CN112954592B - Energy consumption optimization method for D2D-MEC system - Google Patents

Energy consumption optimization method for D2D-MEC system Download PDF

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CN112954592B
CN112954592B CN202110183088.5A CN202110183088A CN112954592B CN 112954592 B CN112954592 B CN 112954592B CN 202110183088 A CN202110183088 A CN 202110183088A CN 112954592 B CN112954592 B CN 112954592B
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CN112954592A (en
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蔡艳
冉亮
张军
朱洪波
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Nanjing University of Posts and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/70—Services for machine-to-machine communication [M2M] or machine type communication [MTC]
    • 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
    • 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 an energy consumption optimization method for a D2D-MEC system, which comprises the following steps: s1, defining that the D2D-MEC system at least comprises a base station provided with an edge cloud server, a task-type user and a resource-type user; s2, establishing a cellular link between the task-type user and the edge cloud server, and acquiring link channel information of the cellular link; s3, establishing a D2D link between the task type user and the resource type user, and acquiring link channel information of the D2D link; s4, on the premise of minimum energy consumption, partial computing tasks in the task type users are respectively unloaded to the resource type users and the edge cloud server, and energy consumption optimization of the D2D-MEC system is achieved. The invention adopts a task allocation mode of cache loading and unloading and combines D2D communication auxiliary calculation, thereby effectively reducing the total energy consumption of the system, improving the endurance capacity of the battery of the intelligent mobile equipment and prolonging the service life of the mobile edge network.

Description

Energy consumption optimization method for D2D-MEC system
Technical Field
The invention relates to a system energy consumption optimization method, in particular to an energy consumption optimization method for a D2D-MEC system, and relates to the technical field of D2D communication and MEC.
Background
With the rapid development of the internet of things and the 5G mobile communication technology in recent years, various mobile terminal services and applications with different types and different functions have become part of people's daily life, such as online games, live webcasts, virtual reality, augmented reality, and the like. Although these emerging mobile-side services and applications can enrich people's lives, they also occupy a large amount of resources such as computation, storage, networks, and batteries on smart mobile devices. In order to solve the problems of the smart Mobile device caused by insufficient resources, the MEC (Mobile Edge Computing) technology has come to work, which offloads the Computing task of the user to the Edge server, and then expands the resources of the smart Mobile device by using the strong Computing power of the Edge server, so that the MEC is an efficient technical solution.
The D2D (Device-to-Device) communication mainly carries out communication through a direct link between user terminals, and data transmission does not need to pass through a base station, so that spectrum resource efficiency can be effectively improved, energy consumption can be reduced, and real-time performance and reliability of the system can be enhanced, which is one of the keys of the 5G mobile communication technology. Meanwhile, the request heterogeneity and the proximity of the devices in the D2D communication network also make the distributed unloading based on the D2D an important way for relieving the unloading pressure of the edge computing.
In the D2D communication, because the terminal communication distance is short and the transmission power between the terminal devices is small, compared with the conventional method, the method consumes less energy and can effectively reduce the energy consumption of the user. However, with the combination of mobile applications typified by online games, virtual reality, and the like and technologies such as artificial intelligence, big data, and the like at the present stage, the battery capacity of smart mobile devices has not been able to fully satisfy the demand of such mobile applications with complex data processing functions. In addition, the cruising ability of the battery directly influences the user experience of the user of the intelligent mobile device, so that research on minimizing energy consumption in the MEC system based on D2D communication is necessary, and the pressure on the battery of the mobile device is reduced.
In summary, if a completely new energy consumption optimization method for the D2D-MEC system can be designed, it will be helpful to provide great help for the subsequent development of D2D communication, MEC, etc.
Disclosure of Invention
In view of the above-mentioned drawbacks of the prior art, the present invention aims to provide a method for optimizing energy consumption of a D2D-MEC system, which is as follows.
An energy consumption optimization method for a D2D-MEC system comprises the following steps:
s1, defining the D2D-MEC system, and enabling the D2D-MEC system to at least comprise a base station provided with an edge cloud server, a task-type user and a resource-type user;
s2, establishing a cellular link between the task-type user and the edge cloud server, and acquiring link channel information of the cellular link;
s3, acquiring the position information of the resource type user through the task type user, establishing a D2D link between the task type user and the resource type user, and acquiring the link channel information of the D2D link;
s4, on the premise of minimum energy consumption, the task type user respectively unloads part of the computing tasks to the resource type user and the edge cloud server, and energy consumption optimization of the D2D-MEC system is achieved.
Preferably, the step of S1 specifically includes: defining that at least one base station equipped with an edge cloud server and two users exist in a D2D-MEC system, wherein one of the two users has a large number of intensive computing tasks and cannot complete the computing tasks only by means of local computing resources, and is called a task-type user, the other user has fewer computing tasks, can complete the computing tasks by means of the local computing resources and has computing resources in an idle state, and the other user is called a resource-type user; the time for processing the computing task by the D2D-MEC system is defined as T, which may be divided into N time slots, and the length of each time slot is τ ═ T/N.
Preferably, in S2, the link channel information of the cellular link includes: distance x of cellular link n Channel gain of cellular link
Figure BDA0002941964810000031
Channel bandwidth B 1 Noise power
Figure BDA0002941964810000032
Task offloaded transmit power
Figure BDA0002941964810000033
And a channel transmission rate of the cellular link of
Figure BDA0002941964810000034
Wherein
Figure BDA0002941964810000035
Preferably, in the S3 step, the link channel information of the D2D link includes: distance y of D2D link n Channel gain of D2D link
Figure BDA0002941964810000036
Channel bandwidth B 0 Power of noise
Figure BDA0002941964810000037
Task offloaded transmit power
Figure BDA0002941964810000038
And channel transmission rate of the D2D link
Figure BDA0002941964810000039
Wherein, the first and the second end of the pipe are connected with each other,
Figure BDA00029419648100000310
preferably, the step S4 specifically includes:
s41, defining the calculation task in the task type user as A in the nth time slot n The computing task in the resource type user is B n The task amount that the task type user can complete by means of local computing resources is l n The task amount unloaded to the edge cloud server by the task type user is e n The task amount unloaded from the task type user to the resource type user is d n The calculation task amount of the resource type user is h n The number of CPU cycles required by the task type user to calculate 1bit data is C tu The number of CPU cycles required by the resource type user to calculate 1bit data is C ru The capacitance conversion coefficient of the task type user is gamma tu The capacitance conversion coefficient of the resource type user is gamma ru ；
S42, defining the task type user to calculate l by means of local computing resource n The energy consumption of bit data is
Figure BDA0002941964810000041
The task-based user uninstalls e n The energy consumption of bit data to the edge cloud server is
Figure BDA0002941964810000042
The task-based user offload d n The energy consumption of bit data to the resource type user is
Figure BDA0002941964810000043
The resource type user calculates h n The bit data has an energy consumption of
Figure BDA0002941964810000044
Wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0002941964810000045
Figure BDA0002941964810000046
Figure BDA0002941964810000047
Figure BDA0002941964810000048
s43, establishing an optimization problem on the premise of the minimum energy consumption of the D2D-MEC system as follows,
Figure BDA0002941964810000049
Figure BDA00029419648100000410
Figure BDA00029419648100000411
Figure BDA00029419648100000412
Figure BDA0002941964810000051
wherein l i ≥0,d i ≥0,e i ≥0，h i ≥0；
S44, solving the optimization problem in S43 through a KKT condition,
Figure BDA0002941964810000053
wherein the content of the first and second substances,
Figure BDA0002941964810000054
Figure BDA0002941964810000055
Figure BDA0002941964810000056
Figure BDA0002941964810000057
then, obtaining the optimal solution of the optimization problem through one-dimensional search;
and S45, performing calculation task allocation in the D2D-MEC system according to the optimal solution in the S44, and realizing energy consumption optimization of the D2D-MEC system.
Compared with the prior art, the invention has the advantages that:
the energy consumption optimization method for the D2D-MEC system provided by the invention adopts a task allocation mode of cache loading and unloading and combines D2D communication auxiliary calculation, so that the total energy consumption of data processing in the MEC system is effectively reduced, the cruising ability of a battery of intelligent mobile equipment is improved, and the service life of a mobile edge network is prolonged.
In addition, technical thought of the invention can be used as a basis for technical personnel in the field, and the similar method can be applied to the construction of energy consumption optimization schemes of other systems, so that the method has a very wide overall application prospect.
The following detailed description of the embodiments of the present invention is provided in connection with the accompanying drawings to make the technical solutions of the present invention easier to understand and master.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic diagram of the process of computing task allocation of the D2D-MEC system in the present invention;
FIG. 3 is a schematic view of a scene model of a D2D-MEC system to which the present invention is directed.
Detailed Description
The invention provides an energy consumption optimization method for a D2D-MEC system, which aims to reduce the total energy consumption of the D2D-MEC system in the data processing process and improve the cruising ability of a battery of intelligent mobile equipment.
As shown in fig. 1, a method for optimizing energy consumption of a D2D-MEC system includes the following steps:
s1, defining the D2D-MEC system, and enabling the D2D-MEC system to at least comprise a base station provided with an edge cloud server, a task-type user and a resource-type user;
s2, establishing a cellular link between the task-type user and the edge cloud server, and acquiring link channel information of the cellular link;
s3, acquiring the position information of the resource type user through the task type user, establishing a D2D link between the task type user and the resource type user, and acquiring the link channel information of the D2D link;
s4, on the premise of minimum energy consumption, the task type user respectively unloads partial computing tasks to the resource type user and the edge cloud server, and energy consumption optimization of the D2D-MEC system is achieved.
The above S1 to S4 will be described in detail below.
The step S1 specifically includes: it is defined that at least one Base Station (BS) equipped with an edge cloud server and two users exist in a D2D-MEC system. One of the two users has a large amount of intensive computing tasks and cannot complete the computing tasks only by depending on local computing resources, and the User is called a Task-based User (TU); the other computing task is less, the computing task can be completed by depending on local computing resources, and the computing resources are in an idle state and are called Resource-based users (RUs); defining the time for the D2D-MEC system to process the computing task as T, wherein T can be divided into N time slots, the length of each time slot is tau-T/N, and the task-type users and the resource-type users generate new computing tasks in each time slot.
In step S2, the link channel information of the cellular link includes: distance x of cellular link n Channel gain of cellular link
Figure BDA0002941964810000071
Channel bandwidth B 1 Noise power
Figure BDA0002941964810000072
Task offloaded transmit power
Figure BDA0002941964810000073
And a channel transmission rate of the cellular link of
Figure BDA0002941964810000074
Wherein
Figure BDA0002941964810000075
In step S3, the link channel information of the D2D link includes: distance y of D2D link n Channel gain of D2D link
Figure BDA0002941964810000076
Channel bandwidth B 0 Power of noise
Figure BDA0002941964810000077
Task offloaded transmit power
Figure BDA0002941964810000078
And channel transmission rate of the D2D link
Figure BDA0002941964810000079
Wherein, the first and the second end of the pipe are connected with each other,
Figure BDA00029419648100000710
the step S4 specifically includes:
s41, as shown in FIG. 2, defining the calculation task in the task type user as A in the nth time slot n The computing task in the resource type user is B n The task amount that the task type user can complete by means of local computing resources is l n The task amount unloaded to the edge cloud server by the task type user is e n The task amount unloaded from the task type user to the resource type user is d n The calculation task amount of the resource type user is h n The number of CPU cycles required by the task type user to calculate 1bit data is C tu The number of CPU cycles required by the resource type user to calculate 1bit data is C ru The capacitance conversion coefficient of the task-type user is gamma tu The resource type user has a capacitance conversion coefficient of gamma ru ；
S42, defining the task type user to rely on local computing resource to compute l n The energy consumption of bit data is
Figure BDA0002941964810000081
The task-based user uninstalls e n The energy consumption of bit data to the edge cloud server is
Figure BDA0002941964810000082
The task-based user offload d n The energy consumption of bit data to the resource type user is
Figure BDA0002941964810000083
The resource type user calculates h n The energy consumption of bit data is
Figure BDA0002941964810000084
Wherein the content of the first and second substances,
Figure BDA0002941964810000085
Figure BDA0002941964810000086
Figure BDA0002941964810000087
Figure BDA0002941964810000088
s43, establishing a specific optimization problem on the premise of the minimum energy consumption of the D2D-MEC system as follows,
Figure BDA0002941964810000089
Figure BDA00029419648100000810
Figure BDA0002941964810000091
Figure BDA0002941964810000092
Figure BDA0002941964810000093
wherein l i ≥0,d i ≥0,e i ≥0,h i ≥0；
S44, solving the optimization problem in S43 through KKT Conditions (Karush-Kuhn-Tucker Conditions),
Figure BDA0002941964810000095
wherein the content of the first and second substances,
Figure BDA0002941964810000096
Figure BDA0002941964810000097
Figure BDA0002941964810000098
Figure BDA0002941964810000099
then obtaining the optimal solution of the optimization problem through one-dimensional search;
and S45, performing calculation task allocation in the D2D-MEC system according to the optimal solution in the S44, and realizing energy consumption optimization of the D2D-MEC system.
As will be briefly described below with reference to fig. 3, a base station equipped with an edge cloud server and two users located near the base station are provided in the D2D-MEC system, and both users can establish a cellular link with the base station, and the task-type user can offload computing tasks to the edge cloud server, but the resource-type user does not offload computing tasks. A D2D link may be established between the task-type user and the resource-type user, and the task-type user may offload computing tasks to the resource-type user, assuming the resource-type user is willing to assist the task-type user in processing computing tasks. The cellular link and the D2D link use different frequency bands to ensure that the two communication modes do not interfere with each other. The task type users and the resource type users generate new computing tasks in each time slot, and it is assumed that the computing tasks generated in the current time slot can be processed only in the current time slot and the subsequent time slots. The two users have buffer queues, the calculation tasks generated by the current time slot and the previous time slot firstly enter the buffer queues, and then the tasks are distributed, so that the buffer queues are ensured to be emptied at the last time slot.
In summary, the energy consumption optimization method for the D2D-MEC system provided by the invention adopts a task allocation mode of cache loading and unloading in combination with D2D communication auxiliary computation, so that the total energy consumption of data processing in the MEC system is effectively reduced, the cruising ability of the battery of the intelligent mobile device is improved, and the service life of the mobile edge network is prolonged.
In addition, the technical idea of the invention can be used as a basis, and the similar method can be applied to the construction of energy consumption optimization schemes of other systems, so that the overall application prospect of the method is very wide.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Finally, it should be understood that although the present description refers to embodiments, not every embodiment contains only a single technical solution, and such description is for clarity only, and those skilled in the art should integrate the description, and the technical solutions in the embodiments can be appropriately combined to form other embodiments understood by those skilled in the art.

Claims (4)

1. A method for optimizing energy consumption of a D2D-MEC system is characterized by comprising the following steps:
s1, defining the D2D-MEC system, and enabling the D2D-MEC system to at least comprise a base station provided with an edge cloud server, a task-type user and a resource-type user;
s2, establishing a cellular link between the task-type user and the edge cloud server, and acquiring link channel information of the cellular link;
s3, acquiring the position information of the resource type user through the task type user, establishing a D2D link between the task type user and the resource type user, and acquiring the link channel information of the D2D link;
s4, on the premise of minimum energy consumption, the task type user respectively unloads part of the computing tasks to the resource type user and the edge cloud server, so as to realize energy consumption optimization of the D2D-MEC system, and the method specifically comprises the following steps:
s41, defining the calculation task in the task type user as A in the nth time slot n The computing task in the resource type user is B n The task amount that the task-type user can complete by means of local computing resources is l n The task amount of the task type user to be unloaded to the edge cloud server is e n The task amount unloaded from the task type user to the resource type user is d n The self calculation task amount of the resource type user is h n The number of CPU cycles required by the task type user to calculate 1bit data is C tu The number of CPU cycles required by the resource type user to calculate 1bit data is C ru The capacitance conversion coefficient of the task-type user is gamma tu The capacitance conversion coefficient of the resource type user is gamma ru ；
S42, defining the task type user to calculate l by means of local computing resource n The energy consumption of bit data is
Figure DEST_PATH_IMAGE002
Said task-based user uninstalling e n The energy consumption of bit data to the edge cloud server is
Figure DEST_PATH_IMAGE004
Said task-based user uninstalling d n The energy consumption of bit data to the resource type user is
Figure DEST_PATH_IMAGE006
The resource type user calculates h n The energy consumption of bit data is
Figure DEST_PATH_IMAGE008
(ii) a Wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE010
，
Figure DEST_PATH_IMAGE012
，
Figure DEST_PATH_IMAGE014
，
Figure DEST_PATH_IMAGE016
τ is the length of each slot;
s43, on the premise of the minimum energy consumption of the D2D-MEC system, establishing an optimization problem as follows,
Figure DEST_PATH_IMAGE018
Figure DEST_PATH_IMAGE020
wherein l i ≥0，d i ≥0，e i ≥0，h i ≥0；
S44, solving the optimization problem in S43 through a KKT condition,
Figure DEST_PATH_IMAGE022
wherein, the first and the second end of the pipe are connected with each other,
Figure DEST_PATH_IMAGE024
then, obtaining the optimal solution of the optimization problem through one-dimensional search;
s45, according to the optimal solution in S44, the calculation task distribution in the D2D-MEC system is executed, and the performance of the D2D-MEC system is realized
And (5) consumption optimization.
2. The energy consumption optimization method for the D2D-MEC system according to claim 1, wherein the step S1 specifically comprises: defining that at least one base station equipped with an edge cloud server and two users exist in a D2D-MEC system, wherein one of the two users has a large number of intensive computing tasks and cannot complete the computing tasks only by means of local computing resources, and is called a task-type user, the other user has fewer computing tasks, can complete the computing tasks by means of the local computing resources and has computing resources in an idle state, and the other user is called a resource-type user; the time for the D2D-MEC system to process the computation task is defined as T, and T may be divided into N time slots, and the length of each time slot is τ ═ T/N.
3. The method as recited in claim 2A method for optimizing energy consumption of a D2D-MEC system, wherein in step S2, link channel information of the cellular link comprises: distance x of cellular link n Channel gain of cellular link
Figure DEST_PATH_IMAGE026
Channel bandwidth B 1 Noise power
Figure DEST_PATH_IMAGE028
Task offloaded transmit power
Figure DEST_PATH_IMAGE030
And a channel transmission rate of the cellular link of
Figure DEST_PATH_IMAGE032
(ii) a Wherein
Figure DEST_PATH_IMAGE034
。
4. The method for optimizing energy consumption of the D2D-MEC system according to claim 3, wherein in step S3, the link channel information of the D2D link includes: distance y of D2D link n Channel gain of D2D link
Figure DEST_PATH_IMAGE036
Channel bandwidth B 0 Power of noise
Figure DEST_PATH_IMAGE038
Task offloaded transmit power
Figure DEST_PATH_IMAGE040
And channel transmission rate of the D2D link
Figure DEST_PATH_IMAGE042
(ii) a Wherein
Figure DEST_PATH_IMAGE044
。
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