Resource Allocation and Deployment Method for UAV Communication Networks Supporting Wireless Power Transfer

Abstract

The present invention relates to a method for resource allocation and deployment of an unmanned aerial vehicle (UAV) communication network supporting wireless power transfer, belonging to the field of resource allocation of wireless sensor networks, and includes the following steps: S1: Establish a UAV system model; S2: Establish a channel model; S3: Establish a power model for UAVs and sensor nodes; S4: Establish a UAV energy consumption model; S5: Establish the constraints for UAV data scheduling and trajectory optimization; S6: Determine the data scheduling and energy transfer strategy based on the optimization of UAV energy consumption. The method of the present invention can effectively ensure that, on the premise of meeting the minimum power requirement of sensors, by optimizing the design of UAV deployment, data transmission, energy harvesting selection, and sensor transmission power strategy, the UAV energy consumption is minimized.

Description

Technical Field

[0001] This invention belongs to the field of wireless sensor network resource allocation, and relates to a method for allocating and deploying UAV communication network resources that supports wireless power transmission. Background Technology

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

[0003] Existing literature has studied the data transmission problem of IoT devices. For example, some studies have investigated how drones assist IoT devices in performing data transmission, jointly optimizing drone deployment and wireless resource allocation to maximize the number of IoT devices served by drones. Another example is the proposal of a resource allocation scheme based on minimizing the data transmission latency of IoT devices. However, existing research rarely considers the impact of drone trajectory design and energy consumption, as well as the power constraints of sensor nodes, 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 UAV communication networks that supports wireless power transmission. For a system including a UAV, multiple sensor nodes and a data center, the method models the total energy consumption of the UAV as the optimization objective and achieves joint optimization of resource allocation and UAV deployment.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for allocating and deploying UAV communication network resources that supports wireless power transmission, comprising the following steps:

[0007] S1: Establish a model of the unmanned aerial vehicle system;

[0008] S2: Establish a channel model;

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

[0010] S4: Establish a drone energy consumption model;

[0011] S5: Establish constraints for UAV 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 consists of a full-duplex UAV, multiple sensor nodes, and a data center. The sensor nodes collect environmental data and transmit it to the data center. The UAV departs from directly above the data center and flies sequentially to directly above each sensor node to collect data. After completing the data collection, it returns to the data center.

[0014] Let SN k This represents the k-th sensor node. Represented as SN k The amount of data collected, Ω k =(x k ,y k ) indicates SN k The location coordinates are given, k = 1, ..., K, and Ω0 = (x0, y0) represents the coordinates of the data center location. The system time is divided into equal-sized time slots, T represents the total number of time slots, and τ0 represents the time slot length. The drone's position remains fixed within a time slot, and the position of the drone in the t-th time slot is represented as... Where h represents the drone's flight altitude, set as a constant; the drone's initial position and return position are both...

[0015] When the drone flies or hovers directly above a sensor node, it charges the sensor and transmits energy using wireless power transmission.

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

[0017] Let g t,k This indicates the relationship between the UAV in the t-th time slot and SN. k The channel gain of the link between them is modeled as follows:

[0018] g t,k =βd t,k -2

[0019] Where d t,k This indicates the relationship between the UAV in the t-th time slot and SN. k The distance between them is modeled as follows:

[0020]

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

[0022] Furthermore, the establishment of the power model for the UAV and sensor nodes in step S3 specifically includes:

[0023] make This represents the battery level of the drone in the t-th time slot. The update formula is:

[0024]

[0025] in This represents the maximum battery level of the drone at the initial moment. The propulsion energy consumption of the UAV in the t-th time slot is calculated using the following formula:

[0026]

[0027] Where P0 and P0′ are constants, U tip v0 is the tip velocity of the rotor blades, and vt is the average rotor induced velocity when the UAV is hovering. t Let ξ represent the flight speed of the UAV in the t-th time slot. drag and ξ rotor These represent the fuselage drag ratio and rotor reliability, respectively. air and S rotor These are air density and rotor disk area, respectively;

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

[0029]

[0030] Where P c τ is the transmit power of the UAV. t τ is the time required for the UAV to transmit energy in the t-th time slot. t ≤τ0;

[0031] Let B t,k Indicates SN k If the battery charge is in the t-th time slot, then SN k The process of battery charge change is represented as follows:

[0032]

[0033] in SN represents the t-th time slot. k Its own basic energy consumption, B k,max Indicates SN k Maximum battery capacity, α t,k ∈{0,1} represents the data transmission variables of the sensor node, α t,k =1 indicates that the t-th 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 t-th time slot.k Harvest energy from drones, and conversely, β t,k =0; SN represents the t-th time slot. k The collected energy is modeled as follows:

[0034]

[0035] SN represents the t-th time slot. k The energy required to transmit data to the drone is modeled as follows:

[0036]

[0037] Among them, P t,k For the t-th time slot SN k Transmit data to the corresponding transmit power of the drone, T t,k For the t-th time slot SN k The time required to transmit data is modeled as follows:

[0038]

[0039] in, For SN up to the t-th time slot k The amount of data that still needs to be transmitted is modeled as follows:

[0040]

[0041] Where R t,k SN represents the t-th time slot. k The transmission rate is modeled as follows:

[0042]

[0043] Where, σ 2 This represents the transmission channel noise power.

[0044] Furthermore, the establishment of the UAV 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's propulsion energy consumption and the energy consumption required for power transmission during the system time, i.e.:

[0046]

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

[0048] ① The restrictions on drone data scheduling are as follows:

[0049] ②SNk The data transmission volume limit is as follows:

[0050] ③SN k The energy limitation for transmitting data is: α t,k =0, if B t,k ≤B k,th Among them, B k,th This indicates that SN can be maintained. k The energy threshold for normal data transmission;

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

[0052] ⑤ The energy limitation 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 received power threshold for successfully receiving energy transmitted from the drone;

[0053] ⑥SN k The limiting condition for energy harvesting is: β t,k =0, if That is, if the t-th time slot SN k If energy is plentiful, energy collection will not be performed;

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

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

[0056] ⑨ The instantaneous speed limit condition for the drone is: 0 ≤ v t ≤v max , where v max This is the maximum flight speed threshold for the drone.

[0057] Furthermore, in step S6, under the constraints of sensor data transmission and energy harvesting, a data scheduling and energy transmission strategy is determined based on UAV energy consumption optimization, specifically as follows:

[0058]

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

[0060] The beneficial effects of the present invention are as follows: The method described in the present invention can effectively ensure that the energy consumption of the UAV is minimized by optimizing the design of UAV deployment, data transmission and energy harvesting selection and sensor transmission power strategy, while meeting the minimum requirement of sensor power consumption.

[0061] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0062] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

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

[0064] Figure 2 This is a flowchart illustrating the method described in this invention. Detailed Implementation

[0065] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed 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 are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0066] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0067] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

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

[0069] Figure 1 This diagram illustrates a data transmission network scenario. As shown, the network contains multiple sensor nodes. By optimizing the drone deployment, data transmission and energy harvesting selection, and sensor transmission power strategies, the drone's energy consumption is minimized.

[0070] Figure 2 The figure shows a flowchart of the method described in this invention. The method specifically includes the following steps:

[0071] 1) Establish an unmanned aerial vehicle (UAV) system model

[0072] The model of the unmanned aerial vehicle (UAV) system 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 it to the data center; in order to achieve efficient data collection by the sensor nodes, the UAV starts from directly above the data center, flies sequentially to directly above each sensor node to collect data, and returns to the data center after completing the data collection.

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

[0074] The system time is divided into equal-sized time slots. Let T represent the total number of time slots and τ0 represent the length of a time slot. The UAV's position remains fixed within a time slot. The position of the UAV in the t-th time slot is denoted as... Where h represents the drone's flight altitude, set as a constant; the drone's initial position and return position are both...

[0075] When a drone is flying or hovering directly above a sensor node, it can charge the sensor and transfer energy using wireless power transmission.

[0076] 2) Establish a channel model

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

[0078] 3) Establish a power model for UAVs and sensor nodes.

[0079] Establishing a power model for drones and sensor nodes includes: This represents the battery level of the drone in the t-th time slot. The update formula is: in This represents the maximum battery level of the drone at the initial moment. The propulsion energy consumption of the UAV in the t-th time slot is calculated using the following formula:

[0080] Where P0 and P0′ are constants, U tip v0 is the tip velocity of the rotor blades, and vt is the average rotor induced velocity when the UAV is hovering. t Let ξ represent the flight speed of the UAV in the t-th time slot. drag and ξ rotor These represent the fuselage drag ratio and rotor reliability, respectively. air and S rotor These are air density and rotor disk area, respectively;

[0081] The energy consumed by the drone to charge the sensor nodes is modeled as follows: Where P c τ is the transmit power of the UAV. tτ is the time required for the UAV to transmit energy in the t-th time slot. t ≤τ0;

[0082] Let B t,k Indicates SN k If the battery charge is in the t-th time slot, then SN k The change in battery charge can be represented as follows:

[0083] in SN represents the t-th time slot. k Its own basic energy consumption, B k,max Indicates SN k Maximum battery capacity, α t,k ∈{0,1} represents the data transmission variables of the sensor node, α t,k =1 indicates that the t-th 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 t-th time slot. k Harvest energy from drones, and conversely, β t,k =0; SN represents the t-th time slot. k The collected energy is modeled as follows: SN represents the t-th time slot. k The energy required to transmit data to the drone is modeled as follows: Among them, P t,k For the t-th time slot SN k Transmit data to the corresponding transmit power of the drone, T t,k For the t-th time slot SN k The time required to transmit data is modeled as follows: in, For SN up to the t-th time slot k The amount of data that still needs to be transmitted is modeled as follows: R t,k SN represents the t-th time slot. k The transmission rate is modeled as follows: Where, σ 2 This represents the transmission channel noise power.

[0084] 4) Establish an energy consumption model for unmanned aerial vehicles (UAVs).

[0085] Establishing an energy consumption model for the UAV includes: Let E represent the total energy consumption of the UAV, which is modeled as the sum of the UAV's propulsion energy consumption and the energy consumption required for power transmission during the system time, i.e.

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

[0087] The constraints for modeling UAV data scheduling and trajectory optimization include:

[0088] ① The restrictions on drone data scheduling are as follows:

[0089] ②SN k The data transmission volume limit is as follows:

[0090] ③SN k The energy limitation for transmitting data is: α t,k =0, if B t,k ≤B k,th Among them, B k,th This indicates that SN can be maintained. k The energy threshold for normal data transmission;

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

[0092] ⑤ The energy limitation 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 received power threshold for successfully receiving energy transmitted from the drone;

[0093] ⑥SN k The limiting condition for energy harvesting is: β t,k =0, if That is, if the t-th time slot SN k If energy is plentiful, energy collection will not be performed;

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

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

[0096] ⑨ The instantaneous speed limit condition for the drone is: 0 ≤ v t ≤v max , where v maxThis is the maximum flight speed threshold for the drone.

[0097] 6) Determine data scheduling and energy transfer strategies based on UAV energy consumption optimization

[0098] Determining data scheduling and energy transfer strategies based on UAV energy consumption optimization includes: under the condition of satisfying constraints such as sensor data transmission and energy harvesting, determining data scheduling and energy transfer strategies based on UAV energy consumption optimization, specifically:

[0099] in, P t,k * They represent the optimized α respectively 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 solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for allocating and deploying UAV communication network resources supporting wireless power transmission, characterized in that: Includes the following steps: S1: Establish a model of the unmanned aerial vehicle system; S2: Establish the channel model; S3: Establish a power model for the drone and sensor nodes; S4: Establish a drone energy consumption model; S5: Establish constraints for UAV data scheduling and trajectory optimization; S6: Determine data scheduling and energy transmission strategies based on UAV energy consumption optimization.

2. The method for resource allocation and deployment of UAV communication networks supporting wireless power transmission according to claim 1, characterized in that: The UAV system model described in step S1 consists of a full-duplex UAV, multiple sensor nodes, and a data center. The sensor nodes collect environmental data and transmit it to the data center. The UAV starts from directly above the data center and flies sequentially to the top of each sensor node to collect data. After completing the data collection, it returns to the data center. Let SN k This represents the k-th sensor node. Represented as SN k The amount of data collected, Ω k =(x k ,y k ) indicates SN k The location coordinates are given, k = 1, ..., K, and Ω0 = (x0, y0) represents the coordinates of the data center location. The system time is divided into equal-sized time slots, T represents the total number of time slots, and τ0 represents the time slot length. The drone's position remains fixed within a time slot, and the position of the drone in the t-th time slot is represented as... Where h represents the drone's flight altitude, set as a constant; the drone's initial position and return position are both... When the drone flies or hovers directly above a sensor node, it charges the sensor and transmits energy using wireless power transmission.

3. The method for resource allocation and deployment of UAV communication networks supporting wireless power transmission according to claim 1, characterized in that: The establishment of the channel model in step S2 specifically includes: Let g t,k This indicates that the UAV in the t-th time slot is related to SN. k The channel gain of the link between them is modeled as follows: g t,k =βd t,k -2 Where d t,k This indicates that the UAV in the t-th time slot is related to SN. k The distance between them is modeled as follows: β represents the channel gain at a distance of 1 meter from the sensor, 1≤k≤K.

4. The method for resource allocation and deployment of UAV communication networks supporting wireless power transmission according to claim 1, characterized in that: Step S3, which involves establishing the power model of the UAV and sensor nodes, specifically includes: make This represents the battery level of the drone in the t-th time slot. The update formula is: in This represents the maximum battery level of the drone at the initial moment. The propulsion energy consumption of the UAV in the t-th time slot is calculated using the following formula: Where P0 and P0′ are constants, U tip v0 is the tip velocity of the rotor blades, and vt is the average rotor induced velocity when the UAV is hovering. t Let ξ represent the flight speed of the UAV in the t-th time slot. drag and ξ rotor These represent the fuselage drag ratio and rotor reliability, respectively. air and S rotor These are air density and rotor disk area, respectively; The energy consumed by the drone to charge the sensor nodes is modeled as follows: Where P c τ is the transmit power of the UAV. t τ is the time required for the UAV to transmit energy in the t-th time slot. t ≤τ0; Let B t,k Indicates SN k If the battery charge is in the t-th time slot, then SN k The process of battery charge change is represented as follows: in SN represents the t-th time slot. k Its own basic energy consumption, B k,max Indicates SN k Maximum battery capacity, α t,k ∈{0,1} represents the data transmission variables of the sensor node, α t,k =1 indicates that the t-th 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 t-th time slot. k Harvest energy from drones, and conversely, β t,k =0; SN represents the t-th time slot. k The collected energy is modeled as follows: SN represents the t-th time slot. k The energy required to transmit data to the drone is modeled as follows: Among them, P t,k For the t-th time slot SN k Transmit data to the corresponding transmit power of the drone, T t,k For the t-th time slot SN k The time required to transmit data is modeled as follows: in, For SN up to the t-th time slot k The amount of data that still needs to be transmitted is modeled as follows: Where R t,k SN represents the t-th time slot. k The transmission rate is modeled as follows: Where, σ 2 This represents the transmission channel noise power.

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

6. The method for resource allocation and deployment of UAV communication networks supporting wireless power transmission according to claim 1, characterized in that: The constraints on UAV data scheduling and trajectory optimization mentioned in step S5 include: ① The restrictions on drone data scheduling are as follows: ②SN k The data transmission volume limit is as follows: ③SN k The energy limitation for transmitting data is: α t,k =0, if B t,k ≤B k,th Among them, B k,th This indicates that SN can be maintained. k The energy threshold for normal data transmission; ④SN k The minimum transmission rate limit condition is: α t,k =0, if R t,k <R k,min Among them, R k,min Indicates SN k Minimum data transmission rate; ⑤ The energy limitation 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 received power threshold for successfully receiving energy transmitted from the drone; ⑥SN k The limiting condition for energy harvesting is: β t,k =0, if That is, if the t-th time slot SN k If energy is plentiful, energy collection will not be performed; ⑦ The time constraint for energy transfer is: τ t ≤τ; ⑧SN k The maximum transmit power limit condition is: P t,k ≤P k,max ; ⑨ The instantaneous speed limit condition for the drone is: 0 ≤ v t ≤v max , where v max This is the maximum flight speed threshold for the drone.

7. The method for resource allocation and deployment of UAV communication networks supporting wireless power transmission according to claim 1, characterized in that: In step S6, under the constraints of sensor data transmission and energy harvesting, a data scheduling and energy transfer strategy is determined based on UAV energy consumption optimization, specifically as follows: in, P t,k * They represent the optimized α respectively t,k ,β t,k ,τ t ,v t , P t,k .

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