UAV communication network resource allocation and deployment method supporting wireless power transmission

Abstract

The present invention relates to a method for allocating and deploying resources of a UAV communication network supporting wireless power transmission, which belongs to the field of wireless sensor network resource allocation, and includes the following steps: S1: establishing a UAV system model; S2: establishing a channel model; S3: establishing a UAV and sensor node power model; S4: establishing a UAV energy consumption model; S5: establishing UAV data scheduling and trajectory optimization constraints; S6: determining data scheduling and energy transmission strategies based on UAV energy consumption optimization. The method of the present invention can effectively ensure that the UAV energy consumption is minimized by optimizing the design of UAV deployment, data transmission and energy collection selection, and sensor transmission power strategy under the premise of meeting the minimum power requirement of the sensor.

Description

Technical Field

[0001] The present invention belongs to the field of wireless sensor network resource allocation, and relates to a method for allocating and deploying resources in an unmanned aerial vehicle communication network supporting wireless power transmission. Background Art

[0002] In recent years, drones have been widely used in many fields. Due to the flexibility and mobility of drones, using drones as relays, aerial base stations, etc. in wireless communication systems can effectively improve the coverage and capacity of the system. In addition, drones can also be used as mobile energy transmitters, flying in the air to broadcast radio frequency signals and wirelessly charge low-power ground nodes (such as sensors and IoT devices) to extend their life.

[0003] At present, there are existing literatures that study the data transmission problem of IoT devices. For example, there is a literature that studies drones assisting IoT devices in performing data transmission, and jointly optimizes drone deployment and wireless resource allocation to maximize the number of IoT devices served by drones. For example, there is a literature that proposes a resource allocation solution based on minimizing the data transmission delay of IoT devices. Existing research rarely jointly considers the impact of drone trajectory design and energy consumption, and sensor node power constraints on network resource allocation and drone deployment. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a method for resource allocation and deployment of a UAV communication network that supports wireless power transmission. For a system including a UAV, multiple sensor nodes and a data center, the total energy consumption of the UAV is modeled as an optimization target to achieve joint optimization of resource allocation and UAV deployment.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A method for allocating and deploying resources in a UAV communication network supporting wireless power transmission comprises the following steps:

[0007] S1: Establish UAV system model;

[0008] S2: Establish channel model;

[0009] S3: Establish the power model of drone and sensor nodes;

[0010] S4: Establish UAV energy consumption model;

[0011] S5: Establish constraints for drone data scheduling and trajectory optimization;

[0012] S6: Determine data scheduling and energy transmission strategies based on UAV energy consumption optimization.

[0013] Furthermore, the UAV system model described in step S1 is composed of a full-duplex UAV, multiple sensor nodes, and a data center, wherein the sensor nodes collect environmental data and transmit the environmental data to the data center; the UAV starts from directly above the data center and flies to directly above each sensor node in turn to collect data, and returns to the data center after completing the data collection;

[0014] SN k represents the kth sensor node, Indicated as SN k The amount of data collected, Ω k =(x k ,y k ) indicates SN k The location coordinates of the data center are k=1,...,K, Ω0=(x0,y0) and the system time is divided into time slots of equal size. Let T represent the total number of time slots and τ0 represent the length of the time slot. The position of the drone is fixed in a time slot. The position of the drone in the tth time slot is expressed as Where h represents the flight altitude of the drone, which is set to a constant; the initial position and return position of the drone are

[0015] When the drone is flying or hovering directly above a sensor node, wireless power transmission is used to charge the sensor and transmit energy.

[0016] Further, the establishment of the channel model in step S2 specifically includes:

[0017] Let g t,k Indicates the time slot between the drone and SN k The channel gain of the link between is modeled as:

[0018] g t,k =βd t,k -2

[0019] where d t,k Indicates the time slot t between the drone and SN k The distance between them is modeled as:

[0020]

[0021] β represents the channel gain at a distance of 1 meter from the sensor, 1≤k≤K.

[0022] Further, the establishment of the power model of the drone and sensor nodes in step S3 specifically includes:

[0023] make represents the battery power of the drone at the tth time slot, The update formula is:

[0024]

[0025] in Indicates the maximum value of the drone’s battery power at the initial moment. is the propulsion energy consumption of the UAV in the tth time slot, and the calculation formula is:

[0026]

[0027] Where P0 and P0′ are constants, U tip is the tip speed of the rotor blade, v0 is the average rotor induced speed when the UAV is hovering, and v t represents the flight speed of the UAV at the tth time slot, ξ drag and rotor are respectively the fuselage drag ratio and rotor reliability, ρ air and S rotor are the air density and the rotor disk area respectively;

[0028] The energy consumed by the drone to charge the sensor nodes is modeled as:

[0029]

[0030] Where P c is the transmitting power of the UAV, τ t is the time required for the drone to transmit energy in the tth time slot, τ t ≤τ0;

[0031] Let B t,k Indicates SN k The battery power at the tth time slot is SN k The battery charge change process is expressed as:

[0032]

[0033] in Indicates the tth time slot SN k Its own basic energy consumption, B k,max Indicates SN k The maximum battery capacity, α t,k ∈{0,1} represents the sensor node data transmission variable, α t,k =1 indicates the tth time slot SN k Transmit data to the drone, and vice versa, α t,k =0;β t,k ∈{0,1} represents the energy harvesting variable of the sensor node, if β t,k =1, indicating that SN is in the tth time slotk Collecting energy from drones, and vice versa, β t,k =0; Indicates the tth time slot SN k The harvested energy is modeled as:

[0034]

[0035] Indicates the tth time slot SN k The energy required to transmit data to the drone is modeled as:

[0036]

[0037] Among them, P t,k is the tth time slot SN k Transmitting power corresponding to data transmission to the drone, T t,k is the tth time slot SN k The time required to transfer data is modeled as:

[0038]

[0039] in, is the SN up to the tth time slot k The amount of data that still needs to be transferred is modeled as:

[0040]

[0041] Where R t,k Indicates the tth time slot SN k The transmission rate is modeled as:

[0042]

[0043] Among them, σ 2 Represents the transmission channel noise power.

[0044] Further, the establishment of the drone energy consumption model in step S4 specifically includes:

[0045] Let E represent the total energy consumption of the UAV, which is modeled as the sum of the UAV propulsion energy consumption and the energy consumption required for power transmission within the system time, that is:

[0046]

[0047] Furthermore, the drone data scheduling and trajectory optimization constraints in step S5 include:

[0048] ① The constraints for drone data scheduling are:

[0049] ②SNk The data transfer volume restrictions are:

[0050] ③SN k The energy limit for transmitting data is: α t,k =0, if B t,k ≤B k,th Among them, B k,th Indicates that it can maintain SN k Energy threshold for normal data transmission;

[0051] ④SN k The minimum transmission rate constraint condition is: α t,k =0, if R t,k <R k,min ; Among them, R k,min Indicates SN k The minimum rate at which data is transmitted;

[0052] ⑤ The energy limit condition for drone transmission is: β t,k =0, if β t,k P c ≤P k,th ; Among them, P k,th Indicates SN k The minimum receiving power threshold for successfully receiving the energy transmitted by the drone;

[0053] ⑥SN k The constraints on energy collection are: β t,k =0, if That is, if the tth time slot SN k If there is sufficient energy, no energy harvesting is performed;

[0054] ⑦The energy transfer time limit is: τ t ≤τ;

[0055] ⑧SN k The maximum transmission power limit condition is: P t,k ≤P k,max ;

[0056] ⑨The instantaneous speed limit of the drone is: 0≤v t ≤v max , where v max It is the maximum flight speed threshold of the UAV.

[0057] Further, in step S6, under the constraints of sensor data transmission and energy collection, data scheduling and energy transmission strategies are determined based on the optimization of drone energy consumption, specifically:

[0058]

[0059] in, P t,k * Respectively represent the optimized α t,k ,β t,k ,τ t ,v t , P t,k .

[0060] The beneficial effect of the present invention is that the method described in the present invention can effectively ensure that the energy consumption of the drone is minimized by optimizing the design of drone deployment, data transmission and energy collection selection and sensor transmission power strategy under the premise of meeting the minimum power requirement of the sensor.

[0061] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0063] Figure 1 This is a schematic diagram of a data transmission network scenario;

[0064] Figure 2 The figure is a schematic diagram of the process of the method of the present invention. DETAILED DESCRIPTION

[0065] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0066] Among them, the drawings are only used for illustrative explanations, and they only represent schematic diagrams rather than actual pictures, and should not be understood as limitations on the present invention. In order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0067] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "front", "rear", etc. indicate the orientation or position relationship, they are based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0068] The present invention discloses a method for allocating and deploying resources in a UAV communication network that supports wireless power transmission. In the network, there are multiple sensor nodes that are responsible for collecting environmental data and transmitting it to a data center. Data scheduling and energy transmission strategies are determined based on the optimization of UAV energy consumption.

[0069] Figure 1 This is a schematic diagram of a data transmission network scenario. As shown in the figure, there are multiple sensor nodes in the network. By optimizing the design of drone deployment, data transmission and energy collection selection, and sensor transmission power strategy, the energy consumption of drones can be minimized.

[0070] Figure 2 The flowchart of the method of the present invention is shown in the figure. As shown in the figure, the method of the present invention specifically includes the following steps:

[0071] 1) Establish UAV system model

[0072] The establishment of the UAV system model includes: the system consists of a full-duplex UAV, multiple sensor nodes and a data center, where the sensor nodes collect environmental data and transmit them to the data center; in order to achieve efficient data collection of sensor nodes, the UAV starts from the top of the data center and flies to the top of each sensor node in turn to collect data, and returns to the data center after completing data collection;

[0073] SN k represents the kth sensor node, Indicated as SN k The amount of data collected, Ω k =(x k ,y k ) indicates SN k The location coordinates of k = 1, ..., K, Ω0 = (x0, y0) represent the coordinates of the data center location;

[0074] The system time is divided into time slots of equal size. Let T represent the total number of time slots, τ0 represents the length of the time slot, and the position of the drone is fixed in a time slot. The position of the drone in the tth time slot is expressed as Where h represents the flight altitude of the drone, which is set to a constant; the initial position and return position of the drone are

[0075] When the drone is flying or hovering directly above a sensor node, wireless power transmission can be used to charge the sensor and transmit energy.

[0076] 2) Establish channel model

[0077] Establishing the channel model includes: Let g t,k Indicates the time slot t between the drone and SN k The channel gain of the link between is modeled as: g t,k =βd t,k -2 , where d t,k Indicates the time slot t between the drone and SN k The distance between them is modeled as: β represents the channel gain at a distance of 1 meter from the sensor, 1≤k≤K.

[0078] 3) Establish the power model of drones and sensor nodes

[0079] Establishing the power model of drones and sensor nodes includes: represents the battery power of the drone at the tth time slot, The update formula is: in Indicates the maximum value of the drone’s battery power at the initial moment. is the propulsion energy consumption of the UAV in the tth time slot, and the calculation formula is:

[0080] Where P0 and P0′ are constants, U tip is the tip speed of the rotor blade, v0 is the average rotor induced speed when the UAV is hovering, and v t represents the flight speed of the UAV at the tth time slot, ξ drag and rotor are respectively the fuselage drag ratio and rotor reliability, ρ air and S rotor are the air density and the rotor disk area respectively;

[0081] The energy consumed by the drone to charge the sensor nodes is modeled as: Where P c is the transmitting power of the UAV, τ tis the time required for the drone to transmit energy in the tth time slot, τ t ≤τ0;

[0082] Let B t,k Indicates SN k The battery power at the tth time slot is SN k The battery charge change process can be expressed as:

[0083] in Indicates the tth time slot SN k Its own basic energy consumption, B k,max Indicates SN k The maximum battery capacity, α t,k ∈{0,1} represents the sensor node data transmission variable, α t,k =1 indicates the tth time slot SN k Transmit data to the drone, and vice versa, α t,k =0;β t,k ∈{0,1} represents the energy harvesting variable of the sensor node, if β t,k =1, indicating that SN is in the tth time slot k Collecting energy from drones, and vice versa, β t,k =0; Indicates the tth time slot SN k The harvested energy is modeled as: Indicates the tth time slot SN k The energy required to transmit data to the drone is modeled as: Among them, P t,k is the tth time slot SN k Transmitting power corresponding to data transmission to the drone, T t,k is the tth time slot SN k The time required to transfer data is modeled as: in, is the SN up to the tth time slot k The amount of data that still needs to be transferred is modeled as: R t,k Indicates the tth time slot SN k The transmission rate is modeled as: Among them, σ 2 Represents the transmission channel noise power.

[0084] 4) Establishing UAV energy consumption model

[0085] Establishing the UAV energy consumption model includes: let E represent the total energy consumption of the UAV, which is modeled as the sum of the UAV propulsion energy consumption and the energy consumption required for power transmission within the system time, that is,

[0086] 5) Modeling UAV data scheduling and trajectory optimization constraints

[0087] Modeling drone data scheduling and trajectory optimization constraints include:

[0088] ① The constraints for drone data scheduling are:

[0089] ②SN k The data transfer volume restrictions are:

[0090] ③SN k The energy limit for transmitting data is: α t,k =0, if B t,k ≤B k,th Among them, B k,th Indicates that it can maintain SN k Energy threshold for normal data transmission;

[0091] ④SN k The minimum transmission rate constraint condition is: α t,k =0, if R t,k <R k,min ; Among them, R k,min Indicates SN k The minimum rate at which data is transmitted;

[0092] ⑤ The energy limit condition for drone transmission is: β t,k =0, if β t,k P c ≤P k,th ; Among them, P k,th Indicates SN k The minimum receiving power threshold for successfully receiving the energy transmitted by the drone;

[0093] ⑥SN k The constraints on energy collection are: β t,k =0, if That is, if the tth time slot SN k If there is sufficient energy, no energy harvesting is performed;

[0094] ⑦The energy transfer time limit is: τ t ≤τ;

[0095] ⑧SN k The maximum transmission power limit condition is: P t,k ≤P k,max ;

[0096] ⑨The instantaneous speed limit of the drone is: 0≤v t ≤v max , where v maxIt is the maximum flight speed threshold of the UAV.

[0097] 6) Determine data scheduling and energy transmission strategies based on drone energy consumption optimization

[0098] Determining data scheduling and energy transmission strategies based on drone energy consumption optimization includes: Under the constraints of sensor data transmission and energy collection, determining data scheduling and energy transmission strategies based on drone energy consumption optimization, specifically:

[0099] in, P t,k * Respectively represent the optimized α t,k ,β t,k ,τ t ,v t , P t,k .

[0100] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A method for allocating and deploying resources in a UAV communication network supporting wireless power transmission, characterized in that: The following steps are involved: S1: Establish UAV system model; S2: Establish channel model; S3: Establish the power model of drone and sensor nodes; S4: Establish UAV energy consumption model; S5: Establish constraints for drone data scheduling and trajectory optimization; S6: Determine data scheduling and energy transmission strategies based on UAV energy consumption optimization; The step S3 of establishing the power model of the drone and sensor nodes specifically includes: make represents the battery power of the drone at the tth time slot, The update formula is: in Indicates the maximum value of the drone’s battery power at the initial moment. is the propulsion energy consumption of the UAV in the tth time slot, and the calculation formula is: Where P0 and P0′ are constants, U tip is the tip speed of the rotor blade, v0 is the average rotor induced speed when the UAV is hovering, and v t represents the flight speed of the UAV at the tth time slot, ξ drag and rotor are respectively the fuselage drag ratio and rotor reliability, ρ air and S rotor are the air density and the rotor disk area respectively; The energy consumed by the drone to charge the sensor nodes is modeled as: Where P c is the transmitting power of the UAV, τ t is the time required for the drone to transmit energy in the tth time slot, τ t ≤τ0, τ0 represents the time slot length; Let B t,k Indicates SN k The battery power at the tth time slot is SN k The battery charge change process is expressed as: in Indicates the tth time slot SN k Its own basic energy consumption, B k,max Indicates SN k The maximum battery capacity, α t,k ∈{0,1} represents the sensor node data transmission variable, α t,k =1 indicates the tth time slot SN k Transmit data to the drone, and vice versa, α t,k =0;β t,k ∈{0,1} represents the energy harvesting variable of the sensor node, if β t,k =1, indicating that SN is in the tth time slot k Collecting energy from drones, and vice versa, β t,k =0; Indicates the tth time slot SN k The harvested energy is modeled as: Indicates the tth time slot SN k The energy required to transmit data to the drone is modeled as: Among them, P t,k is the tth time slot SN k Transmitting power corresponding to data transmission to the drone, T t,k is the tth time slot SN k The time required to transfer data is modeled as: in, is the SN up to the tth time slot k The amount of data that still needs to be transferred is modeled as: Where R t,k Indicates the tth time slot SN k The transmission rate is modeled as: Among them, σ 2 Represents the transmission channel noise power.

2. The method for allocating and deploying resources in a UAV communication network supporting wireless power transmission according to claim 1, characterized in that: The UAV system model described in step S1 is composed of a full-duplex UAV, multiple sensor nodes, and a data center, wherein the sensor nodes collect environmental data and transmit them to the data center; the UAV starts from directly above the data center and flies to directly above each sensor node in turn to collect data, and returns to the data center after completing data collection; SN k represents the kth sensor node, Indicated as SN k The amount of data collected, Ω k =(x k ,y k ) indicates SN k The location coordinates of the data center are k=1,...,K, Ω0=(x0,y0) and the system time is divided into time slots of equal size. Let T represent the total number of time slots. The position of the drone is fixed in a time slot. The position of the drone in the tth time slot is expressed as Where h represents the flight altitude of the drone, which is set to a constant; the initial position and return position of the drone are When the drone is flying or hovering directly above a sensor node, wireless power transmission is used to charge the sensor and transmit energy.

3. The method for allocating and deploying resources in a UAV communication network supporting wireless power transmission according to claim 1, characterized in that: The step S2 of establishing the channel model specifically includes: Let g t,k Indicates the time slot t between the drone and SN k The channel gain of the link between is modeled as: g t,k =βd t,k -2 where d t,k Indicates the time slot t between the drone and SN k The distance between them is modeled as: β represents the channel gain at a distance of 1 meter from the sensor, 1≤k≤K.

4. The method for allocating and deploying resources in a UAV communication network supporting wireless power transmission according to claim 1, characterized in that: The establishment of the UAV energy consumption model described in step S4 specifically includes: Let E represent the total energy consumption of the UAV, which is modeled as the sum of the UAV propulsion energy consumption and the energy consumption required for power transmission within the system time, that is:

5. The method for allocating and deploying resources in a UAV communication network supporting wireless power transmission according to claim 1, characterized in that: The drone data scheduling and trajectory optimization constraints in step S5 include: ① The constraints for drone data scheduling are: ②SN k The data transfer volume restrictions are: T represents the total number of time slots; ③SN k The energy limit for transmitting data is: α t,k =0, if B t,k ≤B k,th Among them, B k,th Indicates that SN can be maintained k Energy threshold for normal data transmission; ④SN k The minimum transmission rate constraint is: t,k =0, if R t,k <R k,min ; Among them, R k,min Indicates SN k The minimum rate at which data is transmitted; ⑤ The energy limit condition for drone transmission is: β t,k =0, if β t,k P c ≤P k,th ; Among them, P k,th Indicates SN k The minimum receiving power threshold for successfully receiving the energy transmitted by the drone; ⑥SN k The constraints on energy collection are: β t,k =0, if That is, if the tth time slot SN k If there is sufficient energy, no energy harvesting is performed; ⑦The energy transfer time limit is: τ t ≤τ0; ⑧SN k The maximum transmission power limit condition is: P t,k ≤P k,max ; ⑨The instantaneous speed limit of the drone is: 0≤v t ≤v max , where v max It is the maximum flight speed threshold of the UAV.

6. The method for allocating and deploying resources in a UAV communication network supporting wireless power transmission according to claim 1, characterized in that: In step S6, under the constraints of sensor data transmission and energy collection, data scheduling and energy transmission strategies are determined based on the energy consumption optimization of the drone, specifically: in, Respectively represent the optimized sensor node data transmission variable α t,k , sensor node energy harvesting variable β t,k , the time τ required for the drone to transmit energy in the tth time slot t , the flight speed v of the drone at the tth time slot t , the horizontal coordinate of the drone in the tth time slot The vertical coordinate of the drone in the tth time slot The tth time slot SN k Transmitting data to the UAV corresponding to the transmission power P t,k .

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