CN111464216B - Mobile edge calculation time delay minimization method based on large-scale MIMO - Google Patents
Mobile edge calculation time delay minimization method based on large-scale MIMO Download PDFInfo
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- CN111464216B CN111464216B CN202010154662.XA CN202010154662A CN111464216B CN 111464216 B CN111464216 B CN 111464216B CN 202010154662 A CN202010154662 A CN 202010154662A CN 111464216 B CN111464216 B CN 111464216B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/0413—MIMO systems
- H04B7/0426—Power distribution
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/0413—MIMO systems
- H04B7/0452—Multi-user MIMO systems
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/04—Wireless resource allocation
- H04W72/044—Wireless resource allocation based on the type of the allocated resource
- H04W72/0473—Wireless resource allocation based on the type of the allocated resource the resource being transmission power
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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
- H04W72/54—Allocation or scheduling criteria for wireless resources based on quality criteria
- H04W72/542—Allocation or scheduling criteria for wireless resources based on quality criteria using measured or perceived quality
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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
The invention discloses a mobile edge calculation time delay minimization method based on large-scale MIMO, which comprises the following steps: the method comprises the following steps: calculating the channel gain of each user according to the channel state information, and performing normalization processing to obtain a data migration rate; step two: considering the maximum power and energy constraint existing in practice and the fairness among all users, under the condition of meeting the maximum energy constraint, obtaining the actual maximum transmission power, and then obtaining the optimal solution; step three: and according to the optimal solution, performing wireless resource allocation, and allocating each user to realize the same overall delay for all users. The invention can utilize limited computing resources to minimize time delay under the condition of maximum transmission power and energy consumption constraint by joint distribution of wireless and computing resources while carrying out a large amount of user task migration computation, and can also ensure stable migration rate and less signal overhead by using the invention.
Description
Technical Field
The invention relates to the technical field of communication signal processing, in particular to a mobile edge calculation time delay minimization method based on large-scale MIMO.
Background
With the advent of the big data era, the network requirements of low time delay and low energy consumption are more and more demanding, and Mobile Edge Computing (MEC) migrates the computing task of the Mobile device to an edge base station with strong computing power, thereby greatly reducing the computing time delay, reducing the power consumption of the Mobile device, and prolonging the service life of the Mobile device. In the conventional MEC system, since the computation delay and the energy consumption are usually contradictory, that is, the more the computation capability is, the higher the energy consumption is, in order to balance between the two, the wireless resources and the computation resources need to be jointly allocated. In the past, the fact that single antennas are arranged on a user and a base station is mainly considered, and the advantage of the MIMO technology in the migration efficiency is not studied, namely the large-scale MIMO technology can support a large number of users to perform task migration simultaneously, and has high spectrum efficiency and energy efficiency, so that loading time delay and energy consumption are reduced. In addition, the inherent channel hardening characteristic of the massive MIMO system can ensure that a stable migration rate is maintained when the mobile device migrates the task, which is important for the time delay analysis in the MEC. Therefore, the invention applies the large-scale MIMO technology to the multi-user MEC system and designs a combined optimization scheme of computing resources and wireless resources to realize the minimum computing time delay of the system.
Disclosure of Invention
The invention aims to provide a method for minimizing time delay of mobile edge calculation based on large-scale MIMO, which solves the problems in the background art.
In order to achieve the purpose, the invention provides the following technical scheme: a mobile edge calculation time delay minimization method based on large-scale MIMO comprises the following steps:
the method comprises the following steps: calculating the channel gain of each user according to the channel state information, and performing normalization processing to obtain a data migration rate;
step two: considering the maximum power and energy constraint existing in practice and the fairness among all users, under the condition of meeting the maximum energy constraint, obtaining the actual maximum transmission power, and then obtaining the optimal solution;
step three: and according to the optimal solution, allocating wireless resources, and allocating each user to realize the same overall delay for all users, thereby completing the joint allocation of wireless and computing resources and finally obtaining the minimum delay time.
Compared with the prior art, the invention has the following beneficial effects: the benefit of the present invention is that it clearly illustrates the advantages of applying massive MIMO techniques to mobile edge computation, while taking into account resource allocation under different channel state information. The invention can utilize limited computing resources to minimize time delay under the condition of maximum transmission power and energy consumption constraint by joint allocation of wireless and computing resources while carrying out a large amount of user task migration computation, and can also ensure stable migration rate and less signal overhead by using the invention.
Drawings
FIG. 1 is a process flow of resource allocation under ideal CSI conditions according to the present invention;
FIG. 2 is a process flow of resource allocation under non-ideal CSI conditions according to the present invention;
FIG. 3 is a graph of time delay versus number of antennas when using the method of the present invention;
figure 4 is a graph of time delay versus transmit power when using the method of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a technical scheme that: the MEC system considered by the invention is provided with a BS of a large-scale antenna array and supports a plurality of single-antenna users to perform migration calculation
A group of users is represented by a list of users,
representing antennas, each user
It results in a computationally intensive task that includes two parameters: input data L k And a computational requirement W k The computational task then goes through two phases: a migration phase and a calculation phase. User k sends its input data to the BS through the wireless channel, and when the BS receives the data, the BS needs to allocate the computing resources to the tasks and perform the computation, so the total delay time t for the task execution of user k is
In the above formula, T k Representing the time consumed by the migration phase, Q k Representing the time consumed by the calculation phase, R k Indicating data migration rate, f k Representing the computing resources allocated by the MEC server to user k, and f k Satisfy the requirement of
F denotes the computing power of the MEC server.
Considering that a massive MIMO system has two cases of ideal and non-ideal Channel State Information (CSI), the present invention designs different joint resource allocation schemes for the two cases.
The method comprises the following specific steps:
for the case of an ideal CSI,
the method comprises the following steps: calculating the channel gain of each user according to the channel state information, and normalizing, and recording as h k Substituting the data into the formula (2) to obtain the data migration rate R k ；
R k ＝log 2 (1+P k h k ) (2)
Wherein
Which is indicative of the power of the transmission,
representing the maximum transmission power.
Step two: considering the maximum power and energy constraint existing in practice and the fairness among users, the maximum energy constraint is satisfied
In the case of (1), that is, the equation (3) holds true, the actual maximum transmission power is obtained by algebraic operation, as shown in (4), and then P is obtained by equation (5) k Of (2) an optimal solution
Where W (x) represents a Lambert-W function.
Step three: according to P k Of (2) an optimal solution
For the ideal CSI situation, the wireless resource allocation is carried out, and f is allocated to each user according to the formula (6) k All users are allowed to realize the same overall delay, so that the joint allocation of wireless and computing resources under the condition of ideal CSI is completed, and finally, the minimized delay time is obtained by the formula (1).
Wherein the delay time t is initialized to the lower limit t according to the expressions (7) and (8) low And an upper limit t up And t is obtained by equation (9) of the bisection method mid 。
Substituting equation (9) into equation (6) to obtain f k And verify
And the size of F, if
Will t mid An upper limit of t up Then f is obtained by the following formulae (6) and (9) k The optimal solution of (a); if it is
Will t mid Giving a lower limit of t low And the same treatment is carried out to obtain f k Is determined by the optimal solution of (a) to (b),the resource allocation scheme in the case of ideal CSI is completed through the above steps.
For the case of non-ideal CSI,
the method comprises the following steps: considering that the CSI under this condition is complex and there is large-scale fading in the channel resources, the effective channel gains of the users are normalized and then recorded as h k And h i ', and R is obtained by approximation of the formula (8) k Comprises the following steps:
the following steps are similar to the design of the ideal CSI case (R) k ，h k And h i ' non-ideal CSI satisfied case), successively find P k And f k But at the maximum energy constraint
And when the total delay time t is reached, the result obtained by the formulas (1) and (3) is non-convex, and an optimal solution cannot be obtained, so that the invention designs a continuous convex approximation method:
the method comprises the following steps: the problem (3) is equivalently transformed into (11), where ω is k Is an auxiliary variable and satisfies the formula (12).
Step two: the problem (12) is further subjected to equivalent transformation and transformed into (13), wherein y is also an auxiliary variable and satisfies the expression (14).
ω k y≤P k h k (13)
By means of auxiliary variable ω k And y constrains the maximum energy
And the total delay time t is converted to a convex constraint.
Step three: substituting the equations (12) and (13) into the equation (15) to perform iterative operation to continuously update omega k And y until the above process converges, at which time ω is obtained k And y.
Wherein y is [n] And
y and ω for the nth iteration, respectively k The value is obtained.
Step four: will omega k The optimal solution is substituted into the formula (11) to obtain the energy consumed by the migration of the calculation task, and finally, the P is obtained through the formulas (4) and (5) k By (6), (7), (8) and (9) to obtain f k Thereby completing a joint resource allocation scheme in the case of non-ideal CSI.
The benefit of the present invention is that it clearly illustrates the advantages of applying massive MIMO techniques to mobile edge computation, while taking into account resource allocation under different channel state information. The invention can utilize limited computing resources to minimize time delay under the condition of maximum transmission power and energy consumption constraint by joint distribution of wireless and computing resources while carrying out a large amount of user task migration computation, and can also ensure stable migration rate and less signal overhead by using the invention.
Referring to fig. 1 to 4 of the drawings,
fig. 1 is a processing flow for resource allocation under an ideal CSI condition according to the present invention, which first obtains a migration data rate according to the CSI, then obtains a corresponding power according to a maximum energy constraint condition, compares the corresponding power with a maximum power constraint condition to find a power optimal solution, and finally allocates corresponding calculation resources to each user according to the power optimal solution and a set initial total time delay, so as to ensure that the time delays of all users are the same. In addition, when the computing resources are distributed, the upper limit and the lower limit of the total time delay are firstly set, and then the initial total time delay is obtained according to the dichotomy. If the sum of the computing resources distributed by all the users does not exceed the specified computing resources, the initial total time delay is given to the upper limit of the total time delay, and the total time delay condition met by the computing resources required by the users is obtained by utilizing the dichotomy calculation again; and otherwise, endowing the initial total time delay to the lower limit of the total time delay, and so on to complete the design and allocation of the computing resources required by the user, thereby completing the design of the combined scheme of the wireless resources and the computing resources.
Fig. 2 is a processing flow for resource allocation under the condition of non-ideal CSI, the process of the processing flow is similar to that under the condition of ideal CSI, the only difference is that because the condition of non-ideal CSI is complex, and the constraint conditions do not satisfy convexity, the convex approximation technique is adopted to introduce auxiliary variables to convert the maximum energy constraint problem into the constraint conditions satisfying convexity, so as to obtain the optimal solution of power and the optimal solution of computational resources through iterative operation, thereby completing the design of the joint scheme of wireless resources and computational resources.
Fig. 3 is a graph of time delay versus number of antennas when using the method of the present invention. It can be seen from the figure that in both the ideal CSI and the non-ideal CSI, the delay decreases with the increase of the number of antennas, which indicates the effectiveness of the present invention in applying massive MIMO technology.
Figure 4 is a graph of time delay versus transmit power when using the method of the present invention. As can be seen from the figure, the latency of the proposed joint allocation scheme is reduced, so the invention can be used to optimize the moving edge calculation.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (1)
1. A method for minimizing time delay of moving edge calculation based on massive MIMO is characterized in that the method comprises the following steps:
s1: channel gain normalization processing is carried out under the conditions of ideal CSI and non-ideal CSI, and the problem of time delay minimization of transmission power distribution, calculation resource distribution and maximum allowable energy consumption joint optimization is respectively established under the two conditions;
s2: solving the optimal solution of computing resource allocation under the condition of ideal CSI by an iterative optimization method based on a dichotomy;
obtaining a transmission rate R k ＝log 2 (1+P k h k ), (1)
Wherein h is k Is the normalized channel gain, P, of the k-th user k User transmission power, combined optimization of transmission rate, maximum allowed energy consumption and total time delay minimization of computing resources are realized, an auxiliary variable t is introduced, and the optimization problem is written as
Wherein L is k Is the user k input data size, W k Is the CPU computational requirement, f, required to process user k data k Is the computing resource allocated to user k, F is the computing power of the MEC server,
is the maximum transmission power that the user is allowed to transmit,
is the maximum energy constraint of user k, whose computational resource allocation f is based on achieving the same user delay k Satisfy the requirement of
Wherein
Is P k Optimal solution of, t mid By upper limit
And lower limit
Dichotomy, then verifying ∑ k∈κ f k And the size of F; if sigma k∈κ f k < F, will t mid An upper limit of t up Then f is obtained through the formula (3) in turn k The optimal solution of (a); if sigma k∈κ f k If > F, then t is mid Giving a lower limit of t low And the same treatment is carried out to obtain f k The optimal solution of (a);
s3: reducing the complexity under the condition of non-ideal CSI by using an iterative optimization method based on successive convex approximation;
in the case of non-ideal CSI:
obtaining a transmission rate of
Wherein h is k And h' i The effective normalized channel gain obtained for different users is rewritten as the optimization problem
Then, a successive convex approximation is applied to process a non-convex problem (5), and then an auxiliary variable omega is introduced k Y, rewrite the original problem to
Wherein
And
respectively representing auxiliary variables omega at the nth iteration k And y, which is obviously a convex optimization problem, which is easy to solve.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107846704A (en) * | 2017-10-26 | 2018-03-27 | 北京邮电大学 | A kind of resource allocation and base station service arrangement method based on mobile edge calculations |
| US10110495B1 (en) * | 2017-11-22 | 2018-10-23 | Intel Corporation | Multi-access edge computing (MEC) service provision based on local cost measurements |
| CN109089272A (en) * | 2018-09-21 | 2018-12-25 | 浙江工业大学 | The mobile edge calculations Brunt formula time delay optimization method based on non-orthogonal multiple access in more base station scenes |
| CN109814951A (en) * | 2019-01-22 | 2019-05-28 | 南京邮电大学 | The combined optimization method of task unloading and resource allocation in mobile edge calculations network |
| CN109951897A (en) * | 2019-03-08 | 2019-06-28 | 东华大学 | A kind of MEC discharging method under energy consumption and deferred constraint |
| CN110062026A (en) * | 2019-03-15 | 2019-07-26 | 重庆邮电大学 | Mobile edge calculations resources in network distribution and calculating unloading combined optimization scheme |
-
2020
- 2020-03-08 CN CN202010154662.XA patent/CN111464216B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107846704A (en) * | 2017-10-26 | 2018-03-27 | 北京邮电大学 | A kind of resource allocation and base station service arrangement method based on mobile edge calculations |
| US10110495B1 (en) * | 2017-11-22 | 2018-10-23 | Intel Corporation | Multi-access edge computing (MEC) service provision based on local cost measurements |
| CN109089272A (en) * | 2018-09-21 | 2018-12-25 | 浙江工业大学 | The mobile edge calculations Brunt formula time delay optimization method based on non-orthogonal multiple access in more base station scenes |
| CN109814951A (en) * | 2019-01-22 | 2019-05-28 | 南京邮电大学 | The combined optimization method of task unloading and resource allocation in mobile edge calculations network |
| CN109951897A (en) * | 2019-03-08 | 2019-06-28 | 东华大学 | A kind of MEC discharging method under energy consumption and deferred constraint |
| CN110062026A (en) * | 2019-03-15 | 2019-07-26 | 重庆邮电大学 | Mobile edge calculations resources in network distribution and calculating unloading combined optimization scheme |
Non-Patent Citations (5)
| Title |
|---|
| A Scalable Energy vs. Latency Trade-Off in Full-Duplex Mobile Edge Computing Systems;Mahmoud T. Kabir and Christos Masouros;《IEEE Transactions on Communications》;20190831;第67卷(第8期);第5848-5861页 * |
| Joint Pilot and Data Transmission Power Control and Computing Resource Allocation for the Massive MIMO Based MEC Network;Tiancong Huang et al;《2019 IEEE 19th International Conference on Communication Technology》;20200102;摘要，第2-3部分 * |
| Offloading in Edge Computing-Enabled Cell-Free Massive MIMO Systems;Sudarshan Mukherjee and Jemin Lee;《2018 IEEE Globecom Workshops》;20190221;全文 * |
| 移动边缘计算中数据缓存和计算迁移的智能优化技术;汪海霞 等;《中兴通讯技术》;20180430;第24卷(第2期);第15-18页 * |
| 超密集网络中基于移动边缘计算的任务卸载和资源优化;张海波 等;《电子与信息学报》;20190531;第41卷(第5期);第1194-1201页 * |
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