station pico；P is the set of micro-base station p；muFor the user for accessing macro base station Set；puFor the user's set for accessing micro-base station；NsfThe number of sub frames for including for the period of ABS, an ABS frame；NmFor macro base station The non-ABS subframe that can be used, i.e. macro base station can carry out the number of sub frames of downlink transfer；APFor available ABS of micro-base station Frame, macro base station keep downlink silent；The transimission power of macro base station；The transimission power of macro base station broadcast singal；The transimission power of micro-base station；The maximal received power that user allows；It is obtained in non-ABS subframe from macro base station Downlink transmission rate；In the downlink transmission rate that ABS subframe is obtained from micro-base station；In non-ABS subframe from micro- The downlink transmission rate that base station obtains；
D, since constraint formula (4) and formula (10), OP1 are a mixed integer programming problems, however, even if eliminating constraint formula (4) And nonnegative real number R is arrived in relaxation formula (10)+, but due to the nonconvex property of objective function, OP1 is not still convex problem, first with point Number planning, is re-converted into optimization problem OP2 to the optimization problem of OP1；
s.t.(2)-(10)
Wherein (2)-(10) indicate that the constraint condition (2) in OP1 optimization problem arrives all formulas of (10),Indicate user Rate and power ratio, i.e. the efficiency of user；
E, pass through the optimization problem OP2 after the conversion of the iterative algorithm solution of EE-eICIC described in right 1, including following step It is rapid:
1. Select Error ε > 0 and maximum the number of iterations Kmax；
2. being arranged in EEWith the number of iterations k=0；
3. as k≤KmaxIt is not restrained with convergence=false expression；
Given for oneSolve problems OP2 obtains in EE-eICIC the almost allocation strategy of blank subframe (ABS) IfThen convergence=true indicates convergence, returns to the optimal ABS subframe of EE-eICIC Allocation strategyWith it is maximumOtherwise it is arrangedWith enable k=k+1, continue to run step 3.；
F, relaxation-rounding algorithm solves OP2；
Firstly, integer variable, which can relax, convenient for solving, then, passes through rounding to obtain convex programming problem for nonnegative real number Operation obtains the feasible solution of OP3, and detailed description are as follows:
1) relaxation: OP2 is relaxed and is solved for OP3；By ignoring N in constraint formula (4) and loose constraint formula (10)mAnd ApOn Feas ible space, obtain OP3, after relaxed constraints formula (10), NmAnd ApNonnegative real number can be taken, removal constraint formula (6) is asked original The influence of topic OP1 essentially consists in user and receives radio resource from macrocellular and microcellulor simultaneously in downlink transmission, for Optimized variableOP3 modeling are as follows:
S.t. (2)-(3) and (5)-(9)
Wherein R+It is the set of nonnegative real number, (2)-(3) indicate that the constraint condition (2) in OP1 optimization problem arrives (3), (5)-(9) Indicate that the constraint condition (5) in OP1 optimization problem arrives (9)；
2) be rounded: in second step, to the first step solve as a result, be suitably rounded, carry out floor operation, obtain OP2 can Row solution.
3. a kind of efficiency optimization method of heterogeneous network enhancement type district interference coordination according to claim 2, special Sign is that the floor operation detailed step in the step F is as follows:
1. being rounded NmAnd Ap: by NmAnd ApInteger is become using following operation from positive real number:
Wherein N'mAnd A'pFor the output for solving convex programming OP3；
2. calculating user from the available downlink transmission rate of macro base station and energy consumption:
With
3. calculating user from the available downlink transmission rate of micro-base station and energy consumption:
With
WhereinWithFor the output for solving convex programming OP3；
4. calculating the efficiency of userWithCompare efficiency of the user from macro base station and micro-base station, IfThe then access macro base station of user, otherwise user accesses micro-base station,
5. calculating each macro base station and micro-base station, the utilization rate of its downlink is calculated,
XmIndicate the non-ABS utilization rate of macro base station, Yp,AAnd Yp,nAIndicate the utilization rate of the ABS and non-ABS of micro-base station, It is the user's set for accessing macro base station,Indicate the set of access micro-base station,
6. each user is calculated, the time that downlink can be transmitted,
7. calculating the user's energy consumption and available transmission rate of access macro base station:
With
8. calculating the user's energy consumption and available transmission rate of access micro-base station:
With
9. calculating user's efficiency:
4. a kind of efficiency optimization method of heterogeneous network enhancement type district interference coordination according to claim 2, special Sign is that the SINR concrete model in the step B is as follows:
For accessing the user of micro-base station, the downlink SINR modeling of user u are as follows:
For accessing the user of macro base station, the downlink SINR of user u:
Then, the average transmission rate of user u is obtained according to SINR model:
In formula, PRx(u) power arrived for user u downlink reception；Ppico(u) for by the interference of the downlink of other micro-base stations； Pmacro(u) for by the interference of the downlink of other macro base stations；Downlink to obtain in non-ABS subframe from macro base station passes Defeated rate；For the downlink transmission rate obtained in ABS subframe from micro-base station；For in non-ABS subframe from micro-base station The downlink transmission rate of acquisition.
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