A method for optimizing edge computing resources of unmanned aerial vehicle assisted ground equipment movement

Abstract

The application discloses a kind of unmanned aerial vehicle auxiliary ground equipment mobile edge computing resource optimization method, comprising: constructing unmanned aerial vehicle auxiliary ground equipment mobile edge computing system model;Ground cognitive equipment and unmanned aerial vehicle spectrum resource sharing model are constructed, and the uplink transmission rate of ground cognitive equipment is calculated;Total data amount generated by ground cognitive equipment is obtained, and unmanned aerial vehicle computing queuing model is constructed based on the uplink transmission rate of ground cognitive equipment, to obtain queuing calculation delay;According to the spectrum sensing performance of ground cognitive equipment, the total loss of unmanned aerial vehicle auxiliary ground equipment mobile edge computing system is obtained;Under the constraints of the transmission power of ground cognitive equipment and the constraints of calculation frequency, the optimization model of the total loss of unmanned aerial vehicle auxiliary ground equipment mobile edge computing system is constructed, to minimize the loss of unmanned aerial vehicle auxiliary mobile edge system.Effectively improve spectrum utilization, and reduce system loss.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a method for optimizing mobile edge computing resources for UAV-assisted ground equipment. Background Technology

[0002] As a highly maneuverable low-altitude aircraft, drones can serve as mobile platforms in communication processes, obtaining wireless channels with device terminals and extending the effective communication coverage. Drones also play a crucial role in emergency communication scenarios. However, in disaster scenarios, local equipment operation generates enormous computational demands and significantly increases energy consumption. The high time cost of base station construction and the limited computing power of device terminals may prevent the equipment from meeting user needs. Therefore, how to coordinate and allocate the computational load of device terminals to reduce latency and energy consumption is an urgent problem to be solved.

[0003] To address the aforementioned issues, the concept of mobile edge computing (MEC) was proposed. MEC utilizes a distributed architecture to deploy computing power at the network edge, significantly reducing the long processing latency of traditional centralized architectures. Drones themselves can also be equipped with high-performance computing chips and fly to the vicinity of users. To further reduce the energy consumption of ground equipment in remote areas, MEC servers can be built on drones, thereby providing offloading services for computing tasks to nearby ground equipment. Simultaneously, its high mobility greatly reduces the construction costs of traditional base stations as edge nodes and enhances the flexibility of MEC systems. When large-scale changes occur in the ground equipment network, drones can adapt to the current system by adjusting their own status, reducing the costs associated with building or adjusting fixed base station solutions.

[0004] With the widespread application of MEC distributed architecture assisted by drones and the continuous growth of wireless services, the demand for spectrum from all parties is also constantly increasing. Under the actual condition of low spectrum resource utilization, coupled with the increasing demand for frequency resources for public basic communication services, and the characteristics of large data transmission volume and high bandwidth consumption of MEC equipment terminals, the available spectrum resources are becoming increasingly scarce. Summary of the Invention

[0005] This invention provides a method for optimizing mobile edge computing resources for UAV-assisted ground equipment, in order to solve the technical problem of increasingly scarce spectrum resources in UAV-assisted ground equipment mobile edge systems.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] On one hand, the present invention provides a method for optimizing mobile edge computing resources for UAV-assisted ground equipment, the method comprising:

[0008] A mobile edge computing system model for UAV-assisted ground equipment is constructed; wherein, the system model includes multiple ground cognitive devices and at least one UAV, the UAV hovering at a fixed altitude;

[0009] Based on the UAV-assisted ground equipment mobile edge computing system model, a spectrum resource sharing model between ground cognitive equipment and UAV is constructed, and the uplink transmission rate of ground cognitive equipment is calculated.

[0010] The total amount of data generated by the ground-based cognitive device is obtained, and a UAV computing queuing model is constructed based on the uplink transmission rate of the ground-based cognitive device to obtain the UAV-side queuing computing latency.

[0011] Based on the spectrum sensing performance of the ground cognitive equipment, the total loss of the UAV-assisted ground equipment mobile edge computing system is obtained; under the constraints of the ground cognitive equipment's transmit power and computing frequency, an optimization model for the total loss of the UAV-assisted ground equipment mobile edge computing system is constructed.

[0012] Based on the optimization model of total loss, the hovering position of the UAV, the perception time of the UAV, the transmission power of the ground cognition device, the computing frequency of the ground cognition device, the computing frequency of the UAV, and the offloading ratio are jointly optimized to minimize the loss of the UAV-assisted ground equipment mobile edge computing system.

[0013] Furthermore, the construction of the UAV-assisted ground equipment mobile edge computing system model includes:

[0014] Acquire the coordinates of multiple ground-based cognitive devices and the initial hovering position of the drone;

[0015] Calculate the relative distance and channel parameters between the ground-based cognitive equipment and the UAV;

[0016] The system model is constructed based on the coordinates of the ground cognitive device and the initial hovering position of the UAV, as well as the calculated relative distance and channel parameters between the ground cognitive device and the UAV.

[0017] Furthermore, in the spectrum resource sharing model between the ground cognitive device and the UAV, the ground cognitive device acts as the source and the UAV acts as the destination.

[0018] During the data offloading process from the ground-based cognitive device to the drone as the primary user, the ground-based cognitive device performs spectrum sensing and, based on the CSMA / CA protocol, opportunistically accesses the drone's idle frequency band. This includes: firstly, detecting the drone's ACK signal using an energy detection algorithm; if no ACK signal is detected, the ground-based cognitive device accesses the drone's shared frequency band with a certain access probability to offload the data.

[0019] Furthermore, the formula for calculating the uplink transmission rate of the ground-based cognitive device is as follows:

[0020]

[0021] in, This represents the uplink transmission rate of the m-th ground-based cognitive device; This represents the access probability when the m-th ground-based cognitive device correctly detects the frequency band occupancy and performs data offloading, provided that the drone's frequency band is not occupied by other users; h m This represents the ground-to-air channel gain between the m-th ground-based cognitive device and the UAV; This represents the transmission power of the m-th ground-based cognitive device; h represents the access probability when the m-th ground-based cognitive device fails to correctly detect the frequency band occupancy, and the frequency band is occupied by other users; m-1 This represents the ground-to-air channel gain between the (m-1)th ground-based cognitive device and the UAV; σ represents the transmit power of the (m-1)th ground-based cognitive device; W represents the channel transmission bandwidth; v This represents the variance of the channel noise.

[0022] Furthermore, the formula for calculating the queuing delay on the UAV side is as follows:

[0023]

[0024] in, Indicates the queuing calculation latency at the drone end; M represents the number of ground-based cognitive devices; θ m B represents the unloading ratio of the m-th ground-based cognitive device. m r is the total number of bits in the task; m The number of data bits received by the drone per unit time; f is the number of computational bits of the drone per unit time. U This indicates the drone's calculation frequency; η represents the queuing computation latency of the m-th ground-based cognitive device on the drone; η represents the number of CPU revolutions per bit.

[0025] Furthermore, the process of obtaining the total loss of the UAV-assisted ground equipment mobile edge computing system based on the spectrum sensing performance of the ground cognitive equipment includes:

[0026] The perceived delay T of the computing system s Unloading delay T o Calculate latency The formula is as follows:

[0027]

[0028]

[0029]

[0030] in, This indicates the arrival rate of the data queue received by the drone. T represents the sensing latency of the m-th ground-based cognitive device. om This represents the unloading delay of the m-th ground-based cognitive device. This represents the local computation latency of the m-th ground-based cognitive device. This represents the number of computational bits for the m-th ground-based cognitive device per unit time. This represents the calculation frequency of the m-th ground device;

[0031] Total system latency

[0032] The offloading energy consumption E of the computing system o Local computing power consumption E DC Drone queuing calculation energy consumption E UC The formula is as follows:

[0033]

[0034]

[0035]

[0036] Where κ represents the effective capacitance coefficient of the CPU in the UAV and ground-based cognitive devices. T represents the offloading energy consumption of the m-th ground-based cognitive device. om This represents the unloading delay of the m-th ground-based cognitive device. This represents the local computing power consumption of the m-th ground-based cognitive device. This represents the energy consumption for queuing calculations of the m-th ground-based cognitive device on the drone. This represents the local computation latency of the m-th ground-based cognitive device. This represents the queuing computation delay of the m-th ground-based cognitive device on the drone.

[0037] The total energy consumption of the computing system is E = E o +E DC +E UC ;

[0038] Calculate the total loss H of the mobile edge computing system for UAV-assisted ground equipment: H = β1T + β2E; where β1 and β2 represent the time delay weight and energy consumption weight, respectively.

[0039] Furthermore, the optimization model for the total loss of the UAV-assisted ground equipment mobile edge computing system is expressed as follows:

[0040] OP:

[0041]

[0042]

[0043]

[0044]

[0045] 0 < θ m <1,

[0046] f U ≤F max ,

[0047]

[0048] Where q represents the hovering position of the drone. Indicates the transmission power of ground-based cognitive equipment. This indicates the sensing latency of ground-based cognitive devices. f represents the computing power of ground-based cognitive devices. U θ represents the frequency at which the drone calculates data. m H represents the unloading ratio of ground-based cognitive equipment, and w represents the hovering altitude of the drone. m Represents the coordinates of the m-th ground-based cognitive device, the... p represents the maximum distance between the drone and the ground-based cognitive device. max For the maximum transmission power of ground-based cognitive equipment, t smax P represents the maximum sensing time. dmin F represents the minimum detection probability. max F represents the maximum computing frequency of the drone. D This represents the total computing resources of ground-based cognitive equipment; This represents the actual distance between the drone and the m-th ground-based cognitive device. This represents the detection probability of the m-th ground device.

[0049] Furthermore, based on the aforementioned optimization model, joint optimization is achieved for the UAV hovering position, UAV perception time, ground cognitive device transmission power, ground cognitive device computing frequency, UAV computing frequency, and offloading ratio, minimizing the losses of the UAV-assisted ground equipment mobile edge computing system, including:

[0050] The optimization model for the total loss is decomposed into a first model with the UAV hovering position and the transmission power of the ground cognitive device as independent variables, a second model with the sensing latency of the ground cognitive device as an independent variable, a third model with the computing frequency of the ground cognitive device as an independent variable, a fourth model with the computing frequency of the UAV as an independent variable, and a fifth model with the unloading ratio of the ground cognitive device as an independent variable; wherein,

[0051] The first model is as follows:

[0052] OP1:

[0053]

[0054]

[0055] The second model is as follows:

[0056] OP2:

[0057]

[0058]

[0059] The third model is as follows:

[0060] OP3:

[0061]

[0062] The fourth model is as follows:

[0063] OP4:

[0064] stf U ≤F max ,

[0065] The fifth model is as follows:

[0066] OP5:

[0067] st0<θ m <1

[0068] Solving the first, second, third, fourth, and fifth models achieves joint optimization of UAV hovering position, UAV perception time, ground cognitive device transmission power, ground cognitive device computing frequency, UAV computing frequency, and offloading ratio, minimizing the loss of the UAV-assisted ground equipment mobile edge computing system, and obtaining the computing resource optimization results.

[0069] In another aspect, the present invention also provides an electronic device comprising a processor and a memory; wherein the memory stores at least one instruction, which is loaded and executed by the processor to implement the above-described method.

[0070] In another aspect, the present invention also provides a computer-readable storage medium storing at least one instruction that is loaded and executed by a processor to implement the above-described method.

[0071] The beneficial effects of the technical solution provided by this invention include at least the following:

[0072] This invention provides a method for optimizing mobile edge computing resources for UAV-assisted ground equipment. Applied to situations where spectrum is scarce, the UAV acts as the primary user, carrying an edge computing server to provide shared spectrum and computation offloading services to multiple ground cognitive devices. Based on the spectrum perception performance of the ground cognitive devices for the primary user, this method develops a loss optimization model for UAV-assisted mobile edge computing. Through joint optimization of the UAV hovering position, ground cognitive device transmit power, computation frequency and perception time, UAV computation frequency, and offloading ratio, it minimizes system losses, effectively improves spectrum utilization, and significantly reduces system losses. Attached Figure Description

[0073] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0074] Figure 1 This is a schematic diagram of the execution flow of the mobile edge computing resource optimization method for UAV-assisted ground equipment provided in an embodiment of the present invention;

[0075] Figure 2 This is a schematic diagram of a cognitive-based UAV-assisted ground equipment mobile edge computing system model provided in an embodiment of the present invention;

[0076] Figure 3 This is a schematic diagram illustrating the variation of system loss with ground cognitive equipment under different operating parameters of the optimized ground cognitive equipment and UAV, provided by an embodiment of the present invention. Detailed Implementation

[0077] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0078] First Embodiment

[0079] This embodiment provides a method for optimizing mobile edge computing resources for UAV-assisted ground equipment. It is applied to a communication system where the UAV acts as the primary user, carrying an edge computing server to provide spectrum sharing and computation offloading services to ground-based cognitive equipment in situations of spectrum scarcity. This method can be implemented by an electronic device, which can be a terminal or a server. The execution flow of this method is as follows: Figure 1 As shown, it includes the following steps:

[0080] S1, Construct a mobile edge computing system model for UAV-assisted ground equipment;

[0081] Among them, the primary and secondary users in the cognitive network are drones and ground-based cognitive devices.

[0082] Specifically, in this embodiment, the implementation process of S1 is as follows:

[0083] S11, acquire the coordinates of multiple ground-based cognitive devices and the initial hovering position of the drone;

[0084] S12, calculate the relative distance and channel parameters between the ground-based cognitive equipment and the UAV;

[0085] S13. Based on the obtained coordinates of the ground cognitive device and the initial hovering position of the UAV, as well as the calculated relative distance and channel parameters between the ground cognitive device and the UAV, the system model is constructed.

[0086] The mobile edge computing system model for UAV-assisted ground equipment constructed in this embodiment is as follows: Figure 2 As shown, the UAV-assisted ground equipment mobile edge computing system model includes a UAV and M ground-based cognitive devices for spectrum sensing and computational offloading. The UAV hovers at a fixed altitude H, its hovering position is denoted as q = (x, y), and its transmission power is p. U The coordinate distribution of the ground-based cognitive equipment is W. m ={w1,w2,...,w M The transmission power of the ground-based cognitive device m is

[0087] S2, Based on the UAV-assisted ground equipment mobile edge computing system model, construct a spectrum resource sharing model between ground cognitive equipment and UAV, and calculate the uplink transmission rate of ground cognitive equipment;

[0088] In the spectrum resource sharing model, the ground cognitive device is the source and the drone is the destination. During the process of the ground cognitive device offloading data to the drone, which is the main user, the ground cognitive device performs spectrum sensing and, based on the CSMA / CA protocol, opportunistically accesses the drone's idle frequency band. Specifically, this includes: firstly, detecting the drone's ACK signal through an energy detection algorithm; if no ACK signal is detected, the ground cognitive device accesses the drone's shared frequency band with a certain access probability to offload data.

[0089] Specifically, in this embodiment, the implementation process of S2 is as follows:

[0090] S21, calculate the relative distance between the m-th ground sensing device and the UAV. Based on the ground-to-air line-of-sight propagation from the ground equipment to the UAV, the ground-to-air channel gain is obtained as follows:

[0091]

[0092] Where β0 is the average channel gain at a distance of 1 meter, and α0 is the channel fading coefficient.

[0093] Assume H0 represents the drone's spectrum being unoccupied and in an idle state. H1 represents the drone's spectrum being occupied and in a busy state. Therefore, considering the drone's spectrum state, the detection signal of the ground device m is represented as follows:

[0094]

[0095] Among them, y m This represents the detection signal received by ground device m, x is the transmission signal from the UAV, and v m With zero as the mean, Gaussian white noise with variance.

[0096] S22, For each UAV, using energy detection as the sensing method, the false detection probability of the ground-based cognitive device m can be obtained. and detection probability It is expressed as follows:

[0097]

[0098]

[0099] Where τ is the energy detection threshold, The signal-to-noise ratio (SNR) received by ground equipment m can be expressed as: f is the sensing time of ground device m. s It is the sampling rate. This represents the Gaussian Q-function.

[0100] S23, when the drone's frequency band is not occupied by other users, and the ground-based cognitive device m correctly detects the frequency band occupancy, it will perform data offloading, at which point there is a probability of access. Represented as:

[0101]

[0102] Wherein, P(H0) represents the probability that the drone frequency band is not occupied by other users.

[0103] When a drone's frequency band is occupied by other users, and the ground-based cognitive device (m) fails to correctly detect the frequency band occupancy, it will also perform data offloading. However, since the user who previously performed data offloading is still offloading data, there is a probability of access failure. Represented as:

[0104]

[0105] Where P(H1) represents the probability that the drone's frequency band is occupied by other users.

[0106] S24, based on access probability and The uplink transmission rate of the ground-based cognitive device m is obtained and expressed as:

[0107]

[0108] in, This represents the uplink transmission rate of the m-th ground-based cognitive device; This represents the access probability when the m-th ground-based cognitive device correctly detects the frequency band occupancy and performs data offloading, provided that the drone's frequency band is not occupied by other users; h m This represents the ground-to-air channel gain between the m-th ground-based cognitive device and the UAV; This represents the transmission power of the m-th ground-based cognitive device; h represents the access probability when the m-th ground-based cognitive device fails to correctly detect the frequency band occupancy, and the frequency band is occupied by other users; m-1 This represents the ground-to-air channel gain between the (m-1)th ground-based cognitive device and the UAV; σ represents the transmit power of the (m-1)th ground-based cognitive device; W represents the channel transmission bandwidth; v This represents the covariance of the channel noise.

[0109] S3, obtain the total amount of data generated by the ground cognitive device, construct the UAV computing queuing model based on the uplink transmission rate of the ground cognitive device, and obtain the UAV terminal queuing computing latency;

[0110] Among them, the amount of data generated by the ground-based cognitive device m is C. m ={B m D m}, where B m D represents the total number of bits in the task. m The number of CPU revolutions required to process the task. The offloading ratio of the ground-based cognitive device m is θ. m The calculated frequency is The drone's calculation frequency is f U The total unloading data volume of M ground devices is The unloading calculation data received by the drone is arranged in a single-in, single-out queue, and the arrival rate of the data queue received by the drone is... Based on this, the formula for calculating the queuing latency on the drone side is:

[0111]

[0112] in, Indicates the queuing calculation latency at the drone end; M represents the number of ground-based cognitive devices; θ m B represents the unloading ratio of the m-th ground-based cognitive device. m r is the total number of bits in the task; m The number of data bits received by the drone per unit time; f is the number of computational bits of the drone per unit time. U This indicates the drone's calculation frequency; η represents the queuing computation latency of the m-th ground-based cognitive device on the drone; η represents the number of CPU revolutions per bit.

[0113] S4. Based on the spectrum sensing performance of the ground cognitive equipment, the total loss of the UAV-assisted ground equipment mobile edge computing system is obtained; under the constraints of the ground cognitive equipment's transmit power and computing frequency, an optimization model for the total loss of the UAV-assisted ground equipment mobile edge computing system is constructed.

[0114] Specifically, in this embodiment, the implementation process of S4 is as follows:

[0115] S41, Perceived latency T of the computing system s Unloading delay T o Calculate latency The formula is as follows:

[0116]

[0117]

[0118]

[0119] in, This indicates the arrival rate of the data queue received by the drone. T represents the sensing latency of the m-th ground-based cognitive device. om This represents the unloading delay of the m-th ground-based cognitive device. This represents the local computation latency of the m-th ground-based cognitive device. This represents the number of computational bits for the m-th ground-based cognitive device per unit time. This represents the calculation frequency of the m-th ground device;

[0120] S42, Calculate the total system delay

[0121] S43, Offloading energy consumption of the computing system E o Local computing power consumption E DC Drone queuing calculation energy consumption E UC The formula is as follows:

[0122]

[0123]

[0124]

[0125] Where κ represents the effective capacitance coefficient of the CPU in the UAV and ground-based cognitive devices. T represents the offloading energy consumption of the m-th ground-based cognitive device. om This represents the unloading delay of the m-th ground-based cognitive device. This represents the local computing power consumption of the m-th ground-based cognitive device. This represents the energy consumption for queuing calculations of the m-th ground-based cognitive device on the drone. This represents the local computation latency of the m-th ground-based cognitive device. This represents the queuing computation delay of the m-th ground-based cognitive device on the drone.

[0126] S44, calculate the total energy consumption of the system E = E o +E DC +E UC ;

[0127] S45, calculate the total loss H = β1T + β2E of the mobile edge computing system for UAV-assisted ground equipment; where β1 and β2 represent the time delay weight and energy consumption weight, respectively.

[0128] In this embodiment, based on the loss optimization model of the UAV-assisted mobile edge system, the optimized UAV hovering position q and the ground cognitive device transmission power are obtained. Ground-based cognitive equipment sensing latency Ground-based cognitive equipment computing power UAV calculation frequency f U θ, the proportion of ground-based cognitive equipment unloading m .

[0129] The optimization model for the total loss of a UAV-assisted mobile edge computing system for ground equipment includes a loss minimization function and constraints. The independent variables of the loss minimization function include the UAV hovering position q and the transmit power of the ground cognitive equipment. Ground-based cognitive equipment sensing latency Ground-based cognitive equipment computing power UAV calculation frequency f U θ, the proportion of ground-based cognitive equipment unloading m The constraints include the relative distance constraint between the ground cognitive device and the UAV, the ground device's transmit power constraint, the ground device's maximum sensing delay constraint, the minimum detection probability constraint, the offloading ratio constraint, the maximum UAV computing frequency constraint, and the ground cognitive device's computing frequency constraint; the optimization model is specifically represented as follows:

[0130] OP:

[0131]

[0132]

[0133]

[0134]

[0135] 0 < θ m <1,

[0136] f U ≤F max ,

[0137]

[0138] Where q represents the hovering position of the drone. Indicates the transmission power of ground-based cognitive equipment. This indicates the sensing latency of ground-based cognitive devices. f represents the computing power of ground-based cognitive devices. U θ represents the frequency at which the drone calculates data. mH represents the unloading ratio of ground-based cognitive equipment, and w represents the hovering altitude of the drone. m Represents the coordinates of the m-th ground-based cognitive device, the... p represents the maximum distance between the drone and the ground-based cognitive device. max For the maximum transmission power of ground-based cognitive equipment, t smax P represents the maximum sensing time. dmin F represents the minimum detection probability. max F represents the maximum computing frequency of the drone. D This represents the total computing resources of ground-based cognitive equipment; This represents the actual distance between the drone and the m-th ground-based cognitive device. This represents the detection probability of the m-th ground device.

[0139] S5. Based on the optimization model of the total loss, the joint optimization of the UAV hovering position, UAV perception time, ground cognitive device transmission power, ground cognitive device computing frequency, UAV computing frequency and offloading ratio is realized to minimize the loss of the UAV-assisted ground device mobile edge computing system.

[0140] Furthermore, based on the aforementioned optimization model, joint optimization is achieved for the UAV hovering position, UAV perception time, ground cognitive device transmission power, ground cognitive device computing frequency, UAV computing frequency, and offloading ratio. This yields optimized results for the UAV hovering position, ground cognitive device transmission power, ground cognitive device perception latency, ground cognitive device computing power, UAV computing frequency, and ground cognitive device offloading ratio, minimizing the losses of the UAV-assisted ground equipment mobile edge computing system. This includes:

[0141] S51, the optimization model for the total loss is decomposed into a first model with the UAV hovering position and the transmission power of the ground cognitive device as independent variables, a second model with the sensing delay of the ground cognitive device as an independent variable, a third model with the computing frequency of the ground cognitive device as an independent variable, a fourth model with the computing frequency of the UAV as an independent variable, and a fifth model with the unloading ratio of the ground cognitive device as an independent variable; wherein,

[0142] Decomposing the objective function and the constraints, we obtain the first model with the UAV hovering position and the ground-based cognitive device transmission power as independent variables as follows:

[0143] OP1:

[0144]

[0145]

[0146] The second model, with the sensing delay of the ground-based cognitive device as the independent variable, is as follows:

[0147] OP2:

[0148]

[0149]

[0150] The third model, with the calculation frequency of the ground-based cognitive device as the independent variable, is as follows:

[0151] OP3:

[0152]

[0153] The fourth model, with the drone's calculation frequency as the independent variable, is as follows:

[0154] OP4:

[0155] stf U ≤F max ,

[0156] The fifth model, with the unloading ratio of the ground-based cognitive equipment as the independent variable, is as follows:

[0157] OP5:

[0158] st0<θ m <1

[0159] The first, second, third, fourth, and fifth models are iteratively solved to achieve joint optimization of UAV hovering position, UAV perception time, ground cognitive device transmission power, ground cognitive device computing frequency, UAV computing frequency, and offloading ratio, minimizing the loss of the UAV-assisted ground equipment mobile edge computing system and obtaining the computing resource optimization results.

[0160] To better understand the UAV-assisted ground equipment mobile edge computing resource optimization method described in this embodiment of the invention, a specific application example will be used for detailed explanation:

[0161] Step 1, assume that the offloading communication model between ground cognitive devices and UAVs includes M ground cognitive devices, whose coordinate distribution is W. m ={w1,w2,...,w M The transmission power of the ground-based cognitive device m is The drone hovers at a fixed altitude H, its hovering position is denoted as q = (x, y), and its transmission power is p. UThe value is 0.035W. The ground-based cognitive equipment performs spectrum sensing; after detecting that the spectrum is unoccupied, it offloads data, following these steps:

[0162] 1.1) The relative distance between the ground-based cognitive device m and the UAV is Based on the ground-to-air line-of-sight propagation from the ground-based cognitive equipment to the UAV, the ground-to-air channel gain is obtained as h. m ;

[0163] 1.2) Ground-based cognitive equipment performs spectrum sensing based on the CSMA / CA protocol, opportunistically accessing the idle frequency bands of UAVs. The detection signal received by ground-based cognitive equipment m is y. m , is represented as:

[0164]

[0165] Among them, y m This represents the detection signal received by ground device m, x is the transmission signal from the UAV, and v m With zero as the mean and variance Gaussian white noise.

[0166] 1.3) For each UAV, using energy detection as the sensing method, the false detection probability of the ground-based cognitive device m can be obtained. and detection probability When the drone's frequency band is not occupied by other devices, and the ground-based cognitive device (m) correctly detects the frequency band occupancy, it will perform data offloading, at which point there is a probability of access. When a drone's frequency band is occupied by other devices, and the ground-based cognitive device m fails to correctly detect the frequency band occupancy, it will also perform data offloading. However, since the device that previously performed data offloading is still offloading data, there is a probability of access failure at this time.

[0167] 1.4) Based on access probability and The uplink transmission rate of the ground-based cognitive device m can be obtained, and can be expressed as:

[0168]

[0169] Step 2, construct the objective function for the loss of the UAV-assisted mobile edge system. Specific steps include:

[0170] 2.1) Let C be the amount of data generated by the ground-based cognitive device m. m ={B m D m}, where B m D represents the total number of bits in the task. mThe number of CPU revolutions required to process the task. The offloading ratio of the ground-based cognitive device m is θ. m The calculated frequency is The drone's calculation frequency is f U .

[0171] 2.2) The queuing calculation delay at the drone end is obtained as follows:

[0172]

[0173] Where η = 1000 is the number of CPU revolutions per bit.

[0174] 2.3) System sensing delay T s Unloading delay T o Calculate latency The delay for drone queuing calculation is Represented as:

[0175]

[0176]

[0177]

[0178] Total system latency

[0179] 2.4) The system unloading energy consumption is E o The calculated energy consumption is E. DC The delay for drone queuing calculation is E. UC , is represented as:

[0180]

[0181]

[0182]

[0183] Where κ = 10 -28 This represents the effective capacitance coefficient of the CPU. The total system energy consumption is E = E o +E DC +E UC ;

[0184] Step 3, optimize the loss of the drone-assisted mobile edge system, specifically including:

[0185] 3.1) Set the maximum distance between the drone and the ground equipment. Maximum ground equipment transmit power p max =6w, maximum perception time t smax =1s, minimum detection probability P dmin=0.95, the maximum computing frequency F of the UAV max =3×10 9 Hz, the total computing resources of ground-based cognitive devices F D =10 9 Hz;

[0186] 3.2) Establish a loss optimization model for the UAV-assisted mobile edge system, and obtain the optimized UAV hovering position q and the transmission power of the ground cognitive device. Ground-based cognitive equipment sensing latency Ground-based cognitive equipment computing power UAV calculation frequency f U θ, the proportion of ground-based cognitive equipment unloading m .

[0187] like Figure 3 As shown, Figure 3 This diagram illustrates the variation of system loss with ground-based cognitive devices under optimized operating parameters for both ground-based cognitive devices and unmanned aerial vehicles (UAVs), as provided in this embodiment. The horizontal axis represents the number of ground-based cognitive devices, and the vertical axis represents system loss.

[0188] from Figure 3 It can be seen that as the number of ground-based cognitive devices increases, the system loss continuously increases. Under the same number of ground-based cognitive devices, the system loss in this invention continuously decreases with optimization of the first model (using the UAV hovering position and ground-based cognitive device transmission power as independent variables), the second model (using the ground-based cognitive device perception delay as an independent variable), the third model (using the ground-based cognitive device calculation frequency as an independent variable), the fourth model (using the UAV calculation frequency as an independent variable), and the fifth model (using the ground-based cognitive device offloading ratio as an independent variable). This indicates that the method provided by this invention can effectively reduce system loss under different numbers of users.

[0189] In summary, this embodiment provides a method for optimizing mobile edge computing resources for UAV-assisted ground equipment. In this method, ground cognitive devices perform spectrum sensing on UAV spectrum resources. After sensing that the UAV spectrum is not occupied, they share spectrum resources with the UAV for data offloading. The method first obtains the coordinates of multiple ground cognitive devices and the initial hovering position of the UAV, calculates the relative distance and channel parameters between the ground cognitive devices and the UAV, and constructs a communication model between the ground cognitive devices and the UAV. Based on the CSMA / CA protocol, the ground cognitive devices construct a spectrum resource sharing model with the UAV. The ground cognitive devices access the shared frequency band of the UAV with a certain access probability for data offloading, and calculate the uplink transmission rate of the ground devices. The total amount of data generated by the ground cognitive devices is obtained, and a UAV computing queuing model is constructed based on the uplink transmission rate of the ground cognitive devices to obtain the queuing computing delay. Based on the spectrum sensing performance of the ground cognitive devices, the offloading delay and energy consumption of the ground cognitive devices, the computing delay and energy consumption of the ground cognitive devices, and the edge computing delay and energy consumption of the UAV are calculated respectively, thereby deriving the total loss expression of the UAV-assisted mobile edge system. By jointly optimizing the hovering position of the UAV, the perception time of the UAV, the transmission power of the ground cognition device, the calculation frequency of the ground cognition device, the calculation frequency of the UAV, and the offloading ratio, the technical effect of minimizing system loss, effectively improving spectrum utilization, and significantly reducing system loss is achieved.

[0190] Second Embodiment

[0191] This embodiment provides an electronic device, which includes a processor and a memory; wherein the memory stores at least one instruction, which is loaded and executed by the processor to implement the method of the first embodiment.

[0192] The electronic device can vary considerably depending on its configuration or performance, and may include one or more processors (central processing units, CPUs) and one or more memories, wherein the memories store at least one instruction that is loaded by the processor and executed in accordance with the above method.

[0193] Third Embodiment

[0194] This embodiment provides a computer-readable storage medium storing at least one instruction, which is loaded and executed by a processor to implement the method of the first embodiment described above. The computer-readable storage medium may be a ROM, random access memory, CD-ROM, magnetic tape, floppy disk, or optical data storage device, etc. The instruction stored therein can be loaded and executed by a processor in a terminal.

[0195] Furthermore, it should be noted that the present invention can be provided as a method, apparatus, or computer program product. Therefore, embodiments of the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, embodiments of the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code.

[0196] Embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0197] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing terminal equipment to cause a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0198] It should also be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0199] Finally, it should be noted that the above description represents a preferred embodiment of the present invention. It should be pointed out that although preferred embodiments have been described, those skilled in the art, once they understand the basic inventive concept of the present invention, can make various improvements and modifications without departing from the principles described herein. These improvements and modifications should also be considered within the scope of protection of the present invention. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the embodiments of the present invention.

Claims

1. A method for optimizing mobile edge computing resources for UAV-assisted ground equipment, characterized in that, The method for optimizing mobile edge computing resources for UAV-assisted ground equipment includes: A mobile edge computing system model for UAV-assisted ground equipment is constructed; wherein, the system model includes multiple ground cognitive devices and at least one UAV, the UAV hovering at a fixed altitude; Based on the UAV-assisted ground equipment mobile edge computing system model, a spectrum resource sharing model between ground cognitive equipment and UAV is constructed, and the uplink transmission rate of ground cognitive equipment is calculated. The total amount of data generated by the ground-based cognitive device is obtained, and a UAV computing queuing model is constructed based on the uplink transmission rate of the ground-based cognitive device to obtain the UAV-side queuing computing latency. Based on the spectrum sensing performance of the ground cognitive equipment, the total loss of the UAV-assisted ground equipment mobile edge computing system is obtained; under the constraints of the ground cognitive equipment's transmit power and computing frequency, an optimization model for the total loss of the UAV-assisted ground equipment mobile edge computing system is constructed. Based on the optimization model of total loss, the hovering position of the UAV, the perception time of the UAV, the transmission power of the ground cognition device, the computing frequency of the ground cognition device, the computing frequency of the UAV, and the offloading ratio are jointly optimized to minimize the loss of the UAV-assisted ground equipment mobile edge computing system.

2. The method for optimizing mobile edge computing resources for UAV-assisted ground equipment as described in claim 1, characterized in that, The construction of the UAV-assisted ground equipment mobile edge computing system model includes: Acquire the coordinates of multiple ground-based cognitive devices and the initial hovering position of the drone; Calculate the relative distance and channel parameters between the ground-based cognitive equipment and the UAV; The system model is constructed based on the coordinates of the ground cognitive device and the initial hovering position of the UAV, as well as the calculated relative distance and channel parameters between the ground cognitive device and the UAV.

3. The method for optimizing mobile edge computing resources for UAV-assisted ground equipment as described in claim 1, characterized in that, In the spectrum resource sharing model between ground-based cognitive devices and UAVs, the ground-based cognitive devices act as the source and the UAVs act as the destination. During the data offloading process from the ground-based cognitive device to the drone as the primary user, the ground-based cognitive device performs spectrum sensing and, based on the CSMA / CA protocol, opportunistically accesses the drone's idle frequency band. This includes: firstly, detecting the drone's ACK signal using an energy detection algorithm; if no ACK signal is detected, the ground-based cognitive device accesses the drone's shared frequency band with a certain access probability to offload the data.

4. The method for optimizing mobile edge computing resources for UAV-assisted ground equipment as described in claim 3, characterized in that, The formula for calculating the uplink transmission rate of the ground-based cognitive device is as follows: in, This represents the uplink transmission rate of the m-th ground-based cognitive device; This represents the access probability when the m-th ground-based cognitive device correctly detects the frequency band occupancy and performs data offloading, provided that the drone's frequency band is not occupied by other users; h m This represents the ground-to-air channel gain between the m-th ground-based cognitive device and the UAV; This represents the transmission power of the m-th ground-based cognitive device; h represents the access probability when the m-th ground-based cognitive device fails to correctly detect the frequency band occupancy, and the frequency band is occupied by other users; m-1 This represents the ground-to-air channel gain between the (m-1)th ground-based cognitive device and the UAV; σ represents the transmit power of the (m-1)th ground-based cognitive device; W represents the channel transmission bandwidth; v This represents the variance of the channel noise.

5. The method for optimizing mobile edge computing resources for UAV-assisted ground equipment as described in claim 4, characterized in that, The formula for calculating the queuing delay on the drone side is as follows: in, Indicates the queuing calculation latency at the drone end; M represents the number of ground-based cognitive devices; θ m B represents the unloading ratio of the m-th ground-based cognitive device. m r is the total number of bits in the task; m The number of data bits received by the drone per unit time; f is the number of computational bits of the drone per unit time. U This indicates the drone's calculation frequency; η represents the queuing computation latency of the m-th ground-based cognitive device on the drone; η represents the number of CPU revolutions per bit.

6. The method for optimizing mobile edge computing resources for UAV-assisted ground equipment as described in claim 5, characterized in that, The total loss of the UAV-assisted ground equipment mobile edge computing system is obtained based on the spectrum sensing performance of the ground cognitive equipment, including: The perceived delay T of the computing system s Unloading delay T o Calculate latency The formula is as follows: in, This indicates the arrival rate of the data queue received by the drone. T represents the sensing latency of the m-th ground-based cognitive device. om This represents the unloading delay of the m-th ground-based cognitive device. This represents the local computation latency of the m-th ground-based cognitive device. This represents the number of computational bits for the m-th ground-based cognitive device per unit time. This represents the calculation frequency of the m-th ground device; Total system latency The offloading energy consumption E of the computing system o Local computing power consumption E DC Drone queuing calculation energy consumption E UC The formula is as follows: Where κ represents the effective capacitance coefficient of the CPU in the UAV and ground-based cognitive devices. T represents the offloading energy consumption of the m-th ground-based cognitive device. om This represents the unloading delay of the m-th ground-based cognitive device. This represents the local computing power consumption of the m-th ground-based cognitive device. This represents the queuing computation energy consumption of the m-th ground-based cognitive device on the drone. This represents the local computation latency of the m-th ground-based cognitive device. This represents the queuing computation delay of the m-th ground-based cognitive device on the drone. The total energy consumption of the computing system is E = E o +E DC +E UC ; Calculate the total loss H of the mobile edge computing system for UAV-assisted ground equipment: H = β1T + β2E; where β1 and β2 represent the time delay weight and energy consumption weight, respectively.

7. The method for optimizing mobile edge computing resources for UAV-assisted ground equipment as described in claim 6, characterized in that, The optimization model for the total loss of a UAV-assisted ground equipment mobile edge computing system is expressed as follows: 0<θ m <1, f U ≤F max , Where q represents the hovering position of the drone. Indicates the transmission power of ground-based cognitive equipment. This indicates the sensing latency of ground-based cognitive devices. f represents the computing power of ground-based cognitive devices. U θ represents the frequency at which the drone calculates data. m H represents the unloading ratio of ground-based cognitive equipment, and w represents the hovering altitude of the drone. m This represents the coordinates of the m-th ground-based cognitive device. p represents the maximum distance between the drone and the ground-based cognitive device. max For the maximum transmission power of ground-based cognitive equipment, t smax Indicates the maximum sensing time. F represents the minimum detection probability. max F represents the maximum computing frequency of the drone. D This represents the total computing resources of ground-based cognitive equipment; This represents the actual distance between the drone and the m-th ground-based cognitive device. This represents the detection probability of the m-th ground device.

8. The method for optimizing mobile edge computing resources for UAV-assisted ground equipment as described in claim 7, characterized in that, Based on the optimization model of the total loss, the joint optimization of UAV hovering position, UAV perception time, ground cognitive device transmission power, ground cognitive device computing frequency, UAV computing frequency, and offloading ratio is achieved to minimize the loss of the UAV-assisted ground device mobile edge computing system, including: The optimization model for the total loss is decomposed into a first model with the UAV hovering position and the transmission power of the ground cognitive device as independent variables, a second model with the sensing latency of the ground cognitive device as an independent variable, a third model with the computing frequency of the ground cognitive device as an independent variable, a fourth model with the computing frequency of the UAV as an independent variable, and a fifth model with the unloading ratio of the ground cognitive device as an independent variable; wherein, The first model is as follows: The second model is as follows: The third model is as follows: The fourth model is as follows: s.t.f U ≤F max , The fifth model is as follows: s.t.0<θ m <1 Solving the first, second, third, fourth, and fifth models achieves joint optimization of UAV hovering position, UAV perception time, ground cognitive device transmission power, ground cognitive device computing frequency, UAV computing frequency, and offloading ratio, minimizing the loss of the UAV-assisted ground equipment mobile edge computing system, and obtaining the computing resource optimization results.

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