CN109413724A - A kind of task unloading and Resource Allocation Formula based on MEC - Google Patents
A kind of task unloading and Resource Allocation Formula based on MEC Download PDFInfo
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- 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
- H04W52/0209—Power saving arrangements in terminal devices
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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
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- 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
Abstract
Mobile edge calculations (MEC) is by bringing low time delay, low-power consumption, highly reliable advantage in mobile network edge offer IT service environment and cloud computing ability, to become the hot spot of the following 5G research.Be disclosed herein invented it is a kind of based on MEC task unloading and Resource Allocation Formula, including formulated unloading determine and resource allocation combined optimization problem；Optimize unloading using coordinate descent to determine；Meet and subchannel distribution is carried out to user using improved Hungary Algorithm and greedy algorithm under the conditions of user's time delay；Minimum power problem is converted by energy consumption minimization problem, and is translated into a convex optimization problem and obtains the optimal transmission power of user.The present invention can satisfy the requirement of the different delay of different user, and energy minimization system total energy consumption, effectively lifting system performance.
Description
Technical field
The present invention relates to mobile edge calculations and wireless communication technology field, in particular to a kind of task based on MEC to unload
Load and Resource Allocation Formula.
Background technique
In recent years, with the continuous upgrading of mobile network and Intelligent mobile equipment, mobile Internet number of users is presented
Explosive growth.Currently, the face the 5th third-generation mobile communication (5G, the fifth generationofmobile technology)
Face explosive data traffic and increases the new challenge for connecting and depositing with bulk device.At the same time, the newly-increased business field of 5G network
Scape, interactive somatic sensation television game, recognition of face, unmanned, virtual reality, industrial Internet of Things Network Communication etc., since they are right
The indexs such as time delay, energy consumption, reliability have higher demand, in order to cope with the corresponding development of mobile Internet high speed development bring
Demand, following 5G communication need to meet the properties demands such as reliable, the ultra high density connection of ultralow time delay, super low-power consumption, superelevation,
Therefore, the data transmission capabilities that wireless cellular network has high speed are not required nothing more than in following mobile communication, but also to be had
Powerful computing capability handles these data.
Currently, augmented reality (AR, augment reality), game on line, smart city, emerging Internet of Things industry etc.
Added Business rapid development.However, existing mobile device due to the factors such as battery capacity is limited, computing capability is insufficient without
Method meets these novel internets and intelligent business for low time delay, high complicated, high reliability demand, and then influences to use
Family experience.Although mobile cloud computing meets user for the performance requirement of these business to a certain extent, it allows to move
Local complicated, a large amount of calculating task is partially or completely unloaded to the cloud data center positioned at core net to execute by equipment, from
And solve the problems, such as that mobile device own resource is in short supply, and to equipment energy when the task of having saved locally executes to a certain extent
The consumption of amount.But the cloud data center offloaded tasks to positioned at core net is needed through return network, he can consume backhaul
Link circuit resource will result in the congestion of backhaul when a large amount of business needs to be unloaded to Cloud Server, generates additional time delay and opens
Pin, therefore, in the following emerging 5G scene, single cloud computing mode can not will meet as far as possible business for it is low when
Prolong, the demand of high reliability.Recently, mobile edge calculations (MEC) is proposed as one of key technology of 5G, MEC system
Allow mobile device that calculating task is unloaded to network edge node, such as base station, wireless access point by wireless cellular network.
Compared with traditional mobile cloud computing, since Edge Server is deployed in the wireless access network edge closer to user terminal by MEC,
Therefore it greatly shortens the distance between cloud computing server and mobile device.Backhaul can be significantly reduced in this way to gather around
Plug, and reduce the time delay expense of user.In addition, Edge Server can provide powerful calculation processing ability for user, thus
Substantially shorten task computation time delay.Secondly, the network architecture of MEC shortens edge in the case where device battery finite energy
Server at a distance from mobile device, largely about unloading when be wirelessly transferred consumed by energy, substantially prolong object
The service life of networked devices.Result of study shows that for different AR equipment, MEC can extend 30%~50% equipment electricity
The pond service life.
Meanwhile network-intensive be turned to cope with the following 5G wireless network capacitance promoted 1000 times of challenges main means it
One, being widely recognized as industrial circle and academia has been obtained.The following 5G network needs support tens Tbits-1·km-2's
Traffic density, million Connection Densities greater than every square kilometre.Network-intensiveization, which mainly passes through intensively to lay, has low function
Microcellulor, Pico cell, the Home eNodeB etc. of rate low cost.They can further the distance between user and all kinds of base stations, from
And realize the spatial multiplex ratio for promoting frequency spectrum resource, bigger band is provided for the user of hot spot region, medium-sized and small enterprises and premises
Wide and higher data rate guarantees user experience, reaches and improves the targets such as network capacity.With traditional macrocellular network framework
It compares, the network architecture of super-intensive networking can improve covering, increase power system capacity, increase user satisfaction.
So in order to meet, the following 5G ultralow time delay, super low-power consumption, superelevation be reliable, new business of ultra high density connection
Demand, super-intensive networking and mobile edge calculations will be the indispensable key technology of the following 5G.
Summary of the invention
For the above the deficiencies in the prior art, the present invention considers the unloading of the calculating under the MEC scene of intensive networking, for
Unloading determines and intensively organizes the influence of interference off the net to system performance, considers system total energy consumption, propose unloading decision and
The joint Solve problems of resource allocation carry out the energy consumption of optimization system, under the different delay constraint condition of different user, minimum system
The total energy consumption of system.A kind of task unloading and Resource Allocation Formula based on MEC of the present invention, comprising the following steps:
Step 101: formulating the combined optimization problem of unloading decision and resource allocation；
Step 102: optimizing unloading using coordinate descent and determine；
Step 103: subchannel distribution is carried out to user using improved Hungary Algorithm and greedy algorithm；
Step 104: converting minimum power problem for energy consumption minimization problem, and be translated into a convex optimization and ask
Topic obtains the optimal transmission power of user；
Preferably, the formulation unloading determines and the combined optimization problem of resource allocation includes: to consider a macro base station and N
The 5G isomery MEC network of a small base station (small base station, SBS) composition.MEC is deployed at the edge of heterogeneous network
Server, MEC server may be performed simultaneously the task of multiple computation-intensives.For Reusespectrum, it is contemplated that at this
SBS is disposed in a manner of same frequency in heterogeneous network, and is connect in a wired manner with macro base station, and the frequency band of each SBS is divided
At K orthogonal subchannels, the set that K={ 1,2 ..., K } represents subchannel is defined, each user in the same cell makes
With orthogonal subchannel, the user in different community can be multiplexed identical subchannel, thus the user of different minizones it
Between can interfere with each other.In order to facilitate analysis, here it is contemplated that each SBS only has the case where 1 user, definition N=1 ...,
N } represent the set of all users.It is contemplated that each user n has a computation-intensive and delay sensitive in this network
Task need to complete, user can according to current network state and self-demand selection locally execute or be unloaded to MEC execute.
Define an∈ { 0,1 } is the unloading decision of user, and 0, which represents user's selection, locally executes, and 1, which represents user's selection, is unloaded to MEC and holds
Row.Therefore, we use A={ a1,...,aNIndicate that the unloading of all users determines；
When user selects unloading task, the interference of adjacent SBS user when uplink is considered, when user n distributes son letter
When road k carries out data transmission, Signal to Interference plus Noise Ratio of the user n on subchannel k are as follows:
Transmission rate of the user n on subchannel k is calculated are as follows:
Then total rate of user n uplink in the task of unloading are as follows:
Wherein, B indicates subchannel bandwidth,It is a binary variable, if subchannel k is assigned to user n
It is on the contrary WithTransmission power of the user n and m on subchannel k is respectively indicated,WithRespectively indicate SBSnUse
Family n and SBSmUser m to SBSn channel gain, ω0Indicate Background Noise Power.
Consider that each user n has the calculating task of a delay-sensitivewnIt represents and calculates the task institute
The cpu cycle needed, dnThe size for representing input data, the parameter including program code and input,Indicate that user can tolerate
Maximum delay.We discuss the computation model of the energy consumption and time delay that locally execute and be unloaded to MEC execution below.
(1) when user's selection locally executes, calculating task will calculate on each user equipment, useUser is represented to set
Standby computing capability, the then time delay of local computing are as follows:
Local computing energy consumption are as follows:
Wherein, the size of κ value depends on the chip structure of mobile device, we take κ=10 here(-26).In view of local
The energy consumption of calculating is with user's computing capabilityIncrease and increase, pass through dynamic voltage scaling technology (dynamic
Voltage scaling, DVS) computing capability of dynamic regulation user can be minimized the calculating energy consumption of user local.Therefore,
Under delay constraint, optimal computing capability that when local computing distributesIt can indicate are as follows:
Wherein,Indicate the max calculation ability of user n.
(2) when user, which selects to unload task, to be calculated, user equipment, which will be linked into corresponding SBS by wireless network, to appoint
Business is unloaded to MEC and is calculated.For unloading the user of task, can be generated when by wireless uplink transformation task to MEC
Corresponding propagation delay time and energy consumption, according to traffic model, uplink time delay when our available user n unload task are as follows:
After calculating task is unloaded to MEC, MEC can distribute certain computing resource to handle the task, use fcIndicate MEC
The calculating speed of distribution, here it is contemplated that during task execution MEC be each user distribution calculating speed be it is fixed, then
MEC executes the time delay of the task are as follows:
User's total energy consumption when then transmitting calculating task are as follows:
WhereinIndicate total transmission power of user；Indicate that the circuit power under user's idle state disappears
Consumption.
Each user equipment will assess the cost of local computing during task unloading, then be reported to
MEC.Meanwhile MEC can also assess cost when each user equipment unloads.Then, MEC is by comparing local and unloading flower
Take, make corresponding unloading and determine, unloading determines to be expressed as:
Here we use NcThe number of users for indicating unloading, uses NcIndicate user's set of unloading, then the user of local computing
Number be N-Nc。
In view of the delay requirement and limited battery capacity of user, it will be unloaded by optimization determine A and subchannel herein
C and power distribution P is distributed to minimize the total energy consumption of user, we illustrate the objective functions for needing to optimize herein:
Wherein,WithThe time delay of user n when local and unloading calculates is respectively indicated,Indicate that user n can receive
Maximum delay,Indicate channel distribution situation,Indicate that k-th of subchannel is allocated to nth user,Then
It indicates without k-th of subchannel of distribution,Indicate transmission power of the nth user in k-th of subchannel, C1, which indicates to calculate, to be appointed
User patient maximum delay requirement when business, what C2 was indicated is that the transmission power of user cannot be greater than its maximum transmission function
Rate, C3 indicate the transmission power on every sub-channels be it is non-negative, C4 indicate be channel distribution state, C5 indicate unloading determines
It surely is a binary variable.
Preferably, it is described optimized using coordinate descent unloading determine include: A=[a1,a2,...,aN] indicate all
The unloading of user determines, gives initial unloading and determines A0For all 1's matrix, Al-1It indicates in l-1 (l=1,2 ...) secondary iteration
Unloading determine, accordingly use V (al-1) indicate to be determined as A in given unloadingl-1When objective function optimal value, definitionIt is
Change income obtained after current unloading determines when l iteration, then
Wherein, Al-1(n) it indicates that the unloading after user n changes current determine determines, it is as follows to update rule:
Wherein,Indicate two adding method of mould.
Coordinate descent is each time along a variable anDirection Filled function, to find the Local Minimum of objective function
Value determines so algorithm can achieve convergence by limited times iteration to obtain an optimal unloading.In the l times iteration
In, we obtain unloading and determine Al, by calculating, if incomeThenWherein,
Indicate the user that Income Maximum is obtained in the l times iteration.
Preferably, described that subchannel distribution is carried out to user using improved Hungary Algorithm and greedy algorithm: in son
During channel distribution, it is intended that the best subchannel distribution of channel quality is maximized the upper of user to user every time
Row transmission rate.Simultaneously, it is intended that in the case where meeting user's delay requirement, distribute subchannel as few as possible for each user and come
It avoids generating serious interference due to the excessive channeling of user.Therefore, according to constraint condition C1 and C3, each user's
Subchannel distribution problem can be planned to as follows:
For above-mentioned subchannel distribution problem, N can be equivalent tocThe matching problem of a user and K sub-channels, here I
A channel matched is carried out using improved Hungary Algorithm first, minimum speed limit demand is then being met using greedy algorithm
Under continue as user and distribute enough subchannels, algorithm steps are as follows:
1) beneficial matrix needed for constructing first time iteration
2) if number of users is greater than number of subchannels, i.e. Nc> K, then add Nc- K virtual subchannels, become N for beneficial matrixc
×NcSquare matrix, if number of users is less than subchannel number, i.e. Nc< K, then add K-NcBeneficial matrix is become K × K's by a user
Square matrix.
3) weight limit is carried out using Hungary Algorithm to match to obtain a channel distribution.
4) subchannel distribution matrix is updated according to the subchannel result of distributionAnd interference matrix
5) check whether each user meets minimum speed limit demand, algorithm terminates if meeting.It is needed if not satisfied, updating
The user for continuing to distribute subchannel is Nc'。
6) channel distribution matrix is checkedTo Nc' each of user selected from remaining subchannel using greedy algorithm
Selecting generation interferes the smallest subchannel distribution to the user.
7) repeat step 4) -6), until all users all meet minimum speed limit demand orThen algorithm terminates.
Preferably, described that subchannel distribution is carried out to user using improved Hungary Algorithm and greedy algorithm:
In the case where determining to unloading with channel distribution, original optimization aim is still a non-convex optimization problem, it is contemplated that mesh
The constraint condition C1 of scalar functions is maximum delay constraint, and therefore, the minimization problem of energy consumption can be converted under delay constraint
Minimal power consumption problem, therefore we convert primal problem are as follows:
Since above-mentioned optimization problem is still a non-convex optimization problem, below we by variable replacement, enableThen:
Therefore we convert optimization problem are as follows:
Here former equation is become inequality constraints C3 by us, it is therefore an objective to be to convert this non-convex problem to convex
Problem, and this change has no effect on the optimal solution of problem, because data rate of the user n on subchannel k is in optimality
On cannot be less than
Detailed description of the invention
Fig. 1 present invention proposes the unloading of the task in intensive networking based on MEC and closes resource allocation preferred implementation flow chart；
Task uninstalling system illustraton of model in Fig. 2 intensive networking used in the present invention based on MEC；
Fig. 3 present invention selects the number of users of unloading to change feelings as number of users increases under different delay constraint condition
Condition；
Fig. 4 present invention increases system total energy consumption simulation comparison figure with number of users；
Specific embodiment
To make the object, technical solutions and advantages of the present invention express to be more clearly understood, with reference to the accompanying drawing and specifically
Case study on implementation is described in further details the present invention.
Fig. 1 show the present invention and is used for task unloading and Resource Allocation Formula preferred embodiment flow chart based on MEC, should
Method the following steps are included:
Step 101: formulating the combined optimization problem of unloading decision and resource allocation；
Step 102: optimizing unloading using coordinate descent and determine；
Step 103: subchannel distribution is carried out to user using improved Hungary Algorithm and greedy algorithm；
Step 104: converting minimum power problem for energy consumption minimization problem, and be translated into a convex optimization and ask
Topic obtains the optimal transmission power of user；
Fig. 2 is the task uninstalling system illustraton of model based on MEC in intensive networking used in the present invention, comprising: considers one
The 5G isomery MEC network of a macro base station and N number of small base station (smallbase station, SBS) composition, system model is as schemed
Show.It is deployed with MEC server at the edge of heterogeneous network, MEC server may be performed simultaneously the task of multiple computation-intensives.
For Reusespectrum, it is contemplated that SBS is disposed in a manner of same frequency in this heterogeneous network, and and macro base station with wired
Mode connects, and the frequency band of each SBS is divided into K orthogonal subchannels, defines the collection that K={ 1,2 ..., K } represents subchannel
It closes, each user in the same cell uses orthogonal subchannel, and the user in different community can be multiplexed identical sub- letter
Road, so can be interfered with each other between the user of different minizones.In order to facilitate analysis, here it is contemplated that each SBS only has 1
The case where a user, defines the set that N={ 1 ..., N } represents all users.It is contemplated that each user n in this network
Have that one computation-intensive and delay sensitive task needs to complete, user can select according to current network state and self-demand
It selects and locally executes or be unloaded to MEC execution.Define an∈ { 0,1 } is the unloading decision of user, and 0, which represents user, selects local hold
Row, 1, which represents user's selection, is unloaded to MEC execution.Therefore, we use A={ a1,...,aNIndicate that the unloading of all users determines.
When user selects unloading task, the interference of adjacent SBS user when uplink is considered, when user n distributes son letter
When road k carries out data transmission, Signal to Interference plus Noise Ratio of the user n on subchannel k are as follows:
Transmission rate of the user n on subchannel k is calculated are as follows:
Then total rate of user n uplink in the task of unloading are as follows:
Wherein, B indicates subchannel bandwidth,It is a binary variable, if subchannel k is assigned to user n
It is on the contrary WithTransmission power of the user n and m on subchannel k is respectively indicated,WithRespectively indicate SBSnUse
Family n and SBSmUser m to SBSnChannel gain, ω0Indicate Background Noise Power.
It is contemplated that each user n has the calculating task of a delay-sensitivewnIt represents and calculates this
Cpu cycle required for being engaged in, dnThe size for representing input data, the parameter including program code and input,Indicate user's energy
The maximum delay of tolerance.We discuss the computation model of the energy consumption and time delay that locally execute and be unloaded to MEC execution below.
(1) when user's selection locally executes, calculating task will calculate on each user equipment, useUser is represented to set
Standby computing capability, the then time delay of local computing are as follows:
Local computing energy consumption are as follows:
Wherein, the size of κ value depends on the chip structure of mobile device, we take κ=10 here(-26).In view of local
The energy consumption of calculating is with user's computing capabilityIncrease and increase, pass through dynamic voltage scaling technology (dynamic
Voltage scaling, DVS) computing capability of dynamic regulation user can be minimized the calculating energy consumption of user local.Therefore,
Under delay constraint, optimal computing capability that when local computing distributesIt can indicate are as follows:
Wherein,Indicate the max calculation ability of user n.
(2) when user, which selects to unload task, to be calculated, user equipment, which will be linked into corresponding SBS by wireless network, to appoint
Business is unloaded to MEC and is calculated.For unloading the user of task, can be generated when by wireless uplink transformation task to MEC
Corresponding propagation delay time and energy consumption, according to traffic model, uplink time delay when our available user n unload task are as follows:
After calculating task is unloaded to MEC, MEC can distribute certain computing resource to handle the task, use fcIndicate MEC
The calculating speed of distribution, here it is contemplated that during task execution MEC be each user distribution calculating speed be it is fixed, then
MEC executes the time delay of the task are as follows:
User's total energy consumption when then transmitting calculating task are as follows:
WhereinIndicate total transmission power of user；Indicate the circuit power consumption under user's idle state.
Each user equipment will assess the cost of local computing during task unloading, then be reported to
MEC.Meanwhile MEC can also assess cost when each user equipment unloads.Then, MEC is by comparing local and unloading flower
Take, make corresponding unloading and determine, unloading determines to be expressed as:
Here we use NcThe number of users for indicating unloading, uses NcIndicate user's set of unloading, then the user of local computing
Number be N-Nc。
In view of the delay requirement and limited battery capacity of user, it will be unloaded by optimization determine A and subchannel herein
C and power distribution P is distributed to minimize the total energy consumption of user, we illustrate the objective functions for needing to optimize herein:
Wherein, C1 indicates user institute patient maximum delay requirement when calculating task, and what C2 was indicated is the transmission of user
Power cannot be greater than it maximum and send power, C3 indicate the transmission power on every sub-channels be it is non-negative, what C4 was indicated is
The distribution state of channel, C5 indicate that unloading determines to be a binary variable.
Wherein, step 102 is optimized unloading using coordinate descent and determined, comprising: we use A=[a1,a2,…,aN] table
Show that the unloading of all users determines, gives initial unloading and determine A0For all 1's matrix, Al-1Indicate secondary in l-1 (l=1,2 ...)
Unloading when iteration determines, uses V (a accordinglyl-1) indicate to be determined as A in given unloadingl-1When objective function optimal value, definitionTo change income obtained after current unloading determines when the l times iteration, then
Wherein, Al-1(n) it indicates that the unloading after user n changes current determine determines, it is as follows to update rule:
Wherein,Indicate two adding method of mould.
Coordinate descent is each time along a variable anDirection Filled function, to find the Local Minimum of objective function
Value determines so algorithm can achieve convergence by limited times iteration to obtain an optimal unloading.In the l times iteration
In, we obtain unloading and determine Al, by calculating, if incomeThenWherein,
Indicate the user that Income Maximum is obtained in the l times iteration.
Wherein, step 103 carries out subchannel distribution, packet to user using improved Hungary Algorithm and greedy algorithm
It includes: during subchannel distribution, it is intended that every time maximize the best subchannel distribution of channel quality to user
The uplink transmission rate of user.Simultaneously, it is intended that in the case where meeting user's delay requirement, distributed for each user as few as possible
Subchannel avoids generating serious interference due to the excessive channeling of user.Therefore, according to the constraint item of optimization aim
Part C1 and C3, the subchannel distribution problem of each user can be planned to as follows:
For above-mentioned subchannel distribution problem, N can be equivalent tocThe matching problem of a user and K sub-channels, it is first here
First using improved Hungary Algorithm carry out a channel matched, then using greedy algorithm in the case where meeting minimum speed limit demand after
Continue and distribute enough subchannels for user, algorithm steps are as follows:
101A: beneficial matrix needed for building first time iteration
101B: if number of users is greater than number of subchannels, i.e. Nc> K, then add Nc- K virtual subchannels, beneficial matrix is become
For Nc×NcSquare matrix, if number of users is less than subchannel number, i.e. Nc< K, then add K-NcA user, by beneficial matrix become K ×
The square matrix of K；
101C: weight limit is carried out using Hungary Algorithm and matches to obtain a channel distribution；
101D: subchannel distribution matrix is updated according to the subchannel result of distributionAnd interference matrix
101E: checking whether each user meets minimum speed limit demand, and algorithm terminates if meeting.If not satisfied, updating
The user for needing to continue to distribute subchannel is Nc'。
101F: channel distribution matrix is checkedTo Nc' each of user using greedy algorithm from remaining subchannel
Middle selection, which generates, interferes the smallest subchannel distribution to the user.
101G: repeat step 101D) -101F), until all users all meet minimum speed limit demand orThen calculate
Method terminates.
Energy consumption minimization problem is converted minimum power problem by step 104, and is translated into a convex optimization and asks
Topic obtains the optimal transmission power of user, concrete methods of realizing are as follows: in view of constraint condition C1 is maximum delay constraint, because
This, the minimization problem of energy consumption can be converted into the minimal power consumption problem under delay constraint, therefore original is optimized mesh by us
Mark conversion are as follows:
Since above-mentioned optimization problem is still a non-convex optimization problem, below we by variable replacement, enableThen:
Therefore we convert optimization problem are as follows:
Here former equilibrium relationships are become inequality constraints C3 by us, it is therefore an objective to are this non-convex problem conversion
For convex problem, and this change has no effect on the optimal solution of problem, because data rate of the user n on subchannel k is most
It cannot be less than in dominance
Theorem: the above problem is a convex optimization problem under high Signal to Interference plus Noise Ratio.
It proves: being a convex function since objective function is the form of index summation, while can be seen that constraint item
Part C1, C2, C4 be all it is convex, for inequality constraints C3, due to the nonconvex property of throughput function, so constraint condition C3 right and wrong
Convex.Under the conditions of high Signal to Interference plus Noise Ratio, for the nonconvex property of throughput function, a kind of general treating method is effective approximation,
So that log (1+x) ≈ log (x).Therefore, constraint condition C3 can be converted are as follows:
Wherein,It is the index and form of logarithm, therefore is convex.To sum up may be used
To obtain, under the conditions of high Signal to Interference plus Noise Ratio, above-mentioned optimization problem is a convex optimization problem, it was demonstrated that is finished.For above-mentioned convex excellent
Change problem, optimal power distribution result can be used interior point method and solved.
Fig. 3 shows the unloading number of variations situation of user under the conditions of different time delays.As can be seen from the figure in user
The lower situation of delay constraint under, user more selects to offload tasks to MEC calculating, when delay constraint is higher, uses
Family more selects local computing.This is because this paper algorithm considers the delay requirement of user, ensure that in resource allocation
The minimum speed limit demand of user, when delay constraint is smaller, the rate requirement of user is higher, it will is assigned to more wireless moneys
Source, propagation delay time is lower, and energy consumption is fewer accordingly, and local computing is since delay constraint is smaller, the cpu cycle of user's consumption
Frequency is higher, and corresponding energy consumption will be higher, and at this moment unloading calculating can obtain higher performance compared to local computing user and mention
It rises, therefore user more selects unloading to calculate.Conversely, if user's delay constraint is bigger, the radio resource got accordingly
Less, energy consumption is higher when unloading calculates, and the cpu cycle frequency that user consumes when local computing is lower, at this moment local computing
Performance is better than unloading and calculates, therefore user more selects local computing.
Fig. 4 is described as number of users increases the situation of change of system total energy consumption.Here we by this paper algorithm and this
Ground calculates, all unloading and JOIM algorithm compare, and wherein JOIM algorithm only only accounts for channel distribution, and does not account for
The different delay requirement of user.As can be seen from the figure this paper algorithm has lower system total energy consumption compared to other algorithms.Its
In compared to JOIM algorithm, this paper algorithm considers that whole task unloads prioritization scheme, in the case where meeting delay constraint for user into
It has gone effective channel distribution, and has minimized the transmission power of user in the case where considering system total energy consumption.So this paper algorithm phase
Than can more preferably be unloaded decision and Resource Allocation Formula in JOIM algorithm, therefore it is obviously improved on system performance.
Claims (5)
1. a kind of task unloading and Resource Allocation Formula based on MEC, which comprises the following steps:
Step 101: formulating the combined optimization problem of unloading decision and resource allocation；
Step 102: optimizing unloading using coordinate descent and determine；
Step 103: subchannel distribution is carried out to user using improved Hungary Algorithm and greedy algorithm；
Step 104: converting minimum power problem for energy consumption minimization problem, and be translated into a convex optimization problem and obtain
The transmission power optimal to user.
2. according to the method described in claim 1, it is further characterized in that, the step 101 is formulated unloading and is determined and resource allocation
Combined optimization problem include:
Define an∈ { 0,1 } is the unloading decision of user, and 0, which represents user's selection, locally executes, and 1, which represents user's selection, is unloaded to MEC
It executes；Therefore, we use A={ a1,...,aNIndicate that the unloading of all users determines, each use during task unloading
Family equipment will assess the cost of local computing, then be reported to MEC；Meanwhile MEC can also assess each user equipment
Cost when unloading, then, MEC are made corresponding unloading and are determined, unloading determines to indicate by comparing local and unloading cost
Are as follows:
Here N is usedcThe number of users for indicating unloading, uses NcIndicate user's set of unloading, then the number of the user of local computing is
N-Nc,Indicate the energy consumption of user n selection local computing,Indicate that user n selection unloading calculates, it is contemplated that the time delay of user
Demand and limited battery capacity, the present invention will be unloaded by optimization and determine A and subchannel distribution Matrix C and power distribution square
Battle array P minimizes the total energy consumption of user, gives the objective function that the present invention needs to optimize:
Wherein,WithThe time delay of user n when local and unloading calculates is respectively indicated,Indicate that user n institute is receptible most
Long time delay,Indicate channel distribution situation,Indicate that k-th of subchannel is allocated to nth user,Then indicate do not have
There is k-th of subchannel of distribution,Indicate transmission power of the nth user in k-th of subchannel, PmaxIndicate the maximum of user
Power is sent, C1 indicates user institute patient maximum delay requirement when calculating task, and what C2 was indicated is the transmission power of user
It maximum cannot be greater than and send power, C3 indicate the transmission power on every sub-channels be it is non-negative, what C4 was indicated is channel
Distribution state, C5 indicate unloading determine be a binary variable.
3. according to the method described in claim 1, it is further characterized in that, the step 102 is unloaded using coordinate descent to optimize
Carrying decision includes:
With A=[a1,a2,...,aN] indicate that the unloading of all users determines, it gives initial unloading and determines A0For all 1's matrix, Al-1Table
Show that the unloading in l-1 (l=1,2 ...) secondary iteration determines, uses V (a accordinglyl-1) indicate to be determined as A in given unloadingl-1
When objective function optimal value, definitionTo change income obtained after current unloading determines when the l times iteration, then
Wherein, Al-1(n) it indicates that the unloading after user n changes current determine determines, it is as follows to update rule:
Wherein,Indicate two adding method of mould, coordinate descent is each time along a variable anDirection Filled function, to find
The local minimum of objective function optimal is unloaded to obtain one so algorithm can achieve convergence by limited times iteration
It carries and determines.
4. according to the method described in claim 1, it is further characterized in that, the step 103 using improved Hungary Algorithm and
Greedy algorithm come to user carry out subchannel distribution include:
For subchannel distribution problem, N can be equivalent tocThe matching problem of a user and K sub-channels, first using improved
Hungary Algorithm carries out a channel matched, and user's distribution is then continued as in the case where meeting minimum speed limit demand using greedy algorithm
Enough subchannels, algorithm steps are as follows:
1) beneficial matrix needed for constructing first time iteration
2) if number of users is greater than number of subchannels, i.e. Nc> K, then add Nc- K virtual subchannels, become N for beneficial matrixc×Nc
Square matrix, if number of users is less than subchannel number, i.e. Nc< K, then add K-NcBeneficial matrix is become the square matrix of K × K by a user；
3) weight limit is carried out using Hungary Algorithm to match to obtain a channel distribution；
4) subchannel distribution matrix is updated according to the subchannel result of distributionAnd interference matrix
5) check whether each user meets minimum speed limit demand, algorithm terminates if meeting；It needs to continue if not satisfied, updating
The user for distributing subchannel is Nc'；
6) channel distribution matrix is checkedTo Nc' each of user using greedy algorithm select from remaining subchannel production
It is raw to interfere the smallest subchannel distribution to the user；
7) repeat step 4) -6), until all users all meet minimum speed limit demand orThen algorithm terminates.
5. according to the method described in claim 1, it is further characterized in that, the step 104 converts energy consumption minimization problem to
Minimum power problem, and be translated into a convex optimization problem and obtain the optimal transmission power of user and include:
In the case where obtaining unloading decision and channel distribution, original optimization aim is still a non-convex optimization problem,
In view of bound for objective function C1 is maximum delay constraint, therefore, the minimization problem of energy consumption can be converted into time delay
Minimal power consumption problem under constraint, therefore we convert primal problem are as follows:
Since above-mentioned optimization problem is still a non-convex optimization problem, below we by variable replacement, enableThen
It obtains:
Therefore and then we convert above-mentioned optimization problem are as follows:
For the convex optimization problem after above-mentioned conversion, optimal power distribution result can be used interior point method and be solved.
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| CN114173359B (en) * | 2021-12-09 | 2023-07-14 | 重庆邮电大学 | Heterogeneous network resource scheduling method used in industrial Internet of things environment |
| CN114173359A (en) * | 2021-12-09 | 2022-03-11 | 重庆邮电大学 | Heterogeneous network resource scheduling method used in industrial Internet of things environment |
| CN114189521B (en) * | 2021-12-15 | 2024-01-26 | 福州大学 | Method for collaborative computing offloading in F-RAN architecture |
| CN114189521A (en) * | 2021-12-15 | 2022-03-15 | 福州大学 | Method for cooperative computing offload in F-RAN architecture |
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