CN113518330A - Multi-user computing unloading resource optimization decision method based on D2D communication - Google Patents
Multi-user computing unloading resource optimization decision method based on D2D communication Download PDFInfo
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Abstract
The invention discloses a multi-user computing unloading resource optimization decision method based on D2D communication, and belongs to the technical field of mobile communication. The method comprises the following steps: 1. establishing a data communication model based on D2D communication; 2. respectively establishing a calculation overhead model comprising time and energy overhead in a task transmission stage and a task execution stage; 3. establishing a global optimization problem for minimizing the total cost calculated by all users of the whole system; 4. establishing a preference sequence of the bilateral user by taking the size of the calculated total overhead as a sequencing basis; 5. and based on the established preference sequence, obtaining resource optimization decisions of calculation and unloading of the multi-user D2D by using a stable matching algorithm. The calculation unloading method based on D2D communication is beneficial to reducing unloading time delay and energy expenditure, obtains resource optimization decision of calculation unloading by using a stable matching algorithm, can effectively reduce the total calculation expenditure of a system compared with a random matching method, and can obtain the performance which is very close to the optimal exhaustive search method by using lower calculation complexity.
Description
Technical Field
The invention relates to the technical field of mobile communication, in particular to a resource optimization decision method for computation and offloading by multiple users through D2D communication, which is a resource optimization decision method capable of improving the shortage of computing resources of a mobile communication network and reducing computation and offloading time delay and energy consumption.
Background
With the development and commercialization of Fifth Generation mobile communication (5G), more and more mobile applications, such as internet of things, augmented reality, video streaming, etc., such new applications that not only require high-speed data transmission but also require delay sensitivity of powerful computing resources are rapidly becoming widespread. To be able to adequately support these applications with special demands on computational resources and latency, Mobile Edge Computing (MEC) has come into play. MEC is a very promising emerging 5G service scheme, mainly using wireless network edge facilities near users or a large number of customer premise equipment collaborations, instead of remote cloud computing, to perform a large number of communication and computing services. Compared with cloud computing, the MEC can provide the capability equivalent to that of cloud computing at the network edge, namely the wireless access network end, so that a user can unload the computing task at a short distance, and the low-delay and flexible computing and communication extension service is realized.
For the offloading problem of user computing tasks, most methods focus on the MEC network resource management based on edge servers, i.e. users can offload computing tasks to the high-speed computing servers at the edge of the network under consideration of wireless communication and mobile computing criteria. Although the computing performance of the mobile network can be improved to some extent by using only the computation offload of the edge server in the MEC network, the limited computing resources of the edge server at the base station are not always sufficient to support all mobile devices in the coverage area. And due to the randomness of the distribution of users in the network, especially for users distributed at the edge of a cell, the distance between the edge server at the base station end and the user may be far, thereby resulting in long offloading delay. To remedy these deficiencies and challenges in MEC networks, incorporating Device-to-Device (D2D) into MEC networks that allow terminals to communicate directly without going through a base station, assisting user computation task offloading with D2D communications is a significant solution.
A large number of heterogeneous devices in a network, such as internet of things devices, smartphones, tablets, etc., can be leveraged to support collaborative computing task execution for multiple services with their diverse computing capabilities and multiplexing gains. A significant reduction in offload distance is a key that is often ignored when computing offload through inter-user collaboration. From a wireless communication perspective, a short transmission distance allows for a higher data transmission rate with lower power consumption, thereby reducing offloading latency and energy consumption when users are offloading tasks over short distances. In consideration of the advantage of D2D offloading, the offloading decision method based on D2D communication assistance is focused, but at present, most of the D2D-assisted computational offloading decisions have very high computational complexity of algorithms, and the optimization goal is to minimize latency, which is a single goal, but the energy consumption of D2D devices is also an important point to be considered, and some methods only use the concept of D2D offloading, and the inter-device communication still adopts cellular communication.
Disclosure of Invention
In view of the above, the present invention is to integrate D2D communication and D2D offloading into an MEC network to form a D2D-MEC network, so as to further reduce offloading latency and energy consumption of users on the basis of minimizing system computation overhead. By modeling the time delay and energy consumption overhead of a computing task in a D2D transmission and execution stage, taking the minimization of the total system computing overhead including time delay and energy consumption as an optimization target, unloading the multi-user computing task and distributing multi-user computing resources, modeling as an integer programming problem of the minimized total system computing overhead, then regarding solving the problem as a D2D pairing process, providing a multi-user computing unloading resource optimization decision method based on D2D communication by using a stable matching algorithm, iteratively solving the optimization distribution decision of D2D unloading, realizing the minimization of the total system computing overhead with low complexity and improving the user experience.
The object of the invention is achieved by a solution, wherein the multi-user communication system comprises dtA mobile user requiring a request for D2D to compute offload, using set Dt＝{1,2,...,i,...,dtDenotes with drAre adjacent to each otherWith a set D, mobile users with free computing resources capable of providing D2D computing offload servicesr＝{1,2,...,j,...,drDenotes d istEach mobile user has an independent and time-delay sensitive computing task which needs to request computing offloading service from neighboring mobile users, and the computing offloading method of D2D is characterized by comprising the following specific steps:
1) establishing a data communication model based on D2D communication to obtain the data rate which can be realized by D2D communication;
2) respectively establishing a calculation overhead model comprising time and energy overhead in a task transmission stage and a task execution stage;
3) establishing a global optimization problem for minimizing the total calculation overhead required by the task unloading of all users of the whole system based on a time and energy overhead model;
4) establishing a preference sequence of the bilateral user by taking the size of the calculated total overhead as a sequencing basis;
5) and based on the established preference sequence, obtaining resource optimization decisions of calculation and unloading of the multi-user D2D by using a stable matching algorithm.
Further, in the step 1), the computation and the unloading among the users need to transmit computation tasks by using D2D communication, and the D2D communication accesses a wireless channel by using an orthogonal frequency division multiple access (ofdma) mode under the control of a cellular network; obtaining the user i belongs to D based on free space propagation path loss and Rayleigh fadingtAnd user j ∈ DrData rate r achievable with D2D communication betweenij。
Further, in the step 2), the computation overhead generated in the task transmission and task execution phases of computation and offloading among users includes time overhead and energy overhead; in the task transmission stage, the total calculation cost of the user i calculation task including time cost and energy cost is
Wherein the content of the first and second substances,
is input data B of the task to be calculated by the user iiIs transmitted to through D2D communicationThe transmission time consumed by the user j,
is the power P of user iiWill input data BiThe energy consumed by the communication transmitted to user j through D2D,
weights representing time overhead and energy overhead, respectively; in the task execution stage, the total calculation cost of the user i for calculating the task, including time cost and energy cost, is
Wherein the content of the first and second substances,
is the computation time consumed by user j to compute the received computation task,
is the computational energy consumed by user j to compute the received computational task.
Further, in the step 3), in order to minimize the time delay in the whole task offloading process and minimize the energy consumption of the user, the user experience is improved, so as to minimize the total calculation overhead in the task offloading process of all the users in the whole system, that is, the total time overhead and energy overhead including the task transmission and task execution stages are used as the objective function, and a linear global optimization problem is established.
Further, in the step 4), in order to solve the established optimization problem with low complexity, the requested user set D is unloadedtAnd a set of users D of the computing offload servicerThe two sets are considered to participate in matching, the unloading process of the computing task can be considered as bilateral matching of one user task matching one service user, and the final matching result is the optimization decision of multi-user computing unloading; for bilateral matching, first, a bilateral user preference sequence is established according to different user preferences, and according to an optimization target, a request user and an optimization target are generatedThe service user establishes a preference sequence by taking the size of the total overhead as a sequencing basis: the smaller the total cost, the higher the user's preference and the higher the ranking.
Further, in the step 5), after the preference sequence is established, a stable matching algorithm is used for performing bilateral matching, the requesting user initiates a matching request to the highest-preference service user ranked first according to the preference sequence, the corresponding service user makes an acceptance or rejection selection according to the preference sequence of the corresponding service user, the algorithm is performed iteratively, the request user who is not matched continues to make a new matching/task offloading request to the service user with the highest preference in the current preference sequence until the matching with the service user is successful or no service user can initiate a request in the preference sequence is available, the algorithm is terminated when no new matching request is available, and a final matching result θ is output, that is, a decision for calculating offloading resource optimization by the multi-user D2D.
Due to the adoption of the technical scheme, the invention at least has the following advantages:
the resource optimization decision of multi-user computing unloading based on D2D communication is realized by using a stable matching algorithm with lower computing complexity, and the total computing unloading cost of the whole system including time and energy consumption is minimized. According to the method, the D2D is merged into the MEC network, idle computing resources of adjacent users are utilized, computing task unloading is performed by requesting the adjacent mobile users to unload the D2D, and compared with the method of unloading all computing tasks to a remote MEC server, the method reduces the time delay and energy consumption of computing unloading and can avoid computing unloading congestion caused by shortage of computing resources of the MEC server. Compared with one-to-many, many-to-one or many-to-many D2D unloading, the method has the advantages that tasks of requesting users do not need to be divided by executing complex algorithms, system processing complexity is low, and the situations that unloading delay and energy consumption are increased due to the fact that a plurality of tasks are queued at one service user to overload the service user can be avoided. Further, the method considers the one-to-one D2D calculation unloading process as bilateral matching of a user task matching with a service user, carries out optimized resource allocation decision by using stable matching, and can prove that the resource optimization decision obtained by the method is stable, weak pareto optimal and has lower complexity through theoretical analysis; compared with the traditional random matching method, the method can effectively reduce the calculation overhead of the system, and can obtain the performance which is very close to that of the optimal exhaustive search method with lower calculation complexity.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof.
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FIG. 1 is a block flow diagram of the present invention;
FIG. 2 is a schematic diagram of a system model according to an embodiment;
FIG. 3 is a block diagram of a stable matching algorithm based process in an embodiment;
FIG. 4 is a graph of a comparative simulation of the example with two reference methods;
fig. 5 is a simulation diagram comparing the embodiment and MEC unloading method.
Detailed Description
The following description will further explain embodiments of the present invention by referring to the figures and examples.
The present embodiment is based on a D2D-MEC network environment composed of a macro cell Base Station (BS) and a plurality of mobile users, and referring to the system model shown in fig. 2, each mobile user in the model can not only establish a cellular communication link with the BS, but also establish a D2D link with other nearby mobile users in a D2D communication manner. The base station is equipped with an MEC server to provide corresponding computing unloading service for unloading larger computing tasks, and some mobile users can provide computing unloading service for smaller computing tasks or divided computing tasks directly by adjacent mobile users in a manner of D2D unloading. Referring to FIG. 2, in the present embodiment, a networkIn which is dtA mobile user requesting D2D to compute offload and DrThe neighboring mobile users with free computing resources capable of providing D2D computing offload service use the set D respectivelyt＝{1,2,...,i,...,dtAnd set Dr＝{1,2,...,j,...,drDenotes wherein d istEach mobile user has an independent and delay sensitive computing task that requires a computing offload service to be requested from neighboring mobile users. In order to avoid complex signaling interaction and consider that the task amount is small, the user i belongs to D in the embodimenttThe computing task can be only unloaded to one adjacent user j E DrUser j e D providing computing servicerIt will only allocate its computing resources to a user i e D requesting offloadingtProviding services to it. Without loss of generality, referring to most research scenarios of mobile edge computing and mobile networks, in order to analyze feasibility and obtain practical results, the embodiment also adopts a semi-static computing offloading scenario, that is, within a typical computing offloading time period (in the order of hundreds of milliseconds), a set D of mobile userstAnd DrRemain unchanged and may change over two or more cycles.
D2D unloading among users needs to utilize a D2D link to transmit calculation tasks and calculation results, and D2D communication accesses a wireless channel by adopting an orthogonal frequency division multiple access mode under the control of a cellular network. Based on free space propagation path loss and Rayleigh fading, a user i belonging to D requesting to calculate unloadingtAnd a user j ∈ D providing computation offload servicerReceived power P of inter-D2D linkjCan be expressed as:
wherein, PiRepresents the transmission power of user i; h isijRepresents the channel gain between user i and user j; dijRepresents the distance between user i and user j; beta is a path fading index, and the value of beta is between 2 and 4 according to different communication environments and channel quality; h is0Is subject to a complex Gaussian distributionRayleigh channel coefficient of CN (0, 1).
Further, the achievable data rate r of the D2D link between the user i and the user j is obtainedijExpressed as:
wherein W represents a bandwidth; n is a radical of0Representing additive white gaussian noise of the channel.
In this embodiment, a binary V is usedi＝{Bi,CiRepresents the user i e DtComputing tasks requiring offloading, wherein BiA unit of kilobytes (Kb) indicating the size of the task data amount; ciIndicating the number of CPU cycles, in Megacycles, required to execute the task. A user's computing task is offloaded to an adjacent user for computation, typically through three stages. The first is the task unloading/transmission phase, i.e. the user i belongs to DtThe computing task needs to be firstly transmitted to a neighboring user j e D through D2D communicationr(ii) a The next is the task execution phase, i.e. user j ∈ DrUser i e D for whom execution is scheduledtThe computing task of (2); finally, result downloading stage, i.e. user j ∈ DrReturning the result of the completion of the calculation to the user i belongs to Dt. In the three stages of D2D offloading, the computational overhead resulting from the offloading of the computational tasks each includes a time overhead and an energy overhead. However, since the size of the data amount of the final calculation result is usually very small compared to the input data amount, the download time and energy consumption of the calculation result are negligible in the present embodiment, and the calculation overhead of the task transmission and task execution stage is mainly analyzed.
Calculating an overhead model in a task transmission stage:
user i will calculate input data B of taskiTo user j via D2D communication, input data B is transmitted based on the data rate achievable by the D2D link between user i and user jiConsumed transmission time
Can be calculated from the following formula:
in addition to the transmission time overhead, since users are usually energy limited, the energy consumed by users needs to be considered in order to extend the working time as much as possible to improve the user experience. User i with power PiWill input data BiThe energy consumed for transmission to user j via D2D communication depends on the time of data transmission, so input data B is transmittediConsumed transmission energy
Can be calculated from the following formula:
integrating equations (3) and (4), task transmission stage, user i calculates task ViComputational overhead including time overhead and energy overhead
Comprises the following steps:
wherein the content of the first and second substances,
representing the weights of the time overhead and the energy overhead, respectively. Because different users may have different specific requirements on time and energy expenditure, the weight factors are increased to represent different weight selections of different users, so that the flexible and practical use of the model is realized. This weight parameter may be dynamically changed depending on the type of program the user is executing or the user's requirements at different optimization cycles. To be made toModeling is performed, and the weight is not changed in an optimization period in the present embodiment.
The task execution stage calculates a cost model:
when the computing task of user i is successfully transmitted to user j, user j replaces user i to execute the related computing task, the computing power owned by different users may be different, and the computing power of user j is set, that is, the computing frequency of the CPU is fjThen user j computes the received computation task ViConsumed computing time
Can be calculated from the following formula:
user j calculates the received calculation task ViWill consume self energy, is provided with
Is the power that user j needs to consume to perform a computational task, where κj≧ 0 is the effective switched capacitance, set to κ in the present embodimentj＝10-11，νj≧ 1 is a normal number. Then user j computes the received computing task ViConsumed computing energy
Can be calculated from the following formula:
integrating equation (6) and equation (7), in task execution phase, user i calculates task ViComputational overhead including time overhead and energy overhead
Comprises the following steps:
in this embodiment, computing task offloading is performed between users in a D2D manner, so as to minimize the time delay in the whole task offloading process and minimize the energy consumption of the users, and improve the user experience, so as to minimize the computing overhead in the task offloading process of all the users in the whole system, that is, the computing overhead includes the total time overhead and energy overhead in the task transmission and task execution stages as an objective function, and establish the following linear global optimization problem:
where, the binary variable x of constraint 1) ═ xijIndicating the pairing situation between the user requesting task offloading and the user providing offloading service, when xijWhen the value is 1, the calculation task of the user i is unloaded to the user j for execution, and the calculation resource of the user j is distributed to the user i, xijWhen the value is 0, the result is not true; constraint 2) ensures that a user requesting task offloading can only offload tasks to at most one user providing offloading services; constraint 3) ensures that a user providing an offload service can only allocate computing resources to at most one user requesting task offload. Since the value of the variable can only be a binary variable of 0 or 1, the established optimization problem is a special binary linear programming problem in the integer programming problem and is a non-deterministic polynomial problem. There are many possibilities for a solution that satisfies the problem conditions 1) -3), but there is only one optimal solution. Although the optimal solution can be found by exhaustively exhausting all possible solutions, the computational complexity is very high. Therefore, in this embodiment, a less complex, sub-optimal stable matching algorithm is used to solve the problem, which can achieve near-optimal performance.
Computing offload request user set DtCan only select a set D of users for the computation offload servicerA user in (1), a meterComputation offload service user set DrCan only select to calculate the unloading request user set DtThe two user sets have exactly one-to-one relationship, which is very suitable for the structure of the matching theory. Matching theory is an effective way to model one-to-one, many-to-one, or many-to-many assignments between two independent sets of subjects. Set of users D who will offload requeststAnd a set of users D of the computing offload servicerThe two sets are regarded as two sets participating in matching, the unloading process of the computing task can be regarded as bilateral matching of one user task matching one service user, and the final matching result is the optimization decision of multi-user computing unloading.
For bilateral matching, a bilateral user preference sequence is established according to different preferences of users. When requesting user i e to DtWhen the pairing is performed with different service users, different pairs may obtain different total calculation costs due to different communication distances between the service users and different calculation capabilities of different service users. Similarly, when the service user j belongs to DrWhen pairing with different requesting users, different pairings may also result in different computational overheads. The optimization goal of this embodiment is to minimize the system computation overhead, and therefore, the requesting user i ∈ DtFor service user j E DrAnd service user j ∈ DrFor requesting user i e DtIn the sum of the formulae (5) and (8)
Is measured as a utility function. That is, the requesting user and the service user establish the preference sequence by taking the size of the calculated total cost as the sequencing basis: the smaller the total cost, the higher the user's preference and the higher the ranking.
Is calculated for each service user j e to DrTotal overhead of
Then sorting the request users i belonging to D according to the ascending order of the numerical value of the total overheadtJ e to D for different service usersrThe preference sequence of (1). For the same reason, j ∈ DrIs computed with each requesting user i e DtTotal overhead of
Then sorting the service users according to the ascending order of the numerical value of the total expense from small to large, and establishing the service user j belonging to the DrFor different requesting users i e to DtThe preference sequence of (1).
After the preference sequence is established, the requesting user initiates a matching request to the highest preference service user ranked first according to the preference sequence, and the corresponding service user makes a selection according to the preference sequence. In this embodiment, resource optimization of multi-user computing offloading is implemented by using stable matching, and a flow of a specific decision method is shown in fig. 3. Firstly, initializing each parameter of system and user, and establishing all unmatched user set lists requesting calculation unloading
Each requesting user i e DtAnd each service user j e DrTo be provided with
And calculating preference values of each individual in the other side set for the utility function and arranging the preference values in ascending order to obtain respective preference sequences. Unmatched lists
And under the condition that the preference sequence of a certain requesting user is not empty, initiating a task unloading/matching request to the service user with the highest preference according to the preference sequence of the certain requesting user. The service user receiving the task unloading request compares the request user initiating the request with the current matching object according to the preference sequence, if the service user prefers the request user initiating the request, the request user accepting the request rejects the current matching objectThe requesting user initiating the request is removed from the unmatched list and the rejected matched object is added to the unmatched list; otherwise, if the service user prefers the current matching object, the current matching is maintained, the request user initiating the request is rejected, and the rejected request user initiating the request removes the service user from the preference sequence of the service user and updates the preference sequence of the service user. The algorithm is iterated, and the unmatched requesting users continue to make new task unloading/matching requests to the service users with the highest preference in the current preference sequence until the matching with the service users is successful or no service users capable of initiating the requests exist in the preference sequence. And when no new matching request exists, the algorithm is terminated, and a final matching result theta is output, namely the multi-user D2D calculates the decision of unloading resource optimization.
In the embodiment, when the preference sequence is established, each user in the bilateral set needs to calculate a preference value (calculation overhead) for each user in the other bilateral set, so that the calculation complexity of the preference sequence is established to be O (d)tdr). The computational complexity of the ascending sort preference value to obtain the preference sequence is O (d)tdrlog(dtdr)). In the process of matching, each requesting user can make d direction at mostrEach service user initiates a match request, then dtA requesting user initiates d at mostt×drSecondary request with computational complexity of O (d)tdr). If an exhaustive search method is used to solve the established optimization problem, all possible pairs need to be enumerated, so that the total number of matching results (the number of exhaustive times) which can appear is dt！×dr| A The computational complexity is O (d)t！×dr| A ) (ii) a If the established optimization problem is solved by adopting a special branch-and-bound method such as the enumeration method, compared with an exhaustive search method, the method can reduce the enumeration times through branch-and-bound, and the calculation complexity is
(ii) a If the random matching method is adopted to solve the established optimization problem, the algorithm complexity is linearly increased along with the increase of the number of the service users, and the calculation is carried outComplexity O (d)r). As can be seen from the analysis and comparison of the complexity of the above different methods, the computational complexity of the embodiment using the stable matching algorithm is slightly higher than that of the random matching method, but is significantly lower than that of the exhaustive search method and the implicit enumeration method, and the advantage is more obvious when the number of participating individuals is more.
When the Matlab simulation experiment is performed on the method of this embodiment, an area with a radius of 100m, d, is settA requesting user with independent computing task and drThe service users with idle computing resources are evenly distributed within the circle. With orthogonal channel resources, according to the matched decision result, the task of a requesting user can be unloaded to the corresponding service user in a D2D communication mode for execution. Setting the bandwidth W to 0.5MHz, the maximum transmission distance of D2D to 200m, and the user equipment transmitting power Pi23dBm, noise power N0-174dBm/Hz, path fading index β -3, task ViInput data volume Bi10-100 Kb, task ViRequired number of CPU cycles Ci1-10 Megacycles, user CPU computing power f j1 to 3.1GHz, weight
Constant v j1. Under the setting, the comparison result of the calculation total cost of the method, the random matching method and the exhaustive search method with the change of the number of the users requesting to unload the service according to the embodiment is shown in fig. 4, and the number d of the set service users at this time r4. It can be seen that the calculated total cost obtained by the method of the present embodiment is very close to the optimal exhaustive search method, and is much lower than that obtained by the random matching method. Although the exhaustive search can obtain the optimal performance, the calculation complexity is very high, and the method described in the embodiment can obtain the performance similar to the optimal performance with lower complexity. Similarly, the comparison result of the overhead of the D2D offload method and the MEC offload method according to this embodiment varying with the number of requested offload users is shown in fig. 5, where the number D of service users is set at this timerIs equal to the number d of the requested userst. The MEC unloading refers to unloading all tasks of a requesting user to an MEC server which is far away from 200-700 mThe MEC server CPU calculates the frequency to be 5 GHz. Because the MEC server usually has ac power supply conditions, the energy consumption overhead when the MEC offloads the execution of the computing task is not considered in the embodiment, and other overhead is obtained by the same way as the D2D offloads the computation. It can be seen that the total overhead, the total delay overhead and the total energy consumption overhead of the D2D offloading method in this embodiment are all lower than those of the MEC offloading method, and particularly, the gap increases as the number of users requesting offloading increases.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.
Claims (6)
1. D2D communication-based multi-user computing unloading resource optimization decision method, wherein the multi-user communication system comprises a DtA mobile user requiring a request for D2D to compute offload, using set Dt＝{1,2,...,i,...,dtDenotes with drA neighboring mobile user with free computing resources capable of providing D2D computing offload services, using set Dr＝{1,2,...,j,...,drDenotes d istEach mobile user has an independent and time-delay sensitive computing task which needs to request computing offloading service from neighboring mobile users, and the computing offloading method of D2D is characterized by comprising the following specific steps:
1) establishing a data communication model based on D2D communication to obtain the data rate which can be realized by D2D communication;
2) respectively establishing a calculation overhead model comprising time and energy overhead in a task transmission stage and a task execution stage;
3) establishing a global optimization problem for minimizing the total calculation overhead required by the task unloading of all users of the whole system based on a time and energy overhead model;
4) establishing a preference sequence of the bilateral user by taking the size of the calculated total overhead as a sequencing basis;
5) and based on the established preference sequence, obtaining resource optimization decisions of calculation and unloading of the multi-user D2D by using a stable matching algorithm.
2. The multi-user computing offload resource optimization decision method based on D2D communication of claim 1, wherein: in the step 1), computation and unloading among users need to transmit computation tasks by using D2D communication, and D2D communication is controlled by a cellular network to access a wireless channel by adopting an orthogonal frequency division multiple access mode; obtaining the user i belongs to D based on free space propagation path loss and Rayleigh fadingtAnd user j ∈ DrData rate r achievable with D2D communication betweenij。
3. The multi-user computing offload resource optimization decision method based on D2D communication of claim 1, wherein: in the step 2), the calculation overhead generated in the task transmission and task execution stages of calculation and unloading among users comprises time overhead and energy overhead; in the task transmission stage, the total calculation cost of the user i calculation task including time cost and energy cost is
Wherein the content of the first and second substances,
is input data B of the task to be calculated by the user iiThe transmission time consumed for transmission to user j via D2D communication,
is the power P of user iiWill input data BiThe energy consumed by the communication transmitted to user j through D2D,
weights representing time overhead and energy overhead, respectively; in the task execution phase, the user i calculates the total calculation of tasks including time cost and energy costOverhead is
Wherein the content of the first and second substances,
is the computation time consumed by user j to compute the received computation task,
is the computational energy consumed by user j to compute the received computational task.
4. The multi-user computing offload resource optimization decision method based on D2D communication of claim 1, wherein: in the step 3), in order to minimize the time delay in the whole task unloading process and minimize the energy consumption of the user, the user experience is improved, so that the total calculation overhead in the task unloading process of all the users in the whole system is minimized, that is, the total time overhead and the energy overhead in the task transmission and task execution stages are taken as objective functions, and a linear global optimization problem is established.
5. The multi-user computing offload resource optimization decision method based on D2D communication of claim 1, wherein: in the step 4), in order to solve the established optimization problem with low complexity, the user set D of the unloading request istAnd a set of users D of the computing offload servicerThe two sets are considered to participate in matching, the unloading process of the computing task can be considered as bilateral matching of one user task matching one service user, and the final matching result is the optimization decision of multi-user computing unloading; the bilateral matching is carried out, firstly, a preference sequence of bilateral users is established according to different preferences of the users, and according to an optimization target, a request user and a service user establish the preference sequence by taking the size of the calculated total cost as a sequencing basis: the smaller the total cost, the higher the user's preference and the higher the ranking.
6. The multi-user computing offload resource optimization decision method based on D2D communication of claim 1, wherein: in the step 5), after the preference sequence is established, a stable matching algorithm is used for performing bilateral matching, the requesting user initiates a matching request to the highest-preference service user ranked first according to the preference sequence, the corresponding service user selects acceptance or rejection according to the preference sequence of the requesting user, the algorithm is iterated, the unmatched requesting user continues to issue a new matching/task unloading request to the highest-preference service user in the current preference sequence until the matching with the service user is successful or no service user can initiate the request in the preference sequence, the algorithm is terminated when no new matching request exists, and a final matching result theta is output, namely, a decision for optimizing the unloading resources is calculated by the multi-user D2D.
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