CN115278784A - Method and system for joint optimization of task bandwidth and power of wireless user based on MEC - Google Patents

Method and system for joint optimization of task bandwidth and power of wireless user based on MEC Download PDF

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CN115278784A
CN115278784A CN202210883630.2A CN202210883630A CN115278784A CN 115278784 A CN115278784 A CN 115278784A CN 202210883630 A CN202210883630 A CN 202210883630A CN 115278784 A CN115278784 A CN 115278784A
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user
base station
tasks
task
bandwidth
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朱佳
李亚利
邹玉龙
王宇靖
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Nanjing University of Posts and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/16—Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
    • H04W28/18—Negotiating wireless communication parameters
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/16—Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
    • H04W28/18—Negotiating wireless communication parameters
    • H04W28/20—Negotiating bandwidth
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/16—Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
    • H04W28/18—Negotiating wireless communication parameters
    • H04W28/22—Negotiating communication rate
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention discloses a wireless user task bandwidth power joint optimization method and a wireless user task bandwidth power joint optimization system based on Mobile Edge Computing (MEC), and aims to reduce the average processing delay of a multi-user task. The user unloads all tasks to a Small Base Station (SBS), and when part of tasks are calculated by the small base station, the rest tasks are forwarded to a Macro Base Station (MBS) by the small base station for calculation. Under the condition that the total amount of wireless resources such as transmission bandwidth and power of different user tasks is limited, the invention constructs the minimization problem of average processing time delay of all user tasks, provides a joint distribution method of user transmission bandwidth and task proportion and bandwidth power of different users forwarded by a small base station, and designs an iterative optimization algorithm based on alternately optimized user tasks, bandwidth and power. Compared with the condition of equal distribution of tasks, bandwidth and power, the bandwidth and power joint optimization method for the user tasks reduces the average processing delay of all the user tasks.

Description

Method and system for joint optimization of task bandwidth and power of wireless user based on MEC
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a method and a system for joint optimization of task bandwidth and power of wireless users based on MEC.
Background
With the rapid development of wireless communication technology, the requirements of users on network experience are higher and higher. The occurrence of the mobile edge calculation sinks the calculation capability to the mobile edge node, thereby greatly reducing the communication time delay and improving the network experience of users. In the mobile edge calculation, the networking mode for deploying the small base station SBS is regarded as a key solution for realizing higher data rate and lower delay, is more and more widely used in the communication field, and has important practical significance to the communication technology.
In addition, reasonable resource allocation also has an important impact on reducing latency. In a multi-user scenario in the networking mode, when a certain user task is heavy and is far away from a Small Base Station (SBS), the user cannot well perform task offloading calculation, so that the average processing delay of all user tasks is large. Therefore, how to reasonably allocate resources and perform joint optimization of tasks, power and bandwidth to minimize the average processing delay of all user tasks is very important.
Disclosure of Invention
The invention aims to provide a method and a system for joint optimization of task bandwidth and power of wireless users based on MEC (media independent coding) to solve the technical problem that in the prior art, when a certain user task is heavy and is far away from a Small Base Station (SBS), the user cannot well perform task unloading calculation, so that the average processing delay of all user tasks is long.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
in a first aspect, a method for jointly optimizing task bandwidth and power of wireless users based on MEC is provided, wherein task processing of N users is executed in parallel, in order to avoid loss of generality, the nth user is represented as a user N, N =1,2,. Calculating the processing time delay of the user task, wherein the processing time delay of the user task comprises the cooperative calculation time of the user task and the transmission time of the user for unloading the task to the small base station; the cooperative computing time of the user task is the maximum value between the time required by the part of the tasks left in the small base station for computing and the time required by the rest tasks forwarded to the macro base station for computing; and calculating the average processing time delay of all user tasks, and performing joint distribution of tasks, power and bandwidth by taking bandwidth and power as constraints to minimize the average processing time delay of all user tasks.
Further, the transmission time for the user n to unload the task to the small cell is:
Figure BDA0003765205570000021
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003765205570000022
indicating the transmission time for user n to offload tasks to small base stations, DnRepresenting the amount of tasks for user n,
Figure BDA0003765205570000023
representing the transmission rate of the user n to the small base station;
Figure BDA0003765205570000024
wherein, 0 is less than or equal to alpha n1 or less represents the bandwidth allocation ratio from user n to small base station, Bu,SRepresenting the total bandwidth of the user to the small cell,
Figure BDA0003765205570000025
for the transmit power of the user n,
Figure BDA0003765205570000026
representing small scale fading, N, of user N to small base station0Representing a gaussian white noise power spectral density,
Figure BDA0003765205570000027
representing the large-scale transmission loss from the user n to the small base station;
Figure BDA0003765205570000028
wherein the content of the first and second substances,
Figure BDA0003765205570000029
denotes the transmission distance, a, of a user n to the small base stationnRepresenting the path loss factor of user n to the small base station.
Further, the collaborative computing time of the task of the user n is as follows:
Figure BDA0003765205570000031
wherein, TnRepresenting the collaborative computing time of the user n task,
Figure BDA0003765205570000032
indicating the time required for the user n part of the task to remain in the small cell for calculation,
Figure BDA0003765205570000033
the remaining tasks for user n are forwarded by the small cell to the macro cell transmission time,
Figure BDA0003765205570000034
calculating the time required by the macro base station for the remaining tasks of the user n;
Figure BDA0003765205570000035
Figure BDA0003765205570000036
Figure BDA0003765205570000037
wherein, D'nRepresenting the amount of tasks calculated by user n on the small cell, CsIndicating the number of CPU cycles required for the mobile edge server MEC to compute 1 bit of data at the small cell site, fsRepresenting the CPU frequency of the mobile edge server MEC at the small cell; dnIndicating the task volume of user n, MsRepresenting the number of CPU cycles, F, required for the mobile edge server MEC at the macro base station to compute 1 bit of datasRepresenting the CPU frequency of the mobile edge server MEC at the macro base station;
Figure BDA0003765205570000038
the transmission rate of the small base station for transmitting the residual tasks of the user n to the MBS is represented;
Figure BDA0003765205570000039
wherein beta is not less than 0nLess than or equal to 1 represents the bandwidth allocation proportion of the small base station for forwarding the residual tasks of the user n to the macro base station, BS,MRepresenting the total bandwidth from the small base station to the macro base station,
Figure BDA00037652055700000310
forwarding the remaining tasks of user n for the small cell to the transmit power of the macro cell,
Figure BDA00037652055700000311
indicating that the small cell forwards the remaining tasks of user n to the macro cell in small scale fading,
Figure BDA00037652055700000312
representing the large-scale transmission loss of the small base station for forwarding the residual tasks of the user n to the macro base station;
Figure BDA00037652055700000313
wherein, dS,MIndicating the small cell forwards the remaining tasks of user n toTransmission distance of macro base station, bnAnd representing the path loss factor of the small base station for forwarding the residual tasks of the user n to the macro base station.
Further, the average processing delay of all user tasks is obtained by the following formula:
Figure BDA0003765205570000041
wherein the content of the first and second substances,
Figure BDA0003765205570000042
represents the average processing delay of all user tasks,
Figure BDA0003765205570000043
representing the processing time delay of the task of the user n;
Figure BDA0003765205570000044
further, taking bandwidth and power as constraints, performing joint allocation of tasks, power and bandwidth, minimizing average processing delay of all user tasks, and adopting the following objective function:
Figure BDA0003765205570000045
wherein, PmaxIs the maximum transmit power of the small base station.
Further, solving the objective function by adopting an alternative optimization algorithm comprises: first, due to the variable αnOnly influence
Figure BDA0003765205570000046
The method is independent from other variables, so that the target optimization problem can be split into two independent sub-optimization problems to be processed:
Figure BDA0003765205570000047
Figure BDA0003765205570000051
αn≥0
Figure BDA0003765205570000052
Figure BDA0003765205570000053
C1，C3，C4
for sub-optimization problem P1The convex optimization problem can be solved directly by a convex optimization algorithm; for sub-optimization problem P2Introduction of an auxiliary variable tnLet us order
Figure BDA0003765205570000054
Then sub-optimization problem P2Can be expressed as:
Figure BDA0003765205570000055
wherein, tnIs an introduced auxiliary variable;
secondly, a block coordinate descent method is adopted to solve the non-convex problem P3Into two subproblems, each being given
Figure BDA0003765205570000056
Time-optimized task allocation { D'nSub-problem P of31And given { D'n *Time optimization power bandwidth allocation
Figure BDA0003765205570000057
Sub-problem P of32。
Sub problem P31As followsShown in the figure:
Figure BDA0003765205570000058
Figure BDA0003765205570000061
Figure BDA0003765205570000062
sub problem P31The method is a linear programming problem, and by utilizing a Lagrange KKT method, a closed solution of the optimal task allocation of an objective function can be obtained as follows:
Figure BDA0003765205570000063
wherein, D'n *Representing the optimal amount of tasks calculated by user n on the small cell,
Figure BDA0003765205570000064
Figure BDA0003765205570000065
indicating the best task transmission rate for the small cell to forward the remaining tasks of user n to the macro base station,
Figure BDA0003765205570000066
representing the optimal bandwidth allocation proportion of the small base station to forward the residual tasks of the user n to the macro base station, BS,MRepresenting the total bandwidth from the small base station to the macro base station,
Figure BDA0003765205570000067
forwarding the rest tasks of the user n to the optimal transmitting power of the macro base station for the small base station;
sub problem P32As follows:
Figure BDA0003765205570000068
sub problem P32The method is a convex optimization problem and can be solved by using a classical convex optimization algorithm;
finally, for sub-problem P2Alternative optimization algorithms can be used, respectively by solving the sub-problems P31And sub-problem P32And performing alternate optimization until convergence, thereby obtaining the optimal solution of each variable in the objective function.
In a second aspect, a wireless user task bandwidth power joint optimization system based on MEC is provided, wherein task processing of N users is executed in parallel, in order to avoid loss of generality, the nth user is represented as a user N, N =1,2,. The computing module is used for computing the processing time delay of the user task, wherein the processing time delay of the user task comprises the cooperative computing time of the user task and the transmission time of the user for unloading the task to the small base station; the cooperative computing time of the user task is the maximum value between the time required by the part of the tasks left in the small base station for computing and the time required by the rest tasks forwarded to the macro base station for computing; and the optimization module is used for calculating the average processing time delay of all the user tasks, performing joint distribution of the tasks, the power and the bandwidth by taking the bandwidth and the power as constraints, and minimizing the average processing time delay of all the user tasks.
Compared with the prior art, the invention has the following beneficial effects: the task processing of N users is executed in parallel, all tasks are unloaded to the small base station by the users, part of the tasks are left in the small base station for calculation, the rest tasks are forwarded to the macro base station by the small base station for calculation, and the macro base station and the small base station are both provided with the mobile edge server MEC; and calculating the average processing time delay of all user tasks, and performing joint distribution of tasks, power and bandwidth by taking bandwidth and power as constraints to achieve the optimal distribution of tasks, power and bandwidth, thereby effectively reducing the average processing time delay of all user tasks.
Drawings
Fig. 1 is a schematic diagram of a system model of a MEC-based wireless user task bandwidth power joint optimization system according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of a method for jointly optimizing task bandwidth and power of a wireless user based on MEC according to an embodiment of the present invention;
FIG. 3 is a first simulation of an embodiment of the present invention;
FIG. 4 is a second simulation of an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
The first embodiment is as follows:
as shown in fig. 1 and fig. 2, in a multi-user scenario in a networking mode in which a Small Base Station (SBS) is deployed, in order to avoid loss of generality, an nth user is represented as a user N, where N =1, 2. The task processing of N users is executed in parallel, all tasks are unloaded to the small base station by the users, part of the tasks are left in the small base station for calculation, meanwhile, the rest tasks are forwarded to a Macro Base Station (MBS) by the small base station for calculation, and the macro base station and the small base station are both provided with a mobile edge server (MEC). Calculating the processing time delay of the user task, wherein the processing time delay of the user task comprises the cooperative calculation time of the user task and the transmission time of the user for unloading the task to the small base station; the cooperative computing time of the user task is the maximum value between the time required by the part of the tasks left in the small base station for computing and the time required by the rest tasks forwarded to the macro base station for computing. And calculating the average processing time delay of all user tasks, and performing joint distribution of tasks, power and bandwidth by taking bandwidth and power as constraints to minimize the average processing time delay of all user tasks.
And acquiring small-scale fading and large-scale transmission loss from the user to the SBS and from the SBS to the MBS, and calculating the transmission rate and the transmission time from the user to the SBS.
Figure BDA0003765205570000081
Figure BDA0003765205570000082
Wherein the content of the first and second substances,
Figure BDA0003765205570000083
representing the large scale transmission loss of user n to SBS,
Figure BDA0003765205570000084
indicating that the SBS forwards the remaining tasks of user n to the MBS at a large scale,
Figure BDA0003765205570000085
indicating the transmission distance of user n to SBS, dS,MIndicating the transmission distance, a, from the SBS forwarding the remaining tasks of user n to the MBSnRepresenting the path loss factor from user n to SBS, bnIndicating that the SBS forwards the remaining tasks of user n to the MBS path loss factor.
The transmission rate from user n to SBS is calculated as follows:
Figure BDA0003765205570000091
wherein the content of the first and second substances,
Figure BDA0003765205570000092
representing the transmission rate of user n to SBS, 0 ≦ α n1 denotes the bandwidth allocation ratio of user n to SBS, Bu,SRepresents the total bandwidth of the user to the SBS,
Figure BDA0003765205570000093
transmit power for user n,
Figure BDA0003765205570000094
Representing small scale fading, N, of user N to SBS0Representing a gaussian white noise power spectral density.
The transmission time from user n to SBS is calculated as follows:
Figure BDA0003765205570000095
wherein the content of the first and second substances,
Figure BDA0003765205570000096
representing the transmission time of user n to SBS, DnRepresenting the task volume of user n.
The SBS forwards the transmission rate of the remaining tasks of the user n to the MBS, and the calculation formula is as follows:
Figure BDA0003765205570000097
wherein the content of the first and second substances,
Figure BDA0003765205570000098
represents the transmission rate of the residual tasks of the SBS forwarding user n to the MBS, and beta is more than or equal to 0n1 denotes the bandwidth allocation ratio of the SBS forwarding the remaining tasks of user n to MBS, BS,MIndicating the total bandwidth of the SBS to the MBS,
Figure BDA0003765205570000099
forwards the remaining tasks of user n to the transmit power of MBS for SBS,
Figure BDA00037652055700000910
indicating that the SBS forwards the remaining tasks of user n to the MBS small-scale fading,
Figure BDA00037652055700000911
indicating that the SBS forwards the remaining tasks of user n to the MBS at a large scale transmission loss.
The SBS-MBS transmission time of the remaining tasks of user n is calculated as follows:
Figure BDA00037652055700000912
wherein the content of the first and second substances,
Figure BDA00037652055700000913
SBS-MBS Transmission time, D 'for the remaining tasks of user n'nRepresenting the amount of tasks calculated by user n on SBS, DnRepresenting the task volume of user n.
The SBS calculation time of the user n task is calculated according to the following formula:
Figure BDA0003765205570000101
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003765205570000102
representing the time required for the user n to remain in the small base station for calculation, CsRepresenting the number of CPU cycles required for the MEC to compute 1-bit data at SBS, fsRepresenting the CPU frequency of the MEC at SBS.
The MBS calculation time of the user n task has the following calculation formula:
Figure BDA0003765205570000103
wherein the content of the first and second substances,
Figure BDA0003765205570000104
time required for calculation at the macro base station for the remaining tasks of user n, MsRepresenting the number of CPU cycles, F, required by the MEC at the MBS to calculate 1-bit datasThe CPU frequency of the MEC at the MBS is shown.
The collaborative computing time of the user n task is calculated according to the following formula:
Figure BDA0003765205570000105
wherein, TnRepresenting the collaborative computing time of the user n task.
The processing time delay of the user n task is calculated according to the following formula:
Figure BDA0003765205570000106
wherein the content of the first and second substances,
Figure BDA0003765205570000107
representing the processing delay of the user n task.
The average processing delay of all user tasks is calculated as follows:
Figure BDA0003765205570000108
wherein the content of the first and second substances,
Figure BDA0003765205570000109
representing the average processing delay of all user tasks.
Taking bandwidth and power as constraints, performing joint allocation of tasks, power and bandwidth, minimizing average processing time delay of all user tasks, and adopting the following objective function:
Figure BDA00037652055700001010
s.t.C1:D'n≤Dn
Figure BDA0003765205570000111
Figure BDA0003765205570000112
Figure BDA0003765205570000113
Figure BDA0003765205570000114
wherein, PmaxIs the maximum transmit power of the small base station.
And solving the objective function by adopting an alternating optimization algorithm, and minimizing the average processing time delay of all user tasks.
First, due to the variable αnOnly influence
Figure BDA0003765205570000115
The method is independent from other variables, so that the target optimization problem can be split into two independent sub-optimization problems to be processed:
Figure BDA0003765205570000116
Figure BDA0003765205570000117
for sub-optimization problem P1The convex optimization problem can be solved directly by a convex optimization algorithm; for sub-optimization problem P2Introduction of an auxiliary variable tnLet us order
Figure BDA0003765205570000118
Then sub-optimization problem P2Can be expressed as:
Figure BDA0003765205570000119
Figure BDA0003765205570000121
Figure BDA0003765205570000122
Figure BDA0003765205570000123
C1，C3，C4
wherein, tnIs an introduced auxiliary variable.
Secondly, a Block Coordinate Descent method (BCD) is adopted to solve the non-convex problem P3Into two subproblems, each being given
Figure BDA0003765205570000124
Time-optimized task allocation { D'nSub-problem P of31And given { D'n *Time optimization power bandwidth allocation
Figure BDA0003765205570000125
Sub-problem P of32。
Sub problem P31As follows:
Figure BDA0003765205570000126
sub problem P31The method is a Linear Programming (LP) problem, and by using a Lagrangian KKT method, a closed solution of the optimal task allocation of an objective function can be obtained as follows:
Figure BDA0003765205570000127
wherein the content of the first and second substances,
Figure BDA0003765205570000128
D'n *representing the optimal amount of tasks calculated by user n on SBS,
Figure BDA0003765205570000129
indicating that the SBS forwards the remaining tasks of user n to the MBS' optimal task transmission rate,
Figure BDA00037652055700001210
indicating the optimal bandwidth allocation ratio for the SBS to forward the remaining tasks of user n to the MBS, BS,MIndicating the total bandwidth of the SBS to the MBS,
Figure BDA00037652055700001211
the best transmit power for the SBS to forward the remaining tasks for user n to the MBS.
Sub problem P32As follows:
Figure BDA0003765205570000131
sub problem P32The method is a convex optimization problem and can be solved by using a classical convex optimization algorithm.
Finally, for sub-problem P2Alternative optimization algorithms can be used, respectively by solving the sub-problems P31And sub-problem P32And performing alternate optimization until convergence, thereby obtaining the optimal solution of each variable in the objective function.
An example of the implementation of the invention on a computer using MATLAB language simulation is given below. In the simulation, the channels from the user to the SBS and from the SBS to the MBS all obey Rayleigh fading, the variance is 1, N is the number of the users, the value is 3, the proportion of each user task to the total task amount is 1/20,1/20 and 9/10 in turn,
Figure BDA0003765205570000132
the values of the transmission distance from the user n to the SBS are 23m,4m,85m and d respectivelyS,MThe value of the transmission distance from SBS to MBS is 200mnRepresenting the path loss factor from user n to SBS, bnIndicating that the SBS forwarded the remaining tasks of user n toThe values of the path loss factors of the MBS are all 4.CsThe number of CPU cycles required for calculating 1 bit data by the MEC at the SBS is represented, and the value is 1000cycles/bit, fsThe CPU frequency of MEC at SBS is 1.3 × 109cycles/s，MsThe CPU periodicity required by the MEC at the MBS position for calculating 1 bit data is represented, and the value is 1000cycles/bit, FsRepresenting the CPU frequency of MEC at MBS position, with the value of 3 multiplied by 109cycles/s，
Figure BDA0003765205570000133
The value of the transmitting power for each user is 0.5wmaxThe maximum transmitting power of SBS is 10wu,SThe total bandwidth from the user to the SBS is 3MHzS,MRepresenting the total bandwidth from SBS to MBS, 3MHz0Representing gaussian white noise power spectral density, with a value of-174 dBm/Hz.
Fig. 3 is a relationship between the average processing delay and the number of iterations of the scheme provided by the present invention when the total task amount of all users is 4 mbits, and as the number of iterations increases, the average processing delay of the scheme provided by the present invention can converge to its optimal solution, which verifies that the alternating optimization algorithm adopted by the present invention has good convergence performance, which also means that the objective function of the scheme provided by the present invention can be solved through a limited number of iterations. Fig. 4 is a comparison between the proposed scheme of the present invention and the equal allocation scheme of tasks, bandwidth and power, and the argument in the figure is the total task amount of all users, and it can be seen that the proposed scheme of the present invention is smaller than the comparison scheme in terms of average processing delay, and this advantage becomes increasingly significant as the number of tasks increases.
Example two:
the embodiment provides a wireless user task bandwidth power joint optimization system based on MEC, which comprises a macro base station, a small base station and N users, wherein the task processing of the N users is executed in parallel, the users unload all tasks to the small base station, part of the tasks are left in the small base station for calculation, the rest tasks are forwarded to the macro base station by the small base station for calculation, and the macro base station and the small base station are both provided with a mobile edge server MEC.
The computing module is used for computing the processing time delay of the user task, wherein the processing time delay of the user task comprises the cooperative computing time of the user task and the transmission time of the user for unloading the task to the small base station; the cooperative computing time of the user task is the maximum value between the time required by the part of the tasks left in the small base station for computing and the time required by the rest tasks forwarded to the macro base station for computing.
And the optimization module is used for calculating the average processing time delay of all user tasks, performing joint distribution of tasks, power and bandwidth by taking the bandwidth and the power as constraints, and minimizing the average processing time delay of all user tasks.
Embodiments of the present application may be provided as methods, systems, or computer program products. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The solution in the embodiment of the present application may be implemented by using various computer languages, for example, object-oriented programming language Java and transliteration scripting language JavaScript, etc.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.

Claims (7)

1. A wireless user task bandwidth power joint optimization method based on MEC is characterized in that task processing of N users is executed in parallel, in order to keep generality, the nth user is represented as user N, N =1,2,.
Calculating the processing time delay of the user task, wherein the processing time delay of the user task comprises the cooperative calculation time of the user task and the transmission time of the user for unloading the task to the small base station; the cooperative computing time of the user task is the maximum value between the time required by the part of the tasks left in the small base station for computing and the time required by the rest tasks forwarded to the macro base station for computing;
and calculating the average processing time delay of all user tasks, and performing joint distribution of tasks, power and bandwidth by taking bandwidth and power as constraints to minimize the average processing time delay of all user tasks.
2. The MEC-based wireless user task bandwidth power joint optimization method of claim 1, wherein the transmission time for user n to offload tasks to the small cell is:
Figure FDA0003765205560000011
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003765205560000012
indicating the transmission time for user n to offload tasks to small base stations, DnRepresenting the amount of tasks for user n,
Figure FDA0003765205560000013
representing the transmission rate of the user n to the small base station;
Figure FDA0003765205560000014
wherein, 0 is less than or equal to alphan1 or less represents the bandwidth allocation ratio from user n to small base station, Bu,SRepresenting the total bandwidth of the user to the small cell,
Figure FDA0003765205560000015
for the transmit power of the user n,
Figure FDA0003765205560000016
representing small scale fading, N, of user N to small base station0Representing a gaussian white noise power spectral density,
Figure FDA0003765205560000017
representing the large-scale transmission loss from the user n to the small base station;
Figure FDA0003765205560000021
wherein the content of the first and second substances,
Figure FDA0003765205560000022
denotes the transmission distance, a, of a user n to the small base stationnRepresenting the path loss factor of user n to the small base station.
3. The MEC-based wireless user task bandwidth power joint optimization method according to claim 2, wherein the collaborative computation time of user n task is:
Figure FDA0003765205560000023
wherein, TnRepresenting the collaborative computing time of the user n task,
Figure FDA0003765205560000024
indicating the time required for the user n part of the task to remain in the small cell for calculation,
Figure FDA0003765205560000025
the remaining tasks for user n are forwarded by the small base station to the macro base station transmission time,
Figure FDA0003765205560000026
for the remaining tasks of user n in the macroThe time required for the base station to perform calculation;
Figure FDA0003765205560000027
Figure FDA0003765205560000028
Figure FDA0003765205560000029
wherein, D'nRepresenting the amount of tasks calculated by user n on the small cell, CsIndicating the number of CPU cycles required for the mobile edge server MEC to compute 1 bit of data at the small cell site, fsRepresenting the CPU frequency of a mobile edge server (MEC) at a small base station; dnIndicating the task volume of user n, MsIndicating the number of CPU cycles, F, required for the mobile edge server MEC at the macro base station to compute 1 bit of datasRepresenting the CPU frequency of a mobile edge server MEC at a macro base station;
Figure FDA00037652055600000210
the transmission rate of the small base station for transmitting the residual tasks of the user n to the MBS is represented;
Figure FDA00037652055600000211
wherein beta is not less than 0nLess than or equal to 1 represents the bandwidth allocation proportion of the small base station for forwarding the residual tasks of the user n to the macro base station, BS,MRepresenting the total bandwidth from the small base station to the macro base station,
Figure FDA00037652055600000212
forwarding the remaining tasks of user n to the transmit power of the macro base station for the small cell,
Figure FDA0003765205560000031
indicating that the small cell forwards the remaining tasks of user n to the macro cell in small scale fading,
Figure FDA0003765205560000032
representing the large-scale transmission loss of the small base station for forwarding the residual tasks of the user n to the macro base station;
Figure FDA0003765205560000033
wherein d isS,MIndicating the transmission distance from the small base station to the macro base station for forwarding the residual tasks of the user n, bnAnd representing the path loss factor of the small base station for forwarding the residual tasks of the user n to the macro base station.
4. The MEC-based wireless user task bandwidth power joint optimization method of claim 3, wherein the average processing delay of all user tasks is obtained by the following formula:
Figure FDA0003765205560000034
wherein the content of the first and second substances,
Figure FDA0003765205560000035
represents the average processing delay of all user tasks,
Figure FDA0003765205560000036
representing the processing time delay of the task of the user n;
Figure FDA0003765205560000037
5. the MEC-based wireless user task bandwidth power joint optimization method of claim 4, wherein the bandwidth and power are taken as constraints, joint allocation of task, power and bandwidth is performed, average processing delay of all user tasks is minimized, and the following objective function is adopted:
Figure FDA0003765205560000038
C5:D'n≥0,αn≥0,βn≥0,
Figure FDA0003765205560000041
wherein, PmaxIs the maximum transmit power of the small base station.
6. The MEC-based wireless user task bandwidth power joint optimization method according to claim 5, wherein solving the objective function by using an alternating optimization algorithm comprises:
first, due to the variable αnOnly influence
Figure FDA0003765205560000042
And other variables are independent, so that the target optimization problem can be split into two independent sub-optimization problems to be processed:
Figure FDA0003765205560000043
Figure FDA0003765205560000044
for sub-optimization problem P1The convex optimization problem can be solved directly by a convex optimization algorithm; for sub-optimization problem P2Introduction of an auxiliary variable tnLet us order
Figure FDA0003765205560000045
Then sub-optimization problem P2Can be expressed as:
Figure FDA0003765205560000046
wherein, tnIs an introduced auxiliary variable;
secondly, a non-convex problem P is solved by adopting a block coordinate descent method3Into two subproblems, each being given
Figure FDA0003765205560000051
Time-optimized task allocation { D'nSub-problem P of31And given { D'n *Time optimization power bandwidth allocation
Figure FDA0003765205560000052
Sub-problem P of32。
Sub problem P31As follows:
Figure FDA0003765205560000053
sub problem P31The method is a linear programming problem, and by utilizing a Lagrangian KKT method, a closed-form solution of the optimal task allocation of an objective function can be obtained as follows:
Figure FDA0003765205560000054
wherein, D'n *Representing the optimal amount of tasks calculated by user n on the small cell,
Figure FDA0003765205560000055
Figure FDA0003765205560000056
indicating that the small cell forwards the remaining tasks of the user n to the optimal task transmission rate of the macro cell,
Figure FDA0003765205560000057
representing the optimal bandwidth allocation proportion of the small base station to forward the residual tasks of the user n to the macro base station, BS,MRepresenting the total bandwidth from the small base station to the macro base station,
Figure FDA0003765205560000058
forwarding the rest tasks of the user n to the optimal transmitting power of the macro base station for the small base station;
sub problem P32As follows:
Figure FDA0003765205560000059
Figure FDA0003765205560000061
βn≥0,
Figure FDA0003765205560000062
C3，C4
sub problem P32The method is a convex optimization problem, and can be solved by using a classical convex optimization algorithm;
finally, for sub-problem P2Alternative optimization algorithms can be used, respectively by solving the sub-problems P31And sub-problem P32And performing alternate optimization until convergence, thereby obtaining the optimal solution of each variable in the objective function.
7. A wireless user task bandwidth power joint optimization system based on MEC is characterized in that task processing of N users is executed in parallel, in order to avoid loss of generality, the nth user is represented as a user N, N =1,2, wherein N and N are positive integers, the user unloads all tasks to a small base station, part of the tasks are left in the small base station for calculation, the rest tasks are forwarded to a macro base station by the small base station for calculation, and the macro base station and the small base station are both provided with a mobile edge server MEC;
the computing module is used for computing the processing time delay of the user task, wherein the processing time delay of the user task comprises the cooperative computing time of the user task and the transmission time of the user for unloading the task to the small base station; the cooperative computing time of the user task is the maximum value between the time required by the part of the tasks left in the small base station for computing and the time required by the rest tasks forwarded to the macro base station for computing;
and the optimization module is used for calculating the average processing time delay of all user tasks, performing joint distribution of tasks, power and bandwidth by taking the bandwidth and the power as constraints, and minimizing the average processing time delay of all user tasks.
CN202210883630.2A 2022-07-26 2022-07-26 Method and system for joint optimization of task bandwidth and power of wireless user based on MEC Pending CN115278784A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115988536A (en) * 2023-03-20 2023-04-18 南京邮电大学 Dual-reconfigurable intelligent surface assisted mobile edge computing system optimization method

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115988536A (en) * 2023-03-20 2023-04-18 南京邮电大学 Dual-reconfigurable intelligent surface assisted mobile edge computing system optimization method

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