Energy efficiency optimization method of mobile edge computing system based on integrated network

Abstract

The invention discloses an energy efficiency optimization method of a mobile edge computing system based on an integrated network. The method comprises the following steps: constructing a mobile edge computing system supported by a space-air-ground integrated network; for a mobile edge computing system, a joint optimization problem is constructed with the purpose of maximizing the total energy efficiency of the system, and the joint optimization problem is used for joint optimization of the flight path of an unmanned aerial vehicle, the transmitting power of a mobile user, the task unloading decision of the mobile user and the incidence relation between the mobile user and the unmanned aerial vehicle. Calculating frequency distribution of the mobile user, the unmanned aerial vehicle and the low earth orbit satellite; and decomposing the joint optimization problem into a sub-problem of optimizing an incidence relation between the mobile user and the unmanned aerial vehicle, a sub-problem of optimizing a task unloading decision of the mobile user, a sub-problem of optimizing transmitting power control and calculating frequency allocation and a sub-problem of optimizing an unmanned aerial vehicle track, and solving by alternately optimizing each sub-problem. According to the invention, the total energy efficiency of the system and the service quality of users are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of communication technology, and more particularly, to an energy efficiency optimization method for a mobile edge computing system based on an integrated network. BACKGROUND

[0002] Space-air-ground integrated network (SAGIN) as a typical non-terrestrial network mainly includes three components: space network, air network and ground network. The space network includes low earth orbit satellites. The air network covers aircraft, unmanned aerial vehicles and high-altitude platforms. The ground network is composed of ground base stations, mobile users and Internet of Things devices. Non-terrestrial networks supported by satellites can provide extensive connection services across regions and time for mobile users. However, due to the long distance between satellites and the ground, there are challenges such as high transmission delay, low transmission rate and increased energy consumption for long-distance transmission. In contrast, ground communication performs well in urban areas with high density of mobile users, and can provide high-speed and low-latency services. However, due to its limited coverage, it is difficult to meet the communication needs of mobile users in remote areas. To address this problem, unmanned aerial vehicles can be used as air communication nodes to provide communication and computing services. With its flexible mobility, unmanned aerial vehicles can provide more flexible coverage and more stable communication quality for mobile users. Through the multi-layer connection architecture of SAGIN, mobile users can obtain diversified services from different service providers such as low earth orbit satellites and unmanned aerial vehicles, thereby improving the overall service quality of all users.

[0003] In addition, the widespread use of computing-intensive and latency-sensitive tasks and devices such as multimedia applications, social networks, and emerging smart devices and smart home controllers has had a significant impact on mobile users. The increase in usage has further increased the demand for efficient communication connections and high-speed data transmission, highlighting the importance of enhancing the processing capacity of mobile users to effectively handle the growing task load. Therefore, optimizing task processing performance has become a key issue that needs to be addressed, which requires efficient and reliable communication and computing resources. However, the traditional cloud-centric computing model is difficult to provide high-quality user experience for mobile users in remote areas. To solve this problem, mobile edge computing (MEC) is proposed to efficiently utilize the computing resources at the edge of the network. MEC sinks computing and storage resources to the edge of the mobile network, making it closer to mobile users on the ground. Through localized data processing and real-time analysis, it can effectively reduce the energy consumption and latency of task processing, and enhance network adaptability.

[0004] Currently, some pioneering research has begun to explore the deployment of SAGIN in the next generation of communication networks. For example, the literature (E. M. Mohamed, M. Ahmed Alnakhli, and M. M. Fouda, “Joint UAV trajectory planning and LEO-sat selection in SAGIN,” IEEE Open J. Commun. Soc., vol. 5, pp. 1624–1638, 2024.) considers the joint UAV trajectory planning and LEO-sat selection problem in post-disaster SAGIN scenarios, taking into account the battery power of UAVs and the dynamic nature of low Earth orbit satellites. It provides an effective solution for rescue communication in post-disaster SAGIN scenarios, improving the energy efficiency and data transmission performance of UAVs, and has important significance for ensuring post-disaster communication. However, the growing computing task demand of mobile users due to emerging applications cannot be met.

[0005] The literature (B. Liang, R. Fan, H. Hu, H. Jiang, J. Xu, and N. Zhang, “Joint task offloading and resource allocation in multi-user mobile edge computing with continuous spectrum sharing,” IEEE Trans. Veh. Technol., vol. 73, no. 5, pp. 7234–7249, May 2024.) addresses the task offloading and resource allocation problem in multi-user MEC systems, considering multi-channel spectrum sharing, and aims to minimize the weighted sum of mobile user energy consumption. It provides an effective resource allocation method for multi-user partial offloading MEC systems, which can help achieve system parameter configuration, reduce mobile user energy consumption, and improve system performance. However, this solution mainly focuses on ground networks, and due to the lack of effective network access, mobile users in remote areas still cannot utilize cloud computing and edge computing resources to process computing tasks.

[0006] After analysis, currently, the multi-UAV enabled mobile edge computing system based on space-air-ground integrated network still faces the following challenges:

[0007] 1) Existing systems do not jointly optimize the multi-path access of mobile users through UAVs or low Earth orbit satellites, and the access strategy is static and single, which cannot dynamically select the optimal link according to the channel state, resulting in insufficient utilization of spectrum resources and difficulty in improving the overall spectrum efficiency of the system.

[0008] 2) The unloading and scheduling of tasks between the terminal local, UAV edge nodes and low Earth orbit satellites lacks unified coordination and cross-level resource joint optimization has not been achieved, resulting in the coexistence of idle computing power on the device side and overload on the edge nodes, and the overall computing power of the system has not been efficiently utilized.

[0009] 3) Existing research has not used energy efficiency as a core indicator for UAV flight path planning, and trajectory design has not been optimized in conjunction with communication and computing tasks, resulting in excessive energy consumption during UAV flight, which affects the system's endurance and operational efficiency.

[0010] 4) The association between users and drones is mostly based on distance or fixed configuration, without dynamic adjustment based on real-time channel status, load conditions and task requirements, resulting in fluctuations in communication quality, imbalance in resource allocation, and difficulty in achieving accurate and efficient task offloading decisions.

[0011] In summary, there are still significant shortcomings in deploying SAGIN or MEC systems in next-generation communication networks, or in introducing SAGIN into MEC systems. This provides an important direction for technological breakthroughs and innovation space for mobile edge computing systems empowered by multiple UAVs based on integrated air-space-ground networks. Summary of the Invention

[0012] The purpose of this invention is to overcome the shortcomings of the prior art and provide an energy efficiency optimization method for a mobile edge computing system based on an integrated network. This method includes the following steps:

[0013] Construct a mobile edge computing system supported by an integrated air-space-ground network, including multiple mobile users, multiple drones, and a low Earth orbit satellite;

[0014] For the mobile edge computing system, a joint optimization problem is constructed with the goal of maximizing the overall energy efficiency of the system. This joint optimization problem is used to jointly optimize the flight trajectory of the UAV, the transmit power of the mobile user, the task offloading decision of the mobile user, the correlation between the mobile user and the UAV, and the computing frequency allocation of the mobile user, the UAV and the low Earth orbit satellite.

[0015] The joint optimization problem is decomposed into subproblems: optimizing the relationship between mobile users and UAVs, optimizing the task offloading decision of mobile users, optimizing the transmit power control and calculation frequency allocation, and optimizing the UAV trajectory. The subproblems are solved by alternately optimizing each subproblem.

[0016] Compared with the prior art, the advantage of the present application is that a new type of mobile edge computing system equipped with multiple unmanned aerial vehicles supported by space-air-ground integrated network is proposed, which realizes wide area coverage and enhances the communication ability of hot spot areas. Under the premise of meeting the maximum delay constraint of mobile user tasks and the maximum energy consumption constraint of unmanned aerial vehicles, the total energy efficiency of the system is maximized by jointly optimizing the association of mobile users and unmanned aerial vehicles, unmanned aerial vehicle trajectories, task offloading strategies, transmit power control and computing frequency allocation.

[0017] Other features of the present application, and its particular advantages, will become apparent to those skilled in the art from the following detailed description, together with the drawings. BRIEF DESCRIPTION OF DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the application and, together with the description, serve to explain the principles of the application.

[0019] Figure 1 is a flow chart of an energy efficiency optimization method of an integrated network-based mobile edge computing system according to an embodiment of the present application;

[0020] Figure 2 is a schematic diagram of an integrated network-based mobile edge computing system according to an embodiment of the present application;

[0021] Figure 3 is a schematic diagram of the convergence performance comparison of the present application and the prior art. DETAILED DESCRIPTION

[0022] Various exemplary embodiments of the present application will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of the components and steps set forth in these embodiments, numerical expressions, and numerical values are not limiting to the scope of the present application unless otherwise specifically stated.

[0023] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way limiting to the scope of the application or its applications or uses.

[0024] Techniques, methods, and devices known to those of ordinary skill in the relevant art can not be discussed in detail herein, but should be considered as part of the specification, where appropriate.

[0025] In all of the examples shown and discussed herein, any specific values should be interpreted as merely illustrative and not as a limitation. Thus, other examples of exemplary embodiments can have different values.

[0026] It should be noted that like reference numerals and characters refer to like elements throughout the following figures and the detailed description, not upon repeated utilization of the detailed description to define them.

[0027] Overall, the present application studies a space-air-ground integrated network supported mobile edge computing system, in which low earth orbit satellites and multiple unmanned aerial vehicles cooperatively provide computing services for mobile users. For this mobile edge computing system, an optimization problem is constructed to maximize the system energy efficiency, which jointly optimizes the association of mobile users and unmanned aerial vehicles, the flight trajectory of unmanned aerial vehicles, the task offloading decision, the computing resource allocation, and the transmission power control, etc. Further, considering that this optimization problem is non-convex, it is decomposed into four sub-problems, and an algorithm based on alternating optimization is proposed to solve it.

[0028] Specifically, referring to Figure 1 The provided energy efficiency optimization method of the integrated network based mobile edge computing system includes the following steps:

[0029] Step S110, for the space-air-ground integrated network supported mobile edge computing system, an optimization problem is constructed to maximize the system energy efficiency.

[0030] In combination with Figure 2 As shown in the figure, the space-air-ground integrated network supported mobile edge computing system includes M mobile users, K unmanned aerial vehicles, and a low earth orbit satellite. The set of mobile users is defined as The set of unmanned aerial vehicles is The low earth orbit satellite and each unmanned aerial vehicle are equipped with an on-board computing server, which can provide computing services for mobile users. At the same time, each mobile user has a computing task that can be decomposed into several independent and fine-grained sub-tasks. Due to the computing delay requirement of the task itself and the limited computing ability of the mobile user, part of these tasks need to be offloaded to unmanned aerial vehicles and low earth orbit satellites for remote computing.

[0031] Consider a task execution time period T with N time slots, and define the set of time slots as Each time slot has an equal duration τ. At the beginning of each time slot, each mobile user will generate a task. The system adopts a hybrid time division multiplexing and frequency division multiplexing multiple access mode. Since the time slot duration τ is extremely short, it can be considered that the position of the unmanned aerial vehicle remains unchanged within each time slot, but changes between different time slots. It is assumed that the unmanned aerial vehicle flies at a fixed flight altitude H to ensure its smooth flight and avoid obstacles and buildings. Therefore, the position of the unmanned aerial vehicle k in the nth time slot can be represented as where q k [n]=(x k[n], y k [n] denotes its horizontal coordinate. In addition, the position of the mobile user m at the nth time slot is denoted as where s m [n] = (x m [n], y m [n]).

[0032] The initial position and the final position of each UAV are defined as To ensure the flight stability of all UAVs, the constraints of avoiding collision between UAVs and the constraints of UAV speed must be satisfied, which are given by the following equations, respectively:

[0033]

[0034] where d min denotes the minimum safe distance between any two UAVs, V max denotes the maximum flight speed of the UAV. In addition, the flight energy consumption of the UAV k can be expressed as:

[0035]

[0036] where ζ1 and ζ2 are fixed parameters related to the weight of the UAV, the wing area, the air density, etc.

[0037] It is assumed that each mobile user is equipped with two communication interfaces, one of which is used for communication with the low earth orbit satellite, and the other is used for communication with the associated UAV. The two interfaces work on different spectrum bands, so as to ensure that there is no mutual interference between the communication with the UAV and the communication with the low earth orbit satellite.

[0038] Since the UAVs have high flexibility, they can fly to the position close to the mobile user to establish the line-of-sight link. The channel gain between the mobile user m and the UAV k can be expressed as:

[0039]

[0040] where β0 denotes the channel gain at the reference distance d0 = 1 m. Therefore, the transmission rate of the mobile user m to the UAV k can be expressed as:

[0041]

[0042] where B m,k denotes the bandwidth allocated for data transmission between the mobile user m and the UAV k, p m,k [n] denotes the transmission power when the mobile user m communicates with the UAV k, denotes the noise power on the UAV side.

[0043] In addition, part of the tasks of mobile users can be offloaded to LEO satellites for computation. Accordingly, the channel condition between a mobile user and a LEO satellite is mainly affected by the distance and the weather environment. Assuming that the weather environment remains unchanged during the task processing, the channel gain between each mobile user and a LEO satellite is mainly determined by the orbital height of the satellite. Therefore, the transmission rate between a mobile user m and a LEO satellite can be represented as:

[0044]

[0045] where B LEO denotes the bandwidth allocated for data transmission between each mobile user and a LEO satellite, p m,LEO denotes the transmission power when a mobile user m communicates with a LEO satellite, g m,LEO denotes the channel gain between a mobile user m and a LEO satellite, denotes the noise power on the LEO satellite side.

[0046] At the beginning of each time slot, each mobile user arrives with a task, denoted as {D m [n],φ m [n]}, where D m [n] denotes the data volume of the task, and φ m [n] denotes the number of computation cycles required to process each bit of data. Let denote the task offloading decision set of a mobile user m, where denote the proportion of tasks processed locally, offloaded to the associated UAV, and offloaded to a LEO satellite, respectively. The offloading decision satisfies:

[0047] Local processing: Let denote the number of computation cycles per second (i.e., the computing capacity) of a mobile user m in the nth time slot. Then, the time delay required by a mobile user m to process D bits of tasks locally is:

[0048]

[0049] Accordingly, the energy consumed by local processing is:

[0050]

[0051] where κ L denotes the energy efficiency factor of local computation of all mobile users.

[0052] UAV processing: When a mobile user m offloads part of the tasks The processing latency includes three parts when offloading to its associated UAV: the transmission latency of task data from mobile users to the associated UAV, the computation latency of the task on the associated UAV, and the backhaul latency of the computation result from the UAV back to the mobile users. Since the data volume of the computation result is much smaller than that of the offloaded task, the backhaul latency can be ignored.

[0053] Each mobile user is associated with a specific UAV in each time slot, and its partial task can be offloaded to the associated UAV for computation. A binary indicator variable m,k [n] is introduced to represent the association, where m,k [n] = 1 means that the mobile user m is associated with the UAV k in the nth time slot, otherwise m,k [n] = 0. Let denote the number of computation cycles per second that the UAV k allocates to the mobile user m in the nth time slot. The UAV k completes The latency of a bit task includes two parts: the task offloading (i.e., uplink transmission) latency and the task processing (i.e., computation) latency. Therefore, the latency can be represented as:

[0054]

[0055] Therefore, the energy consumed by the mobile user m to offload a task to its associated UAV, and the energy consumed by the UAV k to process the task, can be represented as:

[0056]

[0057]

[0058] where U denotes the energy efficiency factor of the UAV task processing.

[0059] Low Earth Orbit Satellite Processing: Similar to the task processed by the UAV, the latency of a task processed by the Low Earth Orbit Satellite also includes two parts: the transmission latency of the task from the mobile user to the Low Earth Orbit Satellite, and the computation latency of the task on the Low Earth Orbit Satellite. Let denote the number of computation cycles per second that the Low Earth Orbit Satellite allocates to the mobile user m in the nth time slot. The Low Earth Orbit Satellite completes The latency of a bit task can be represented as:

[0060]

[0061] Correspondingly, the energy consumed by the mobile user m to offload a task to the Low Earth Orbit Satellite, and the energy consumed by the Low Earth Orbit Satellite to process the task, can be represented as:

[0062]

[0063] where κ S denotes the energy efficiency factor for LEO satellites to perform task computation.

[0064] In summary, the total latency and total energy consumption of mobile user m can be expressed as:

[0065]

[0066] The objective of this work is to maximize the total energy efficiency of the system by jointly optimizing the flight trajectory of the UAVs the transmit power of all mobile users the task offloading decisions of all mobile users the association between mobile users and UAVs and the computation frequency allocation of mobile users, UAVs and LEO satellites This optimization problem can be formulated as:

[0067]

[0068] subject to the following constraints:

[0069]

[0070]

[0071] where and denote the maximum computation frequency of mobile users, UAVs and LEO satellites, respectively. and denote the maximum transmit power of mobile users when communicating with the associated UAVs and with LEO satellites, respectively. denotes the energy budget of UAVs. Constraints (17b)-(17d) represent the mobility constraints of UAVs. Constraints (17e)-(17f) represent the association constraints between mobile users and UAVs. Constraints (17g)-(17h) represent the constraints on task offloading decisions. Constraints (17i)-(17k) represent the computation frequency constraints of local task processing, UAV task processing and LEO task processing, respectively. Constraints (17l)-(17m) represent the transmit power allocation constraints when mobile users offload tasks to UAVs and to LEO satellites, respectively. Constraint (17n) represents the latency constraint of task processing. Finally, constraint (17o) represents the constraint on the energy consumption of UAVs themselves.

[0072] Step S120, decompose the joint optimization problem into a mobile user and UAV association optimization sub-problem, a UAV trajectory optimization sub-problem, a task offloading decision optimization sub-problem, and a transmit power control and computing frequency allocation optimization sub-problem, and solve them by an alternating optimization method.

[0073] In problem (P1), the objective function has a non-convex fractional form and involves multiple coupled variables and binary variables. Therefore, (P1) is a challenging mixed-integer nonlinear programming problem, which is difficult to be directly solved by traditional convex optimization methods. To effectively cope with this challenge, in one embodiment, a quadratic transformation method is used to transform the fractional structure in the objective function to decouple the variables in the numerator and the denominator. Subsequently, problem (P1) is decomposed into four sub-problems: mobile user-UAV association optimization (i.e., mobile user and UAV association optimization), UAV trajectory optimization, task offloading decision optimization, and transmit power control and CPU frequency allocation optimization. These four sub-problems will be solved by an alternating optimization method:

[0074] 1) Problem reformulation based on quadratic transformation

[0075] First, the quadratic transformation method is used to reformulate the objective function.

[0076] Given Theorem 1: Given an M x N pair of non-negative functions and a positive function The ratio and problem:

[0077]

[0078] Subject to the following constraints:

[0079]

[0080] is equivalent to:

[0081]

[0082] Subject to the following constraints:

[0083]

[0084] where x is the optimization variable, denotes the feasible solution set, y denotes the quadratic transformation coefficient matrix of size M x N, and y m,n denotes the element in the mth row and nth column of matrix y.

[0085] According to Theorem 1, problem (P1) can be transformed into problem (P2), which is specifically expressed as follows:

[0086]

[0087] Subject to the following constraints:

[0088] (17b)-(17o),(20b)

[0089] where, denotes a matrix of size M x N. To achieve the optimal energy efficiency, y is updated alternatively as follows:

[0090]

[0091] and after updating y at each iteration, solve for the variable X in problem (P2). Since each element in matrix y is non-decreasing after each iteration, the convergence of the algorithm is guaranteed.

[0092] Each element of matrix y is initialized to a small constant. Then, the feasible solution set of variable X is searched. Next, the values of elements in matrix y are updated based on the obtained feasible solution X. By repeating this process for several iterations, the optimal values of y * and X * can be determined eventually.

[0093] 2) Subproblem 1: Mobile user-UAV association optimization

[0094] Subject to the following constraints:

[0095]

[0096] Subject to the following constraints:

[0097] (17e)-(17f),(17n)-(17o),(22b)

[0098] where,

[0099]

[0100] Problem (SP1) is a standard integer linear programming problem, which can be solved by traditional optimization methods, such as branch-and-bound algorithm.

[0101] 3) Subproblem 2: Task offloading decision optimization

[0102] Subject to the following constraints:

[0103]

[0104] Subject to the following constraints:

[0105] (17g)-(17h),(17n)-(17o),(24b)

[0106] where:

[0107]

[0108] Problem (SP2) is a standard linear programming problem, which can be solved by interior point method.

[0109] 4) Subproblem 3: Transmit power control and computing frequency allocation optimization

[0110] Under the condition of fixing variables Q, Ω, α, the subproblem of optimizing transmit power control and computing frequency allocation can be expressed as:

[0111]

[0112] Subject to the following constraints:

[0113] (17i)-(17o),(26b)

[0114] where and is given by:

[0115]

[0116]

[0117] It is not difficult to verify that Regarding the variable is a convex function. In addition, the function Regarding the variable p m,k [n] and p m,LEO [n] is a concave function.

[0118] By introducing a set of auxiliary variables {ξ m,k [n],ξ m,LEO [n]}, can be approximately expressed as:

[0119]

[0120] where, ξ m,k [n] and ξ m,LEO [n] are given by:

[0121]

[0122] and the function Regarding the variable {ξ m,k [n],ξ m,LEO [n]} is a convex function. It is worth noting that, which cannot be directly represented in CVX. Therefore, it can be transformed by exponential cone optimization. Specifically, by introducing a set of auxiliary variables {Γ m,k [n],Γ m,LEO [n]}, can be rewritten as and represented as:

[0123]

[0124] where:

[0125]

[0126] Correspondingly, the constraints (17l)-(17n) can be rewritten as:

[0127]

[0128]

[0129] In summary, problem (SP3) is transformed into problem (SP3'), which is stated as follows:

[0130]

[0131] subject to the following constraints:

[0132] (17i)-(17k),(17o),(33)-(39),(40b)

[0133] where, Problem (SP3') is a convex optimization problem, which can be solved by CVX or other tools.

[0134] 5) Subproblem 4: UAV Trajectory Optimization

[0135] Under the condition of fixing variables P, Ω, F, α, the subproblem of optimizing the UAV trajectory can be stated as:

[0136]

[0137] subject to the following constraints:

[0138] (17b)-(17d),(17n)-(17o).(41b)

[0139] Regarding the inequality constraint (17o), contains the term This expression is the inverse of a convex function, and thus is a concave function, resulting in a non-convex constraint (17o). To handle this problem, we introduce an auxiliary variable ψ k [n], which satisfies:

[0140]

[0141] Thus, can be approximated by its convex upper bound:

[0142]

[0143] By replacing with inequality constraint (17o) can be converted to:

[0144]

[0145] Moreover, constraint (42) is non-convex. It can be transformed to the following convex form by a successive convex approximation method:

[0146]

[0147] Similarly, constraint (17c) can be transformed to:

[0148]

[0149] As for constraint (17n), only in it is related to the UAV trajectory. Therefore, we mainly consider its equivalent constraint By introducing auxiliary variables S m,k [n] and γ m,k [n] = R m,k [n], where S m,k [n] and γ m,k [n] satisfy:

[0150] S m,k [n] ≤ H 2 +‖q k [n] - s m [n]‖ 2 ,(47)

[0151]

[0152] can be transformed to:

[0153]

[0154] It is easy to prove that the expression is a convex function with respect to variable S m,k [n]. Then, similar to the transformation method of equation (45), constraint (49) can be transformed to:

[0155]

[0156] Similarly, the constraint condition S m,k [n]≤H 2 +‖q k [n]-s m [n]‖ 2 can be converted to:

[0157]

[0158] Then, the objective function can be expressed as:

[0159]

[0160] In summary, the problem (SP4) is converted to the problem (SP4') as shown below, which is expressed as:

[0161]

[0162] Subject to the following constraint conditions:

[0163] (17b),(17d,(44)-(46),(49)-(51).(53b)

[0164] wherein, The problem (SP4') is a convex optimization problem, which can be solved by a standard convex optimization solving tool such as CVX.

[0165] To further evaluate the effect of the present application, a simulation experiment is performed. In the simulation experiment, two unmanned aerial vehicles are considered to fly from a given initial position to a specified final position to provide computing services for mobile users, and the simulation scenario includes eight mobile users randomly distributed in a 1000x1000m 2 square area, all unmanned aerial vehicles fly at a height of 100m, the task period is 40s, the number of time slots is 40, the time slot length is 1s, the minimum safety distance is 50m, the maximum flight speed is 50m / s, the unmanned aerial vehicle flight energy consumption parameters are 0.00614 and 15.976, the channel power gain between the unmanned aerial vehicle and the mobile user is-60dBm, the noise power at the unmanned aerial vehicle is-170dBm, the channel power gain between the low earth orbit satellite and the mobile user is [5,10]dBm, the noise power at the low earth orbit satellite is-170dBm, the maximum transmission power is 3W, the task data volume is [5.5,6.5]Mbits, the number of computing periods required for each bit of data is 100 periods / bit, the maximum computing frequency of the mobile user is 5GHz, the mobile user computing capacitance coefficient is 10 -26 , the maximum computing frequency of the unmanned aerial vehicle is 9GHz, and the unmanned aerial vehicle computing capacitance coefficient is 10 -27, the maximum calculated frequency of the low earth orbit satellite is 9GHz, and the calculated capacitance coefficient of the low earth orbit satellite is 10 -27 .

[0166] To evaluate the performance and efficiency of the present application, the present application is compared with five benchmark schemes, including: a single UAV scheme: only one UAV in the considered space-air-ground integrated network system can provide computing services for mobile users; a fixed trajectory scheme: the flight trajectory of the UAV is pre-set and remains unchanged; a fixed allocation scheme: the task offloading decision of the mobile user is fixedly configured and does not dynamically adjust with the environment; a low earth orbit satellite offloading scheme: only a low earth orbit satellite provides computing services for mobile users; and a hybrid low earth orbit satellite and base station offloading scheme: a low earth orbit satellite and a ground base station cooperate to provide computing services for mobile users in a hybrid mode.

[0167] Figure 3 A comparative analysis of the total energy efficiency of different schemes under the change of the number of iterations is given. Figure 3 It is shown that, with the increase of the number of iterations, the total energy efficiency increases significantly in the initial stage and then gradually stabilizes, verifying the convergence of the present application and each scheme. It is worth noting that the total energy efficiency of the present application is the highest among all schemes, which is due to the joint optimization design of the mobile user-UAV association, UAV trajectory, task offloading decision, transmit power control and CPU frequency control.

[0168] In summary, compared with the prior art, the present application mainly has the following advantages:

[0169] 1) The present application proposes a new type of mobile edge computing system supported by a space-air-ground integrated network and equipped with multiple UAVs, which realizes wide-area coverage and enhances the communication capability of hot spot areas. Under the premise of meeting the maximum delay constraint of the mobile user task and the maximum energy consumption constraint of the UAV, the total energy efficiency of the system is maximized by jointly optimizing the mobile user-UAV association, UAV trajectory, task offloading strategy, transmit power control and computing frequency allocation.

[0170] 2) The present application first uses the quadratic transformation method to eliminate the fractional structure in the objective function, and then uses the alternating optimization method to divide the system optimization problem into four parts, which are independently optimized for mobile user-UAV association, task offloading decision, transmit power control and computing frequency allocation, and UAV trajectory.

[0171] 3) The present application innovatively uses Taylor expansion and continuous convex approximation algorithm in the optimization process, which significantly simplifies the complex optimization problem.

[0172] 4) The system design of the present application has high scalability, not only limited to the current space-ground-ground integrated network, but also can be adjusted according to different actual application requirements.

[0173] 5) The present application uses the multi-layer connection architecture of SAGIN, so that mobile users in remote areas can also use cloud computing and edge computing resources to process computing tasks, improving the service quality of all users.

[0174] 6) The present application further considers the energy efficiency problem in the MEC system enabled by multiple UAVs under SAGIN, wherein the low earth orbit satellite provides extensive global coverage, and the UAV provides flexible deployment and dynamic response capability, providing more stable and efficient computing and communication services in application scenarios, while solving various existing limitations, significantly improving the system energy efficiency, and meeting the growing computing task requirements of mobile users due to emerging applications.

[0175] The present application can be a system, a method and / or a computer program product. The computer program product can include a computer readable storage medium having computer readable program instructions embodied therewith to implement various aspects of the present application.

[0176] The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch cards or punched tape, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

[0177] Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0178] Computer readable program instructions for carrying out operations of the present application can be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computing / processing device, partly on the user's computing / processing device, as a stand-alone software package, partly on the user's computing / processing device and partly on a remote computing / processing device or entirely on the remote computing / processing device or server. In the latter scenario, the remote computing / processing device can be connected to the user's computing / processing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computing / processing device, for example, through the Internet using an Internet Service Provider. In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present application.

[0179] The computer readable program instructions can also be loaded onto a computing / processing device, other programmable data processing apparatus, or other device to cause a series of operations to be performed on the computing / processing device, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computing / processing device, other programmable apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0180] These computer readable program instructions can be provided to a processor of a general purpose computer, special purpose computer, 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 / acts specified in the flowchart and / or block diagram block or blocks. These computer readable program instructions can also be stored in a computer readable storage medium that can include a non-transitory computer readable storage medium that can be a computer- readable storage medium having no data, programs, program modules, e.g., instructions for operation, or digital content stored thereon or therein for a short time or not at all. The computer readable storage medium can also have instructions stored thereon or therein which may

[0181] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable data processing apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0182] The flow diagrams and block diagrams in the accompanying drawings show archi tectures, functional and operational architectures of possible implementations of systems, methods, and computer program products according to the present disclosure. In this regard, each block in the flow diagrams and block diagrams can represent a module, a segment, or a portion of instructions, which comprises one or more executable instructions for implementing the specified logical functions (acts). In some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in some cases, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart illustrations, and combinations thereof, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

[0183] Having described various embodiments of the application, it is to be understood that the above description is meant to be illustrative only and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art, without departing from the scope and spirit of the described embodiments. The selection of terms to be used in the description is intended to best explain the principles of the embodiments, the practical application, or technical improvement over the prior art, or to enable others skilled in the art to understand the embodiments disclosed herein. The scope of the application is defined by the claims appended hereto.

Claims

1. An energy efficiency optimization method for a mobile edge computing system based on an integrated network, comprising the following steps: Construct a mobile edge computing system supported by an integrated air-space-ground network, including multiple mobile users, multiple drones, and a low Earth orbit satellite; For the mobile edge computing system, a joint optimization problem is constructed with the goal of maximizing the overall energy efficiency of the system. This joint optimization problem is used to jointly optimize the flight trajectory of the UAV, the transmit power of the mobile user, the task offloading decision of the mobile user, the correlation between the mobile user and the UAV, and the computing frequency allocation of the mobile user, the UAV and the low Earth orbit satellite. The joint optimization problem is decomposed into subproblems: optimizing the relationship between mobile users and UAVs, optimizing the task offloading decision of mobile users, optimizing the transmit power control and calculation frequency allocation, and optimizing the UAV trajectory. The subproblems are solved by alternately optimizing each subproblem.

2. The method according to claim 1, characterized in that, The joint optimization problem is set as follows: The constraints are set as follows: Where Q is the UAV's flight trajectory, P is the transmit power of all mobile users, Ω is the task offloading decision for all mobile users, α is the correlation between mobile users and UAVs, and F is the computational frequency allocation for mobile users, UAVs, and low Earth orbit satellites; the set of mobile users is... The collection of drones is The time slot set is The task execution time period T is N time slots, each time slot has an equal duration τ, M is the number of mobile users, K is the number of drones, and N is the number of time slots; D is the total energy consumption of mobile user m. At the start of each time slot, each mobile user arrives at a task. m [n] represents the amount of data for this task; This indicates the maximum computing frequency for mobile users. This indicates the maximum computing frequency of the drone. This indicates the maximum calculation frequency for low Earth orbit satellites. This indicates the maximum transmit power when a mobile user communicates with an associated drone. This indicates the maximum transmission power for mobile users to communicate with low Earth orbit satellites. d represents the energy budget of the drone. min V represents the minimum safe distance between any two drones. max This represents the maximum flight speed of the drone. The initial and final positions of each drone are defined as follows: v k [n] represents the velocity of drone k in time slot n; the position of drone k in the nth time slot is represented as... Where q k [n] = (x k [n],y k [n]) represents its horizontal coordinate; the position of mobile user m in the nth time slot is represented as... Where s m [n] = (x m [n],y m [n]); α represents the number of calculation cycles per second for mobile user m in the nth time slot; m,k [n] is a binary indicator variable, where α m,k [n] = 1 indicates that mobile user m is associated with drone k in the nth time slot; otherwise, α m,k [n] = 0; This represents the number of computation cycles per second allocated by drone k to mobile user m in the nth time slot; These represent the proportions of tasks processed locally, unloaded to associated drones, and unloaded to low Earth orbit satellites, respectively. This is the energy consumed by mobile user m when offloading a task to its associated drone; This refers to the flight energy consumption of the drone k; This refers to the energy consumed by the drone (k) in processing its tasks. p is the total latency for mobile user m; m,LEO [n] represents the transmission power when mobile user m communicates with a low Earth orbit satellite; p m,k [n] represents the transmission power when mobile user m communicates with drone k; This represents the number of computation cycles per second allocated by a low Earth orbit satellite to mobile user m in the nth time slot.

3. The method according to claim 2, characterized in that, Under the condition of fixed variables Q, P, Ω, F, the subproblem of optimizing the relationship between mobile users and drones is expressed as: The constraints are: (17e)-(17f),(17n)-(17o) in, Let y represent a matrix of size M×N, and let y be updated alternately according to the following formula: in: Among them, R m,k [n] represents the transmission rate from mobile user m to drone k, κ U This represents the energy efficiency factor for unmanned aerial vehicle (UAV) mission processing.

4. The method according to claim 3, characterized in that, Under the condition of fixed variables Q, P, F, α, the task offloading decision subproblem SP2 for optimizing mobile users is expressed as: The constraints are: (17g)-(17h),(17n)-(17o) in: Among them, κ S κ represents the energy efficiency factor used in mission calculations for low Earth orbit satellites. L R represents the energy efficiency factor calculated locally for all mobile users. m,LEO [n] represents the transmission rate between mobile user m and a low Earth orbit satellite.

5. The method according to claim 4, characterized in that, Under the condition of fixed variables Q, Ω, and α, the subproblem SP3 of optimizing transmit power control and calculating frequency allocation is expressed as follows: The constraints are set as follows: (17i)-(17o) in: Where, p m,LEO This indicates the transmission power when mobile user m communicates with a low Earth orbit satellite.

6. The method according to claim 5, characterized in that, Under the condition of fixed variables P, Ω, F, α, the subproblem SP4 of optimizing the UAV trajectory is expressed as: The constraints are: (17b)-(17d),(17n)-(17o).

7. The method according to claim 4, characterized in that, The interior-point method is used to solve the task offloading decision subproblem SP2 for optimizing mobile users.

8. The method according to claim 2, characterized in that, For the joint optimization problem, a quadratic transformation method is used to transform the fractional structure to decouple the variables in the numerator and denominator, thereby decomposing it into the subproblem of optimizing the relationship between mobile users and UAVs, the subproblem of optimizing the task offloading decision of mobile users, the subproblem of optimizing the transmit power control and calculating the frequency allocation, and the subproblem of optimizing the UAV trajectory.

9. A computer-readable storage medium having a computer program stored thereon, wherein, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 8.

10. A computer device comprising a memory and a processor, wherein a computer program capable of running on the processor is stored in the memory, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 8.

Images