Energy efficiency optimization method for active ris assisted uav mobile edge computing system

Abstract

The application discloses an energy efficiency optimization method of an active RIS assisted UAV mobile edge computing system, comprising the following steps: constructing an active RIS assisted UAV mobile edge computing system model; constructing a communication model of the active RIS assisted UAV mobile edge computing system; constructing an energy consumption model of the active RIS assisted UAV mobile edge computing system; establishing a multivariate joint optimization model with the maximum system energy efficiency as a target; and solving the target optimization model by using a convex optimization method; and finally obtaining the maximum system energy efficiency by means of active RIS and joint optimization resource allocation, so that the performance of the unmanned aerial vehicle network system is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless communication, and particularly relates to an energy efficiency optimization method of an active RIS assisted UAV mobile edge computing system. BACKGROUND

[0002] Mobile Edge Computing (MEC) has become an effective solution to help Ground Termianl (GT) devices handle computationally intensive and delay-sensitive tasks. Compared with the traditional cloud computing model, the advantage of MEC is that by deploying edge servers on the edge side of the network topology, such as in the base station or access point (AP) near the mobile Internet of Things device, GT devices can offload their computing tasks to these edge servers, which effectively reduces the computing burden of GT devices.

[0003] The servers of the ground MEC network are usually fixed deployment and are easily affected by obstacles in the urban environment, making it difficult to adapt to dynamic and heterogeneous environments and unable to quickly and reliably support hotspots and temporary computing. The combination of unmanned aerial vehicles (UAV) and MEC technology can provide more flexible computing offloading services.

[0004] Although the UAV assisted MEC network solves many problems of the traditional MEC network, it still faces complex and time-varying fading channels. This can cause unstable signal quality of the UAV, resulting in reduced signal-to-noise ratio, increased bit error rate, signal strength fluctuations, data transmission delays and packet loss problems. In addition, the endurance of the unmanned aerial vehicle is limited by its flight energy consumption, and it cannot support long-time hovering or long-distance maneuvering. Reconfigurable Intelligent Surface (RIS) can dynamically control the amplitude, phase, frequency and other characteristics of electromagnetic waves, thereby controlling the propagation behavior of electromagnetic waves in free space, breaking through the limitations of traditional wireless channels that cannot be actively regulated. Integrating RIS into the UAV-MEC system is a win-win strategy that can greatly improve the wireless transmission environment and increase the received signal power, thereby improving the energy efficiency of the unmanned aerial vehicle system.

[0005] At present, most of the researches on RIS unmanned aerial vehicle (UAV) mobile edge computing systems use a large number of low-cost passive reflecting elements, and most of the RISs are fixed on buildings to assist the uplink. However, the traditional passive RIS (PRIS) is difficult to overcome the double fading problem in the RIS-assisted cascade link. In contrast, the active RIS (ARIS) solves the double fading problem by adjusting the signal phase and amplifying the signal amplitude, thereby further improving the channel quality. SUMMARY

[0006] To solve the problems existing in the prior art, the present application provides an energy efficiency optimization method for an active RIS-assisted UAV mobile edge computing system. The method combines the high flexibility of the UAV and the characteristic that the active RIS can greatly improve the communication channel quality compared with the passive RIS, and optimizes the receiving beam vector, the offloading amount, the active RIS reflection coefficient, the UAV flight trajectory and the flight speed at the MEC to achieve the goal of maximizing the system energy efficiency.

[0007] An energy efficiency optimization method for an active RIS-assisted UAV mobile edge computing system, comprising the following steps:

[0008] S1: establishing a system model, a communication model and an energy consumption model according to the ground terminal device, the active RIS and the UAV operating parameters;

[0009] S2: constructing an optimization problem with the goal of maximizing the system energy efficiency according to the total amount of tasks completed by the ground terminal device and the MEC server and the weighted total energy consumption of the system.

[0010] S3: optimizing and solving the maximum energy efficiency function by using the linear least mean square error estimation, the continuous convex approximation method and the Dinkelbach algorithm, and obtaining the local optimal solution of the original problem through continuous iteration to achieve the maximum system energy efficiency.

[0011] Further, the system model, the communication model and the energy consumption model in S1 specifically include:

[0012] The system model includes that the system is composed of one ground MEC server, one UAV and K ground terminal devices in the region. The set {1, 2, …, K} represents all ground terminal devices. The UAV is equipped with an active RIS, which helps the ground terminal devices to forward tasks to the ground MEC server for calculation as a relay.

[0013] It is assumed that the K ground terminal devices and the MEC server are located on the ground and have fixed positions, i.e. the height is 0, and the horizontal coordinates of the kth ground terminal device are represented as r k =[x k ,yk ] T , the horizontal coordinates of the ground MEC server are denoted as r e = [x e , y e ] T , it is assumed that the UAV-ARIS keeps flying at a fixed height H and its horizontal position keeps unchanged. The flight time is evenly divided into T time slots, and the length of each time slot is δ t , thus, at time slot t, the position and flight velocity of the UAV-ARIS are denoted as q[t] = [x[t], y[t]] T , v[t] = [v x [t], v y [t]] T , From which the following flight constraints can be obtained:

[0014] q[1] = q0, q[T+1] = q F

[0015] ||v[t]||≤v max

[0016]

[0017] where v max denotes the maximum flight velocity of the UAV, q0, q F denote the starting and ending positions of the flight, respectively. The distance from the kth ground terminal device to the ARIS at time slot t can be denoted as The distance from the ARIS to the MEC server at time slot t can be denoted as Since the UAV has a good line-of-sight path, the channel from the kth ground terminal device to the ARIS at time slot t can be denoted as where β denotes the path loss per unit distance, a k [t] denotes the steering vector. Similarly, the channel from the ARIS to the MEC at time slot t can be denoted as where a M [t] and a R [t] denote the receiving and transmitting steering vectors of the MEC and the ARIS, respectively.

[0018] The communication model includes: assuming that the ARIS is configured with N elements, the ground MEC server is configured with M receiving antennas, and the K ground terminal devices are all configured with a single antenna, the channel from the ARIS to the ground edge server at time slot t can be denoted as The channel from the kth ground terminal device to the ARIS at time slot t is denoted as

[0019] Consider the ground terminal devices perform task offloading in a TDMA manner, and each ground terminal device is allocated an equal-time sub-slot, i.e. Assume the transmit power of the kth ground terminal device is P k , and the transmit symbol is s k , then the received signal at the MEC is represented as

[0020] Where β k,n [t] and φ k,n [t] ∈ [0, 2π) represent the reflection amplitude and phase of the kth ground terminal device at the tth time slot to the nth reflecting element of the ARIS; s k [t] represents the transmit symbol of the kth ground terminal device at the tth time slot, and represent the thermal noise at the ARIS and MEC receiving ends, respectively, I M and I N represent the MxM and NxN identity matrices, respectively, and represent the Gaussian white noise power at the ARIS and MEC receiving ends, respectively.

[0021] Assume the receive beam vector of the kth ground terminal device at the tth time slot at the MEC is The signal recovered at the MEC server by the kth ground terminal device at the tth time slot is represented as The rate of the kth ground terminal device at the tth time slot can be represented as:

[0022]

[0023] Where B represents the transmission bandwidth.

[0024] This paper considers partial offloading, i.e. the computing task of the kth ground terminal device at the tth time slot is divided into two parts, represented as and and satisfy Where is the amount of local computation, is the amount of offloaded computation, l k [t] is the total amount of computation of the kth ground terminal device, and the amount of local computation should satisfy the following conditions

[0025]

[0026] Where b represents the number of CPU cycles required to process 1 bit of task, f L,kdenotes the maximum computation capability of the kth ground terminal device. The constraint of offloading amount can be expressed as

[0027]

[0028] The above constraint indicates that the offloading amount cannot exceed the product of the maximum rate and the allocated time.

[0029] The energy consumption model includes: in the UAV-ARIS assisted MEC communication system, the total energy consumption is mainly composed of the energy consumption of ground terminal device local computation and offloading tasks, the computing energy consumption of MEC server, the flight energy consumption of UAV and the ARIS energy consumption. The energy consumption of all ground terminal devices in the tth time slot can be modeled as

[0030]

[0031] Wherein denotes the switched capacitance coefficient of the ground terminal device. The computing energy consumption of the MEC server in the tth time slot can be expressed as

[0032]

[0033] Wherein denotes the switched capacitance coefficient of the MEC server. As for the flight energy consumption of the UAV, a new energy consumption model of the rotor UAV is adopted, which considers the actual thrust-weight ratio, as follows:

[0034]

[0035] Wherein P0 is the blade power in the hovering state, U tip is the blade angular velocity, d o is the fuselage drag ratio, p is the air density, s is the rotor volume, A is the rotor area, P i is the induced power in the hovering state, and v0 is the induced velocity when the rotor flies forward.

[0036] Since the ARIS is an active unit, it consumes energy, and for the nth unit, its power consumption is modeled as

[0037] P ARIS,n [t]=μp out,n [t]+P c +P DC

[0038] Wherein p denotes the amplifier efficiency, P c and P DC denote the static circuit power consumption and the direct current bias power consumption part respectively, p out,n[t] represents the output power of the nth unit, which is related to the incident signal power at the tth time slot, and can be expressed as

[0039]

[0040] wherein g k,n [t] represents the channel from the kth user ground terminal device to the nth reflecting unit of ARIS in the tth time slot. Therefore, the energy consumption of ARIS in the tth time slot can be expressed as

[0041]

[0042] Since the energy consumption of the UAV is much larger than the offloading energy consumption, the following weighted energy consumption is considered as follows:

[0043]

[0044] wherein a is a weighting coefficient.

[0045] Further, the optimization problem in S2 for maximizing the system energy efficiency specifically includes: by jointly optimizing the UAV trajectory Q, the UAV flight speed v, the offloading amount l, the ARIS reflection coefficient Θ and the MEC receiving beam vector w, the energy efficiency of the system is maximized as follows:

[0046]

[0047] C4: q1=q0, q[T+1]=q F

[0048] C5: ||v[t]||≤v max

[0049]

[0050] wherein C1 represents the maximum task amount constraint of local calculation, C2 represents the maximum offloadable task amount constraint, C3 represents that the total amount of tasks of all time slots of each ground terminal device should be greater than a certain threshold, C4 represents the initial position and ending position of the UAV flight, C5 represents that the flight speed of the UAV cannot exceed the specified maximum flight speed, and C6 represents the position change in the UAV flight process.

[0051] Furthermore, the process of optimizing the maximum energy efficiency function using linear minimum mean square error estimation, continuous convex approximation and Dinkelbach algorithm includes: optimizing the receiving beam vector w at the MEC, optimizing the unloading amount l, optimizing the ARIS reflection coefficient Θ, optimizing the UAV flight trajectory Q and flight speed v, solving these sub-problems, and obtaining the local optimal solution of the original problem by continuously iterating the solutions to the sub-problems, ultimately achieving the goal of maximizing the system energy efficiency.

[0052] Beneficial effects of the present invention:

[0053] 1. This invention considers the double fading problem of fixed RIS in complex urban communication environments, where the coverage area of ​​ground terminals is narrow and passive RIS cannot overcome it. It dynamically combines UAV, MEC server and active RIS, greatly improving the quality of the communication channel.

[0054] 2. The present invention considers the impact of active RIS energy consumption on the objective function. By rationally optimizing the MEC receiving beam vector, offloading amount, active RIS reflection coefficient, UAV flight trajectory and flight speed, and continuously iterating the solutions to the sub-problems, the local optimal solution of the original problem is obtained, ultimately achieving the goal of maximizing the system energy efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 Schematic diagram of the basic flow of the energy efficiency optimization method of the active RIS-assisted UAV mobile edge computing system in the present invention;

[0056] Figure 2 1 is a system model diagram of the mobile edge computing system of the active RIS assisted UAV in the present invention;

[0057] Figure 3 This is a graph showing the trajectory changes of the drone under different schemes of the present invention;

[0058] Figure 4 It is the iterative convergence diagram of the energy efficiency of the active RIS-assisted UAV-MEC system under different schemes of the present invention. DETAILED DESCRIPTION

[0059] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are intended only to illustrate the present invention and are not intended to limit the scope of the present invention. It should be noted that the terms "front," "rear," "left," "right," "up," and "down" used in the following description refer to directions in the accompanying drawings, and the terms "inward" and "outward" refer to directions toward or away from the geometric center of a particular component, respectively.

[0060] Figure 1is a basic flow diagram of an energy efficiency optimization method of an active RIS assisted UAV mobile edge computing system in the application, comprising the following steps:

[0061] S1: establishing a system model, a communication model and an energy consumption model according to the ground terminal device, the active RIS and the UAV operation parameters;

[0062] S2: constructing an optimization problem with the maximum system energy efficiency as the target according to the total amount of tasks completed by the ground terminal device and the MEC server and the weighted total energy consumption of the system.

[0063] S3: using linear least mean square error estimation, continuous convex approximation method and Dingkelbach algorithm to optimize and solve the maximum energy efficiency function, and obtaining the local optimal solution of the original problem through continuous iteration to realize the maximum system energy efficiency.

[0064] As shown in Figure 2 , further, the system model, the communication model and the energy consumption model in S1 specifically comprise:

[0065] The system model comprises: the system is composed of one ground MEC server, one UAV and K ground terminal devices in the region. The set {1, 2,..., K} represents all ground terminal devices. The UAV is equipped with an active RIS, which helps the ground terminal device to forward the task to the ground MEC server for calculation as a relay.

[0066] It is assumed that the K ground terminal devices and the MEC server are located on the ground and fixed, i.e. the height is 0, and the horizontal coordinates of the kth ground terminal device are represented as r k =[x k ,y k ] T , the horizontal coordinates of the ground MEC server are represented as r e =[x e ,y e ] T , it is assumed that the UAV-ARIS keeps flying at a fixed height H, and its horizontal position keeps unchanged. The flight time is evenly divided into T time slots, and the time length of each time slot is δ t , therefore, at time slot t, the position and flight speed of the UAV-ARIS are represented as q[t]=[x[t],y[t]] T ,v[t]=[v x [t],v y [t]] T , From this, the following flight constraints can be obtained:

[0067] q[1]=q0,q[T+1]=q F

[0068] ||v[t]||≤v max

[0069]

[0070] where v max denotes the maximum flight speed of UAV, q0,q F denote the start and end positions of flight, respectively. The distance from the kth ground terminal device to ARIS at the tth time slot can be expressed as The distance from ARIS to MEC server at the tth time slot can be expressed as Since UAV has a good line-of-sight path, considering the pure LoS path, the channel from the kth ground terminal device to ARIS at the tth time slot can be expressed as

[0071] where β denotes the path loss per unit distance, a k [t] denotes the steering vector. Similarly, the channel from ARIS to MEC at the tth time slot can be expressed as where a M [t] and a R [t] denote the receiving and transmitting steering vectors of MEC and ARIS, respectively.

[0072] The communication model includes: assuming that ARIS is configured with N units, the ground MEC server is configured with M receiving antennas, and the K ground terminal devices are all configured with single antennas. The channel from ARIS to the ground MEC server at the tth time slot can be expressed as The channel from the kth ground terminal device to ARIS at the tth time slot is expressed as

[0073] Considering that the ground terminal device performs task offloading in the TDMA manner, and each ground terminal device is allocated an equal time sub-slot, i.e. Assuming that the kth ground terminal device has a transmission power of P k , and a transmission symbol of s k , the received signal at MEC can be expressed as

[0074] where β k,n [t] and φ k,n [t]∈[0, 2π) denote the reflection amplitude and phase of the kth ground terminal device at the tth time slot to the nth reflecting unit of ARIS, respectively. s k [t] denotes the transmission symbol of the kth ground terminal device at the tth time slot, and and I M and I N denote the identity matrix of M x M and N x N, respectively, and denote the Gaussian white noise power at the ARIS and MEC receiver, respectively.

[0075] Let the receive beam vector of the kth ground terminal device at the tth time slot at the MEC be The signal recovered at the MEC server for the kth ground terminal device at the tth time slot is denoted as The rate of the kth ground terminal device at the tth time slot can be denoted as:

[0076]

[0077] where B denotes the transmission bandwidth.

[0078] In this paper, partial offloading is considered, i.e., the computing task of the kth ground terminal device at the tth time slot is divided into two parts, denoted as and and satisfy where is the computation amount of local computing, is the offloaded computation amount, l k [t] is the total computation amount of the kth ground terminal device, and the computation amount of local computing should satisfy the following condition

[0079]

[0080] where b denotes the number of CPU cycles required to process 1 bit task, f L,k denotes the maximum computing capacity of the kth ground terminal device. The constraint of offloaded amount can be expressed as

[0081]

[0082] The above constraint means that the offloaded amount cannot exceed the product of the maximum rate and the allocated time.

[0083] The energy consumption model includes: in the UAV-ARIS assisted MEC communication system, the total energy consumption is mainly composed of the energy consumption of the local computing and offloaded tasks of the ground terminal device, the computing energy consumption of the MEC server, the flight energy consumption of the UAV and the energy consumption of the ARIS. The energy consumption of all ground terminal devices at the tth time slot can be modeled as

[0084]

[0085] where denotes the switched-capacitor coefficient of the ground terminal device. The energy consumption of the MEC server at the t-th time slot can be denoted as

[0086]

[0087] where denotes the switched-capacitor coefficient of the MEC server. As for the flight energy consumption of the UAV, a novel energy consumption model of the rotor UAV is adopted, which considers the actual thrust-to-weight ratio, as follows:

[0088]

[0089] where P0is the blade power in the hovering state, U tip is the blade angular velocity, d o is the fuselage drag ratio, p is the air density, s is the rotor volume, A is the rotor area, P i is the induced power in the hovering state, and v0is the induced velocity of the rotor when flying forward.

[0090] Since ARIS is an active unit, it consumes energy, and for the n-th unit, its power consumption can be modeled as follows

[0091] P ARIS,n [t] = μP out,n [t] + P c + P DC

[0092] where μ denotes the amplifier efficiency, P c and P DC denote the static circuit power consumption and the DC bias power consumption part, respectively, p out,n [t] denotes the output power of the n-th unit, which is related to the incident signal power at the t-th time slot, and can be denoted as

[0093]

[0094] where denotes the channel from the k-th ground terminal device at the t-th time slot to the n-th reflecting unit of ARIS. Therefore, the energy consumption of ARIS at the t-th time slot can be denoted as

[0095]

[0096] Since the flight energy consumption of the UAV is much greater than the offloading energy consumption, the following weighted energy consumption is considered as follows:

[0097]

[0098] where a is the weighting coefficient.

[0099] Further, the optimization problem in S2 aiming to maximize the system energy efficiency specifically comprises: by jointly optimizing the UAV trajectory Q, the UAV flight speed v, the offloading amount l, the ARIS reflection coefficient Θ and the MEC receiving beam vector w, the energy efficiency of the system is maximized as follows:

[0100]

[0101] C4: q1=q0, q[T+1]=q F

[0102] C5: ||v[t]||<=v max

[0103]

[0104] Wherein, C1 represents the maximum task amount constraint of local calculation, C2 represents the maximum offloadable task amount constraint, C3 represents that the task amount sum of all time slots of each terminal should be greater than a certain threshold, C4 represents the initial position and end position of the UAV flight, C5 represents that the flight speed of the UAV cannot exceed the specified maximum flight speed, and C6 represents the position change in the UAV flight process.

[0105] Due to the close coupling and non-convexity between variables in the optimization problem, it is difficult to directly solve. In order to solve this problem, the application adopts linear minimum mean square error estimation, continuous convex approximation method and Dingkelbach algorithm to decouple the complex non-convex optimization problem into four sub-problems easy to solve, then uses CVX to solve, and carries out alternating optimization, and finally the local optimal solution of the original problem can be obtained.

[0106] The specific calculation process comprises:

[0107] 1. Optimize the receiving beam vector w at the MEC

[0108] For the objective function of the optimization problem P1, only the ground terminal device transmission rate part is related to the receiving beam, therefore, the energy efficiency is maximized, that is, the receiving rate of the MEC is maximized, for the TDMA access mode, the maximum reachable rate of the kth ground terminal device in the tth time slot can be expressed as

[0109]

[0110] It has been proved that the linear minimum-mean-square error (LMMSE) detector is the optimal receiving beam vector, and the normalized receiving beam based on LMMSE can be expressed as

[0111]

[0112] where

[0113] 2. Optimization of the offloading amount l

[0114] According to the optimization problem P1, the sub-problem of optimizing the offloading amount is as follows:

[0115]

[0116] By using the Dinkelbach algorithm, an auxiliary variable ξ is introduced to represent the optimal objective function value of the last iteration, and the term related to the offloading amount is retained, so that the objective function of the above optimization problem can be transformed as follows:

[0117]

[0118] It can be observed that the objective function is a linear function with respect to the offloading amount, and the constraint condition is a linear constraint with respect to the offloading amount, so it can be directly solved, where the update of the auxiliary variable ξ is as follows:

[0119]

[0120] 3. Optimization of the ARIS reflection coefficient Θ

[0121] According to the optimization problem P1, the sub-problem of optimizing the reflection coefficient is as follows:

[0122]

[0123] According to the energy consumption expression of the ARIS in the tth time slot, the objective function

[0124]

[0125] It can be observed that the objective function is independent of the phase of the ARIS, so it is only necessary to optimize the phase of the ARIS to maximize the transmission rate, that is,

[0126]

[0127] Since It is only necessary to consider adjusting the phase of the ARIS to align the cascaded channel, that is,

[0128]

[0129] Based on the above results, the rate of the kth user in the tth time slot can be expressed as

[0130]

[0131] After a simple transformation, the following can be obtained:

[0132]

[0133] The above constraints are transformed into convex constraints using SCA as follows:

[0134]

[0135] The transformed optimization problem is shown as follows:

[0136]

[0137] This optimization problem is a convex optimization problem and can be solved using CVX.

[0138] 4. Optimize the UAV flight trajectory Q and flight speed v

[0139] According to the optimization problem P1, the sub-problems of the optimized flight trajectory and flight speed are obtained as follows:

[0140]

[0141] C2: q1 = q0, q[T+1] = q F

[0142] C3: ||v[t]||≤v max

[0143]

[0144] First, for the UAV flight energy consumption part, the inequality Therefore, the upper bound of the UAV flight energy consumption can be obtained as follows

[0145]

[0146] It can be seen that this part is a convex function of speed v, and for the objective function part, since Define Then

[0147]

[0148] Introduce auxiliary variable ||q[t]-r k || 2 +H 2 ≥ω k [t], then the above equation can be transformed as

[0149]

[0150] Therefore, the objective function is transformed as

[0151]

[0152] The formula is a joint convex function of omega and speed v. Further constraint ||q[t]-r k || 2 +H 2 ≥omega k [t] is converted into a convex constraint as follows:

[0153]

[0154] For the ground terminal device rate part

[0155]

[0156] Wherein For the above function, an auxiliary variable is introduced By can be transformed into the following form by SCA:

[0157]

[0158] At this time, the above formula is a linear function. As can be known, the sub-problem is converted into the following form

[0159]

[0160] C5: q1=q0, q[T+1]=q F

[0161] C6: ||v[t]||<=v max

[0162]

[0163] The above optimization problem is a convex optimization problem about the UAV trajectory and speed, and can be solved by CVX.

[0164] The application will be further described below in combination with simulation conditions and results, and the simulation parameters are as follows:

[0165] The total bandwidth of the channel is 1MHz, the number of ground terminal devices is 4, the flight height of the UAV is 50m, the time period is 50s, which is divided into 50 time slots, the maximum flight speed of the UAV is 50m / s, the initial position and the final position of the UAV are [0, 200]m and [0, -200]m, and the transmission power of the ground terminal device is 500mW. The computing capacity of the ground terminal device locally and the computing capacity of the MEC end are 5x10 8 Hz and 10 10 Hz respectively, and the effective capacitance coefficients are 1x10 -27 and 1x10 -28, the CPU cycles required for processing the unit bit task is 1*10 3 The MEC receiving end noise power and the ARIS noise power are -100dBm and -60dBm respectively, and the weight of the flight energy consumption of the unmanned aerial vehicle is 0.0001.

[0166] Figure 3 The unmanned aerial vehicle trajectories under the proposed algorithm are compared under the conditions of 4 ground terminal devices, a period T=50s, random RIS phase shift and fixed UAV trajectory.

[0167] Figure 4 The iterative convergence diagram of the energy efficiency of the active RIS assisted UAV-MEC system under different schemes is shown, and it can be seen from the diagram that the energy efficiency of the unmanned aerial vehicle system rapidly increases at the beginning and then converges after about 6-7 iterations, and the proposed algorithm can achieve higher energy efficiency compared with the other two benchmark schemes.

[0168] The technical means disclosed in the scheme of the application are not limited to the technical means disclosed in the above-mentioned embodiments, and also include technical solutions composed of any combination of the above technical features.

Claims

1. An energy efficiency optimization method for an active RIS-assisted UAV mobile edge computing system, characterized in that: The specific steps include: S1: Establish a system model, a communication model, and an energy consumption model based on the ground terminal equipment, the active RIS, and the UAV operating parameters. The system model established in S1 specifically includes: The system model includes a ground MEC server, a drone, and K ground terminal devices in the area. The set {1, 2, …, K} represents all ground terminal devices. The drone is equipped with an active RIS, which acts as a relay to help ground terminal devices forward tasks to the ground MEC server for computation. Assumptions The ground terminal devices and MEC servers are all located on the ground and have fixed positions, that is, the height is 0. The horizontal coordinates of a ground terminal device are expressed as , the horizontal coordinates of the MEC server are expressed as , assuming that UAV-ARIS maintains a fixed altitude flying, its horizontal position remains unchanged; the flight time is evenly divided into time slots, and the duration of each time slot is , so in the time slot The position and flight speed of UAV-ARIS are expressed as , from which we can get the following flight constraints: in Indicates the maximum flight speed of the UAV, Respectively represent the starting and ending positions of the flight; The ground terminal equipment to ARIS in the first The distance of time slots is expressed as , ARIS and MEC servers in the The distance between time slots can be expressed as ; Since the UAV has a good line-of-sight path, a pure LoS path is considered. The ground terminal equipment to ARIS The time slot channel can be expressed as ,in represents the path loss per unit distance, Represents the steering vector; similarly, ARIS to MEC The channel of time slots is represented as ,in and denote the receiving and sending steering vectors of MEC and ARIS respectively; The communication model established in S1 includes: assuming that ARIS is configured with units, the ground MEC server is configured with Root receiving antenna, Each ground terminal device is equipped with a single antenna, and the ARIS to ground MEC server is The channel of time slots is represented as , No. The first ground terminal device to ARIS The time slot channel is represented as ; Consider that the ground terminal equipment performs task offloading in a TDMA manner, and each ground terminal equipment is allocated equal-time sub-slots, that is, ; Assume that The transmission power of a ground terminal device is , the emission symbol is , then the received signal at MEC is expressed as ； in , and Respectively represent Ground terminal equipment time slot to ARIS The reflection amplitude and phase of each reflection unit; Indicates the The ground terminal equipment is The transmitted symbols of time slots are and denote the thermal noise at the ARIS and MEC receivers, respectively. and Respectively and The identity matrix, and denote the Gaussian white noise power at the receiving end of ARIS and MEC respectively; Assume that the MEC is Ground terminal equipment The receiving beam vector of the time slot is , No. Ground terminal equipment The signal recovered on the MEC server for each time slot is expressed as , then Ground terminal equipment The rate of a time slot is expressed as: ;in Indicates transmission bandwidth; Consider partial uninstallation, The ground terminal equipment is The computational task of each time slot is divided into two parts, which are represented as as well as , and satisfies ,in is the computational effort of local computing, is to offload the computation, It is The total computing capacity of the ground terminal equipment and the local computing capacity should meet the following conditions ;in Indicates the number of CPU cycles required to process 1 bit task, Indicates the The maximum computing capacity of the ground terminal equipment; the constraint of the offloading amount is expressed as ;The above constraint means that the amount of unloading cannot exceed the product of the maximum rate and the allocated time; The energy consumption model established in S1 includes that in the UAV-ARIS assisted MEC communication system, the total energy consumption is mainly composed of the energy consumption of local computing and offloading tasks of ground terminal equipment, the computing energy consumption of MEC server, the flight energy consumption of UAV and the energy consumption of ARIS; The energy consumption of all ground terminal devices in a time slot is modeled as ;in Indicates the switching capacitance coefficient of the ground terminal equipment; MEC server is in the The energy consumption of each time slot is expressed as ;in represents the switch capacitance coefficient of the MEC server. As for the flight energy consumption of the UAV, a new energy consumption model for rotary-wing UAVs is adopted, which takes into account the actual thrust-to-weight ratio, as shown below: ;in is the blade power in hovering state, is the blade angular velocity, is the fuselage drag ratio, is the air density, is the rotor volume, is the rotor area, is the induced power in the hovering state, is the induced speed of the rotor when flying forward; Since ARIS is an active unit, it consumes energy. The power consumption of each unit is modeled as follows: ;in represents the amplifier efficiency, and Represent the static circuit power consumption and DC bias power consumption respectively, Indicates the The output power of each unit is The incident signal power of each time slot is related to the ;in , Indicates the Ground terminal equipment time slot to ARIS channel of a reflection unit; therefore, The energy consumption of ARIS in each time slot is expressed as ; Since the flight energy consumption of the drone is much greater than the unloading energy consumption, the following weighted energy consumption is considered as follows: ;in is the weighting coefficient; S2: Based on the total number of tasks completed by ground terminal devices and MEC servers and the system's weighted total energy consumption, an optimization problem is constructed with the goal of maximizing the system's energy efficiency. S3: The maximum energy efficiency function is optimized and solved using linear minimum mean square error estimation, continuous convex approximation, and the Dinkelbach algorithm. The local optimal solution of the original problem is obtained through continuous iteration to achieve maximum system energy efficiency.

2. The energy efficiency optimization method of an active RIS-assisted UAV mobile edge computing system according to claim 1 is characterized in that: The optimization problem constructed in S2 with the goal of maximizing the system energy efficiency specifically includes: , UAV flight speed , uninstall volume , ARIS reflection coefficient and the MEC receive beam vector , to maximize the energy efficiency of the system, as follows: ; Among them, C1 represents the maximum task volume constraint of local calculation, C2 represents the maximum offloadable task volume constraint, C3 represents that the sum of the task volume of all time slots of each ground terminal device should be greater than a certain threshold, C4 represents the initial and end positions of the UAV flight, C5 represents that the UAV flight speed cannot exceed the specified maximum flight speed, and C6 represents the position change of the UAV during the flight.

3. The energy efficiency optimization method of an active RIS-assisted UAV mobile edge computing system according to claim 1 is characterized in that: S3 uses linear minimum mean square error estimation, continuous convex approximation and Dinkelbach algorithm to optimize the maximum energy efficiency function. The process includes: optimizing the receiving beam vector at MEC , optimize uninstall volume , optimize the ARIS reflection coefficient , optimize UAV flight trajectory and flight speed , solve these sub-problems, and obtain the local optimal solution of the original problem by continuously iterating the solutions to the sub-problems, thereby achieving maximum system energy efficiency.

Images