A method for improving energy efficiency of real-time monitoring in unmanned areas for 6G networks

Abstract

The present invention provides a method for improving energy efficiency in real-time monitoring of unmanned areas for 6G networks. It is a method for improving energy efficiency of cells under the background of real-time monitoring of unmanned areas, and belongs to the field of energy efficiency optimization in unmanned aerial vehicle real-time monitoring networks. In order to meet the "ubiquitous coverage" requirements of 6G, the present invention proposes to use the flexibility of unmanned aerial vehicles to deploy to unmanned areas and monitor in real time. The unmanned aerial vehicles hover over the unmanned areas in a circular trajectory and communicate with the equipment in real time. Under the call for global emission reduction, the present invention aims to maximize the amount of information that can be transmitted per joule of energy, that is, energy efficiency, by collaboratively optimizing the flight center point, flight radius and power distribution of the unmanned aerial vehicle to improve the energy efficiency of the cell and achieve green three-dimensional coverage.

Description

Technical Field

[0001] The present invention belongs to the field of energy efficiency optimization in real-time monitoring networks of unmanned aerial vehicles, and relates to a design scheme for improving energy efficiency when unmanned aerial vehicles serve communities. Specifically, a unmanned aerial vehicle hovers over a community in a circular trajectory to provide real-time services to users in the community. By optimizing the flight center point, flight radius and transmission power of the unmanned aerial vehicle, the energy efficiency of the entire community is maximized, thereby achieving efficient, green and three-dimensional coverage. Background Art

[0002] 5G has entered a large-scale commercial state. In order to further improve communication performance and meet future communication needs, the research of 6G has been put on the agenda. Although 5G has made life smart in many aspects, promoted the construction of smart cities, and made travel, medical care, work and other aspects more convenient, its application in unmanned areas is not satisfactory. 6G proposes that by 2030+, "ubiquitous coverage" will become the main form of the network, complete the network deployment in "unmanned areas" such as deep mountains, deep seas, and deserts, achieve full coverage of the air, land, and sea, and promote the convenience, refinement and intelligence of social governance. At the same time, Zhifu Agriculture and Zhifu Industry also proposed the concept of "unmanned", real-time detection of equipment, collection and analysis of data, and maximization of industrial benefits. However, if a base station is built to monitor an unmanned area, supporting signal towers, power supply equipment, etc. are indispensable, and personnel are required to go for regular maintenance and repair. Moreover, once the monitoring area changes or is damaged by natural disasters, the base station can no longer be used. Obviously, this investment will cost a lot of manpower and financial resources, but the benefits obtained are not considerable. In this scenario, drone base stations have obvious advantages over ordinary base stations and are more adaptable to environmental changes. Therefore, drone base stations can be deployed in uninhabited areas for real-time monitoring.

[0003] With their high mobility, drones have been used in many fields, such as military, transportation and agriculture. In recent years, the application of drones in the field of wireless communications has also received increasing attention. First, drones can assist ground cellular networks. In areas with dense hotspots, drones can quickly divert business and reduce the pressure on ground base stations by relying on their flexibility and easy deployment. Then, drones can also be used as mobile relays, which are deployed according to the mission. Compared with traditional fixed relays, they are more flexible, can optimize deployment locations, and improve communication performance. Finally, drones can carry base stations and be deployed as aerial base stations in areas without ground base stations to provide communication connections, which is also the main aspect of the present invention.

[0004] As the number of access devices continues to increase, base stations and supporting facilities also grow accordingly, which not only brings a surge in data traffic, but also causes a sharp increase in the energy consumption of the entire network. Therefore, the concept of "bit / joule" to measure system energy efficiency has become an important consideration in the research of 6G. In the context of global advocacy to reduce carbon emissions, the development of mobile communications should not only pursue speed and capacity, but also contribute to sustainable development. Therefore, this invention is committed to the research on energy efficiency improvement in real-time monitoring of unmanned areas, and the flight trajectory and power distribution of drones are reasonably designed. Summary of the invention

[0005] The present invention provides a method for improving the energy efficiency of a cell in the context of real-time monitoring in an unmanned area. 6G proposes that by 2030+, "ubiquitous coverage" will become the main form of the network, completing the network deployment in "unmanned areas" and achieving full coverage of the air, land, and sea. In areas lacking infrastructure, air-based access points can be used for real-time monitoring, and drones equipped with base stations hover over the monitoring area to achieve three-dimensional, wide-area, and mobile coverage. The specific solution is as follows: Figure 1 Based on this model, the present invention proposes a scheme for optimizing the flight center point, flight radius and power distribution of the UAV, thereby improving the energy efficiency of the system and achieving green three-dimensional coverage.

[0006] The technical solution adopted by the present invention to solve the technical problem is as follows:

[0007] A method for improving energy efficiency of real-time monitoring in unmanned areas for 6G scenarios includes the following steps:

[0008] The first step is to build a system model:

[0009] 1) Without loss of generality, assume that there are K access devices in an unmanned area that needs real-time monitoring. The devices are three-dimensionally distributed on the ground and in the air at a distance of 0-300m. A drone carrying a base station flies in a circular trajectory at an altitude of H. T is the flight period. The drone is required to communicate with K devices in real time and meet the rate threshold. By collaboratively optimizing the flight center point, flight radius, and power allocation of the drone, the energy efficiency of the cell is effectively improved.

[0010] 2) In the system model, bold capital letters represent matrices, bold lowercase letters represent vectors, and non-bold letters represent scalars; ||A|| represents the Frobenius norm of matrix A, and ||a|| represents the Euclidean norm of vector a;

[0011] The energy efficiency of the system is defined as the amount of information that can be transmitted per joule. Without loss of generality, the present invention takes a flight cycle of a drone as an example. The amount of information transmitted in one cycle compared with the energy consumed is the energy efficiency, which can be expressed as:

[0012]

[0013] Where I is the amount of information transmitted in one cycle, and E is the energy consumed in one cycle.

[0014] The energy consumption of a drone is mainly composed of three parts, namely propulsion power, transmission power and fixed power consumption of the base station. The energy consumed by a drone in a flight cycle can be expressed as:

[0015] E=(P p +P t +P b )×T, (2)

[0016] Where P p represents propulsion power, P t Indicates the transmit power, P b represents the fixed power consumption of the base station, and T is a flight cycle of the drone. The drone flies in a circular trajectory, and the propulsion energy consumption in one cycle is E p It is related to flight speed, acceleration, and drone parameters and can be expressed as:

[0017]

[0018] Among them, C1 and C2 are the parameters of the UAV, V and a cen are the flight speed and acceleration of the drone, and V = 2πr / T, a cen =4π 2 r / T 2 , where r is the flight radius. Then the energy consumption of the drone in one cycle can be written as:

[0019]

[0020] In order to study the amount of information transmitted in a cycle, the present invention divides a cycle into N time slots, and the duration of each time slot is τ = T / N. When the time slot is very small, the position of the drone can be considered unchanged in each time slot. Since the drone flies at a high altitude, it is easy to establish a LoS (line-of-sight) channel with the user. In the nth time slot, the channel between the drone and the kth device can be expressed as

[0021]

[0022] Where ρ is the unit path loss, q U [n]=(x E +rcosθ[n],y E +rsinθ[n],H) and q k =(x k ,y k ,zk ) are the position of the UAV in the nth time slot and the position of the kth device, respectively. (x E ,y E ) is the horizontal coordinate of the flight center point, where θ[n] = 2πn / N. Then the amount of information of the kth device in the nth time slot can be expressed as:

[0023]

[0024] Where B is the bandwidth allocated to each user, P k [n] is the power allocated to the kth user in the nth time slot, σ 2 is the noise power. The total amount of information transmitted in one cycle is:

[0025]

[0026] In summary, the energy efficiency expression in this system can be expressed more comprehensively as follows:

[0027]

[0028] where q U [n] and q k are the positions of the UAV and user k at the nth time slot, respectively, and V is the flight speed of the UAV.

[0029] In the second step, according to the specific settings of the first step, determine the objective function and list the optimization problem:

[0030] The purpose of this invention is to maximize energy efficiency, or EE, while meeting the user's rate limit, acceleration limit, transmission power limit, and drone trajectory limit. According to the optimization objectives and constraints, the optimization problem is listed as follows:

[0031]

[0032]

[0033] V min ≤V≤V max , (9c)

[0034] a cen ≤a max , (9d)

[0035]

[0036] Among them, R th is the rate threshold of a single user, V min and V max are the minimum flight speed and the maximum flight speed respectively, a maxMaximum acceleration, P max is the maximum transmit power.

[0037] The third step is to design an algorithm to solve the optimization problem:

[0038] First, considering the distribution of K devices, we discuss how to determine the flight center point. The flight altitude of the drone is fixed at H, and the flight center point, i.e. (x E ,y E ), which has a certain impact on the energy efficiency of the cell. When users are evenly distributed in space, in order to simplify the algorithm, the center point of the cell can be directly selected as the flight center point. When users are not evenly distributed, such as when users are distributed on one side of the cell, directly using the center point of the cell as the flight center point will cause the flight trajectory of the drone to not cover the users well, affecting the energy efficiency of the cell. The flight altitude of the drone and the distribution altitude of the equipment are fixed. Therefore, it is proposed to use the point with the shortest sum of horizontal distances to each user as the flight center point, that is, (x E ,y E ) satisfies the following relationship:

[0039]

[0040] Where (x, y) is any point on the plane.

[0041] When (x E ,y E ) is determined, the optimization problem (P1) is about the optimization problem of radius and power. For constraints (9c) and (9d), when the flight period T of the UAV is constant, since the UAV flies in a circular trajectory, V and a cen It is only related to the flight radius, and can be rewritten as a constraint on r:

[0042]

[0043]

[0044] Although (9e), (11) and (12) are all convex constraints, the optimization objective is a fraction and constraint (9b) is a non-convex constraint, which makes (P1) cannot be solved directly. The present invention uses the continuous convex approximation method to convert (P1) into a convex problem. First, the auxiliary variable γ is introduced. n,k , and satisfy:

[0045]

[0046] is equivalent to:

[0047]

[0048] However, (14) is still a non-convex constraint. Based on the idea of ​​continuous convex optimization, at the i+1th iteration, the right side of the inequality can be expanded by a first-order Taylor as follows:

[0049]

[0050] in is the value of the i-th iteration. Then problem (P1) can be converted to:

[0051]

[0052] Although the constraints in problem (P2) are all convex constraints, the optimization target is a fractional function, so (P2) is still difficult to solve directly. Based on the Dinkelbach algorithm, the present invention proposes the following solution. At the i+1th iteration, the amount of information transmitted in one cycle can be expressed as:

[0053]

[0054] By introducing the constant λ, the optimization problem can be transformed into:

[0055]

[0056] For problem (P3), the present invention proposes the following algorithm:

[0057] 1. Initialize the number of iterations i = 0, λ = 1 and

[0058] 2. Utilize Solving problem (P3) with λ yields

[0059] 3. Update

[0060] 4. Update i=i+1;

[0061] 5. If it does not converge, return to step 2. If it converges, proceed to step 6.

[0062] 6. Get r and P k [n], and based on the pre-determined center point (x E ,y E ) calculates the optimized energy efficiency.

[0063] The beneficial effects of the present invention are as follows: the present invention provides a solution for the real-time monitoring needs of 6G unmanned areas. Compared with traditional monitoring methods, it does not need to deploy a large number of ground equipment. The flexibility of drones is used to monitor unmanned areas. By reasonably designing the flight center point, flight trajectory and power allocation plan, the energy efficiency of the cell is maximized to achieve green and efficient three-dimensional coverage. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 A diagram of the system for drones to monitor unmanned areas in real time.

[0065] Figure 2 Energy efficiency comparison between the optimized solution and the baseline solution.

[0066] Figure 3 To optimize the scheme and the variation of energy efficiency with transmission power at different radii.

[0067] Figure 4 Energy efficiency, information volume, and energy consumption vary with the number of users. DETAILED DESCRIPTION

[0068] In order to better understand the above technical solution, the following is a detailed analysis in conjunction with the accompanying drawings and specific implementation methods. The specific steps of this embodiment include:

[0069] The first step is to build a system model:

[0070] 1) Without loss of generality, assume that the unmanned area to be monitored in real time is a cellular cell with a radius of 2 km, there are K = 200 access devices in the cell, the devices are three-dimensionally distributed on the ground and in the air at 0-300 m, the flight altitude of the drone is set to H = 1000 m, the flight period is set to T = 1000 s, and the drone is required to communicate with all access devices in real time and meet the rate threshold. By collaboratively optimizing the flight center point, flight radius and power allocation of the drone, the energy efficiency of the cell is effectively improved.

[0071] 2) In the system model, bold capital letters represent matrices, bold lowercase letters represent vectors, and non-bold letters represent scalars; ||A|| represents the Frobenius norm of matrix A, and ||a|| represents the Euclidean norm of vector a;

[0072] The energy efficiency of the system is defined as the amount of information that can be transmitted per joule. Without loss of generality, the present invention takes a flight cycle of a drone as an example. The amount of information transmitted in one cycle compared with the energy consumed is the energy efficiency, which can be expressed as:

[0073]

[0074] Where I is the amount of information transmitted in one cycle, and E is the energy consumed in one cycle.

[0075] The energy consumption of a drone is mainly composed of three parts, namely propulsion power, transmission power and fixed power consumption of the base station. The energy consumed by a drone in a flight cycle can be expressed as:

[0076] E=(Pp +P t +P b )×T, (20)

[0077] Where P p represents propulsion power, P t Indicates the transmit power, P b represents the fixed power consumption of the base station. The transmission power and the fixed power consumption of the base station are set to fixed values, which are set to P t =5W and P b = 80W. The energy consumption in one cycle is E p It is related to flight speed, acceleration, and drone parameters. Since the drone flies in a circular trajectory, E p It can be expressed as:

[0078]

[0079] Where C1 = 1.85 × 10 -3 , C2=4500 is the UAV parameter, V and a cen are the flight speed and acceleration of the drone, and V = 2πr / T, a cen =4π 2 r / T 2 , where r is the flight radius. Then the energy consumption of the drone in one cycle can be written as:

[0080]

[0081] In order to study the amount of information transmitted in a cycle, the present invention divides a cycle into N time slots, and the duration of each time slot is τ = T / N. When the time slot is very small, the position of the drone can be considered unchanged in each time slot. Since the drone flies at a high altitude, it is easy to establish a LoS (line-of-sight) channel with the user. In the nth time slot, the channel between the drone and the kth device can be expressed as in the form of formula (5). Then the amount of information of the kth device in the nth time slot can be expressed as:

[0082]

[0083] The bandwidth allocated to each user is set to B = 360kHz, P k [n] is the power allocated to the kth user in the nth time slot, and the noise power and bandwidth satisfy σ 2 =10 -20.4 B relationship. The total amount of information transmitted in one cycle is:

[0084]

[0085] In the second step, according to the specific settings of the first step, determine the objective function and list the optimization problem:

[0086] The purpose of this invention is to maximize energy efficiency, or EE, while meeting the user's rate limit, acceleration limit, transmission power limit, and drone trajectory limit. According to the optimization objectives and constraints, the optimization problem is listed as follows:

[0087]

[0088]

[0089] V min ≤V≤V max , (25c)

[0090] a cen ≤a max , (25d)

[0091]

[0092] Among them, R th Set the minimum flight speed V for the rate threshold of a single user. min =10m / s and maximum flight speed V max =100m / s, a max =10m / s 2 is the maximum acceleration.

[0093] The third step is to design an algorithm to solve the optimization problem:

[0094] First, for the distribution of K devices, the center point (x E ,y E ) must satisfy formula (10). E ,y E ) is determined, the optimization problem is about the optimization problem of radius and power. In order to transform the non-convex constraint (9b) into a convex constraint, the present invention uses a continuous convex approximation method and introduces an auxiliary variable γ n,k Problem (P2) is obtained, and the specific operation process is as follows (13)-(15). Although the constraints in problem (P2) are all convex constraints, the optimization target is a fractional function, so (P2) is still difficult to solve directly. Based on the Dinkelbach algorithm, the present invention proposes a solution. At the i+1th iteration, the amount of information transmitted in one cycle can be expressed as:

[0095]

[0096] By introducing the constant λ, the optimization problem can be transformed into (P3).

[0097] For problem (P3), the present invention proposes the following algorithm:

[0098] 1. Initialize the number of iterations i = 0, λ = 1 and

[0099] 2. Utilize Solving problem (P3) with λ yields

[0100] 3. Update

[0101] 4. Update i=i+1;

[0102] 5. If it does not converge, return to step 2. If it converges, proceed to step 6.

[0103] 6. Get r and P k [n].

[0104] The beneficial effects of the present invention are as follows: the present invention provides a solution for the real-time monitoring needs of 6G unmanned areas. Compared with traditional monitoring methods, it does not need to deploy a large number of ground equipment. The flexibility of drones is used to monitor unmanned areas. By reasonably designing the flight center point, flight trajectory and power allocation plan, the energy efficiency of the cell is maximized to achieve green and efficient three-dimensional coverage.

[0105] Figure 2 The comparison between the optimization scheme and the benchmark scheme is shown in Figure 1. The optimization scheme is the optimization center point, flight radius and flight center point proposed by the present invention to maximize the energy efficiency of the cell. The benchmark scheme is that the UAV takes the cell center point as the flight center point, the power is evenly distributed, and the speed is selected to minimize the propulsion power, that is, V op =(C1 / 3C2) 1 / 4 The energy efficiency of the optimization scheme is 1.51 Mbit / J, and the energy efficiency of the benchmark scheme is 1.46 Mbit / J. It can be seen that the optimization method proposed in the present invention is effective and has a certain improvement compared with the benchmark scheme.

[0106] Figure 3 The relationship between several schemes and transmission power is shown. The center of the cell is used as the flight center point, and the radius is 360m, 460m, and 560m for comparison. For these three schemes, when the transmission power increases, it can meet the user's greater information needs, so the energy efficiency is improved. When the radius is 460m, the flight speed of the drone is close to V op , the energy efficiency is high, and the energy efficiency will decrease below or above this speed. The energy efficiency of the optimization scheme proposed by the present invention also increases with the increase of transmission power, and the energy efficiency is the highest at each transmission power, which shows that the optimization scheme is not affected by the transmission power and can still effectively improve the energy efficiency.

[0107] Figure 4 The figure shows the changes in the energy efficiency, information volume, and energy consumption of the drone in one cycle when the number of users increases. As can be seen from the figure, in addition to energy consumption, energy efficiency and information volume increase with the increase in the number of users. Although the energy consumption of the optimization scheme is greater than that of the baseline scheme, the amount of information transmitted is also high. Overall, the energy efficiency of the optimization scheme is still higher than that of the baseline scheme.

[0108] The above-described embodiments merely express the implementation methods of the present invention, but they cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A method for improving energy efficiency of real-time monitoring in unmanned areas for 6G networks, characterized in that: The following steps are involved: The first step is to build a system model: 1) Assume that there are K access devices in an unmanned area that needs real-time monitoring. The devices are three-dimensionally distributed on the ground and in the air at a distance of 0-300m. A drone carrying a base station flies in a circular trajectory at an altitude of H. T is the flight period. The drone is required to communicate with K devices in real time and meet the rate threshold. By collaboratively optimizing the flight center point, flight radius and power allocation of the drone, the energy efficiency of the cell is effectively improved. 2) In the system model, bold capital letters represent matrices, bold lowercase letters represent vectors, and non-bold letters represent scalars; ||A|| represents the Frobenius norm of matrix A, and ||a|| represents the Euclidean norm of vector a; The energy efficiency is the ratio of the amount of information transmitted by the drone in one flight cycle to the energy consumed, which can be expressed as: Among them, I is the amount of information transmitted in one cycle, and E is the energy consumed in one cycle; The energy consumed by a drone in one flight cycle is expressed as: E=(P p +P t +P b )×T, (2) Among them, P p represents propulsion power, P t Indicates the transmit power, P b represents the fixed power consumption of the base station, T is a flight cycle of the drone; the drone flies in a circular trajectory, and the propulsion energy consumption in one cycle is E p It is related to flight speed, acceleration, and drone parameters and is expressed as: Among them, C1, C2 are the parameters of the UAV, V and a cen are the flight speed and acceleration of the drone, and V = 2πr / T, a cen =4π 2 r / T 2 , where r is the flight radius; In order to study the amount of information transmitted in one cycle, one cycle is divided into N time slots, and the duration of each time slot is τ = T / N; in the nth time slot, the channel between the drone and the kth device is expressed as: Where ρ is the unit path loss, q U [n]=(x E +rcosθ[n],y E +rsinθ[n],H) and q k =(x k ,y k ,z k ) are the position of the UAV in the nth time slot and the position of the kth device, respectively. (x E ,y E ) is the horizontal coordinate of the flight center point, where θ[n] = 2πn / N; then the amount of information of the kth device in the nth time slot can be expressed as: Among them, B is the bandwidth allocated to each user, P k [n] is the power allocated to the kth user in the nth time slot, σ 2 is the noise power; the total amount of information transmitted in one cycle is: In summary, the energy efficiency expression in this system can be expressed as: where q U [n] and q k The positions of the UAV and user k in the nth time slot respectively, and V is the flight speed of the UAV; In the second step, according to the specific settings of the first step, determine the objective function and list the optimization problem: Maximize energy efficiency EE while meeting the user's rate limit, acceleration limit, transmission power limit, and drone trajectory limit; list the optimization problems based on the optimization goals and constraints: V min ≤V≤V max ,(9c) a cen ≤a max ,(9d) Among them, R th is the rate threshold of a single user, V min and V max are the minimum flight speed and the maximum flight speed respectively, a max Maximum acceleration, P max is the maximum transmit power; The third step is to design an algorithm to solve the optimization problem: The flight altitude of the drone and the distribution altitude of the equipment are fixed. It is proposed to take the point with the shortest sum of horizontal distances to each user as the flight center point, that is, (x E ,y E ) satisfies the following relationship: Where (x, y) is any point on the plane; When (x E ,y E ) is determined, the optimization problem (P1) is about the optimization problem of radius and power; for constraints (9c) and (9d), when the flight period T of the UAV is constant, since the UAV flies in a circular trajectory, V and a cen It is only related to the flight radius, and can be rewritten as a constraint on r: Using the continuous convex approximation method, (P1) is transformed into a convex problem. First, the auxiliary variable γ is introduced. n,k , and satisfy: is equivalent to: Based on the idea of ​​continuous convex optimization, at the i+1th iteration, the right side of the inequality can be expanded by a first-order Taylor as follows: in is the value of the i-th iteration; Then the problem (P1) can be transformed into: (11),(12),(15) Based on the Dinkelbach algorithm, at the i+1th iteration, the amount of information transmitted in one cycle is expressed as: And by introducing the constant λ, the optimization problem can be transformed into: st(16b),(11),(12),(15) For problem (P3), the following algorithm is proposed: 1) Initialize the number of iterations i = 0, λ = 1 and 2) Utilize Solving problem (P3) with λ yields 3) Update 4) Update i=i+1; 5) If it does not converge, return to step 2; if it converges, proceed to step 6; 6) Get r and P k [n], and based on the pre-determined center point (x E ,y E ) calculates the optimized energy efficiency.

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