A cell-free uav communication resource allocation method for wireless energy transmission

Abstract

The application discloses a cell-free UAV communication resource allocation method for wireless energy transmission and belongs to the technical field of UAV communication and wireless energy transmission. The method comprises the following steps: constructing a coordinated transmission and reception model of downlink wireless energy transmission and uplink wireless information transmission; dynamically dividing energy collection time slots and data transmission time slots in a communication period; jointly optimizing downlink transmission power of a distributed access point and uplink transmission power of a UAV; establishing a three-dimensional flight trajectory model of the UAV and jointly optimizing the three-dimensional flight trajectory model and communication resources; and solving the joint optimization problem by using an alternating optimization algorithm with the maximum system global energy efficiency as the target. The application can relieve the energy limitation problem of a UAV communication module and improve the stability of an air-ground communication link and system energy efficiency.

Description

Technical Field

[0001] This invention belongs to the field of UAV communication and wireless power transfer technology, specifically relating to a cellless UAV communication resource allocation method for wireless power transfer. Background Technology

[0002] With the rapid development of Unmanned Aerial Vehicle (UAV) technology, UAVs have demonstrated significant advantages in emergency communication, environmental monitoring, and IoT data collection. Compared to traditional ground communication nodes, UAVs offer advantages such as flexible deployment and a high probability of line-of-sight communication links. However, the size and payload of UAVs severely limit their battery capacity, resulting in extremely limited endurance, which has become a core bottleneck restricting their large-scale, long-duration missions. How to effectively overcome the energy-constrained problem of UAVs has become a critical challenge that urgently needs to be addressed in the field of aeronautical communications.

[0003] In recent years, wireless power transfer (WPT) technology has been considered an effective way to solve the energy bottleneck of low-power wireless nodes. In a wireless powered communication network (WPCN), the terminal collects energy from radio waves in the downlink and uses this energy to transmit data in the uplink. Introducing WPT technology into a drone communication system allows ground nodes to provide wireless charging for the drone, which then uses the collected energy to perform communication tasks, thereby extending its operating time and enabling the system to operate self-sustainingly.

[0004] While some wireless power transfer solutions for drones based on traditional cellular networks exist, they still have inherent limitations. Traditional cellular networks employ centralized base station deployment, forming a cellular topology, and rely on fixed cell boundaries for access. When drones move at high speeds, they face frequent handovers, which can easily trigger signaling storms and link interruptions. More importantly, wireless power transfer efficiency is extremely sensitive to physical distance, resulting in significant spatial path loss. The distance between traditional macro base stations and high-altitude drones is often considerable, leading to negligible amounts of radio frequency energy actually collected by the drone, insufficient to support its subsequent flight and uplink communication needs.

[0005] Cell-free network architecture offers a novel paradigm for addressing these issues. This architecture forms a virtual megacell centered on the user by densely deploying a large number of low-power distributed access points (APs) within a region, coordinated in real-time by a central processing unit, thus completely eliminating the problems of traditional cell boundaries and frequent handovers. Applying cell-free architecture to drone wireless power transfer scenarios has inherent advantages: densely distributed APs significantly shorten the link distance between the power transmitter and the drone, reducing spatial path loss and improving downlink wireless power transfer efficiency; simultaneously, multi-AP collaborative reception also improves the signal quality of uplink communication for the drone.

[0006] However, in cellless UAV communication systems for wireless power transfer, resource allocation and scheduling become exceptionally complex due to the introduction of the UAV's three-dimensional high-dynamic mobility and the tight coupling between downlink power harvesting and uplink information transmission. Currently, there is still a lack of systematic and mature solutions for dynamically allocating time slots between downlink wireless power transfer and uplink communication, controlling the transmit power of multiple access points and UAVs, and jointly optimizing the allocation of UAV flight trajectories to maximize system energy efficiency or spectral efficiency. Summary of the Invention

[0007] To address the aforementioned technical problems, this invention provides a cellless UAV communication resource allocation method for wireless power transmission. Based on a cellless network architecture, it maximizes system energy efficiency by jointly optimizing time slot allocation, uplink and downlink power control, and the UAV's three-dimensional flight trajectory.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] A cell-free UAV communication resource allocation method for wireless power transfer includes:

[0010] Cellular-free air-to-ground collaborative transceiver architecture is constructed by densely deploying a large number of distributed low-power access points (APs) under unified scheduling by a central processing unit (CPU) within the mission area, breaking down traditional cell boundaries. A collaborative transceiver model for downlink wireless power transfer (WPT) and uplink wireless information transfer (WIT) is established: In the downlink, the distributed AP cluster jointly transmits radio frequency power to the UAV; in the uplink, the UAV uses the collected energy to send communication data to the AP cluster, which is then globally decoded by the CPU. Simultaneously, a "collect first, transmit later" communication protocol is established, dividing the communication cycle into a downlink wireless power collection phase and an uplink data transmission phase.

[0011] Dynamic Time Slot Allocation for Energy and Information Transmission: Addressing the strong temporal coupling between downlink wireless energy harvesting and uplink data transmission, a dynamic time slot allocation mechanism is designed. Based on the UAV's real-time battery status, service data volume requirements, and current air-to-ground channel quality, the ratio of time slots allocated to the downlink energy harvesting and uplink data transmission phases within the communication cycle is adaptively adjusted to maximize effective communication time while ensuring the UAV's basic survivability.

[0012] Joint control of downlink and uplink communication power: A joint control model for downlink and uplink transmission power is established. In the downlink phase, the downlink transmission power of the distributed AP cluster is optimized to meet the energy causal constraints of the UAV communication module and reduce the total system energy consumption. In the uplink phase, the uplink transmission power of the UAV is dynamically adjusted based on the remaining energy of the UAV communication module, and the uplink received signal-to-interference-plus-noise ratio is improved through multi-AP collaborative reception.

[0013] 3D trajectory planning for UAVs in highly dynamic environments: Considering the extremely high sensitivity of spatial distance to wireless RF energy attenuation, a 3D spatial kinematic model of the UAV is established. By combining air-to-ground line-of-sight (LoS) channel states, the flight waypoints, speeds, and 3D spatial trajectories of the UAV are dynamically planned. The algorithm guides the UAV to actively approach densely distributed "energy hotspot" areas with low energy levels to improve collection efficiency, and to occupy the optimal line-of-sight position when communication demand is high, effectively addressing the time-varying fading characteristics of air-to-ground links.

[0014] A joint optimization algorithm based on maximizing energy efficiency is designed: A joint optimization mathematical model is constructed with the objective function of maximizing the system's global energy efficiency. The time slot allocation ratio, AP downlink power, UAV uplink power, and UAV 3D trajectory are used as coupled optimization variables, and physical constraints such as energy causality, maximum transmit power, maximum flight speed, and collision avoidance are introduced. Alternating optimization is employed, combined with a continuous convex approximation method for iterative solution, outputting a convergent resource allocation scheme that satisfies all constraints.

[0015] In a second aspect, the present invention provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the aforementioned method for allocating cellless unmanned aerial vehicle communication resources for wireless power transfer.

[0016] Thirdly, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enable the processor to implement the aforementioned method for allocating cellless unmanned aerial vehicle communication resources for wireless power transfer.

[0017] The beneficial effects of this invention are as follows:

[0018] First, by densely deploying distributed access points and adopting a "collect first, transmit later" protocol, the energy transmission distance is significantly shortened, effectively overcoming the bottleneck of drone endurance and improving downlink wireless energy collection efficiency and uplink communication quality.

[0019] Second, a dynamic time slot allocation and power joint control mechanism is adopted to adaptively allocate resources based on the UAV's remaining energy, channel status and service requirements, thereby maximizing system throughput and significantly improving spectrum utilization while ensuring energy causal constraints.

[0020] Third, the three-dimensional trajectory of the UAV and communication resources are incorporated into a unified optimization framework to guide the UAV to actively fly to energy hotspot areas or channel-optimized locations, thereby achieving synergistic gains in energy collection and data transmission and further improving the system's energy efficiency.

[0021] Fourth, the alternating optimization algorithm is used to efficiently solve the non-convex joint optimization problem and output the global optimal resource allocation decision, taking into account both theoretical optimality and engineering feasibility, thus providing reliable technical support for the actual deployment of cell-free unmanned aerial vehicle communication systems. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the cell-free unmanned aerial vehicle (UAV) communication system architecture and uplink / downlink for wireless energy transfer according to the present invention. Detailed Implementation

[0023] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0024] This invention presents a cellless UAV communication resource allocation method for wireless energy transfer. Its core innovations are: 1) Constructing a collaborative architecture for wireless energy harvesting and information transmission. Utilizing densely deployed distributed access points (APs) in a cellless network, radio frequency wireless energy transfer (WPT) is performed to the UAV in the downlink; in the uplink, the UAV uses the harvested energy to transmit communication data, which is jointly decoded by the central processing unit, effectively overcoming the UAV's endurance bottleneck. 2) Designing a dynamic optimization mechanism for time slots and power. Based on the coupling relationship between energy harvesting and data transmission, the ratio of downlink energy transfer to uplink communication time slots is dynamically allocated; simultaneously, the downlink transmit power of the distributed AP cluster and the uplink transmit power of the UAV are jointly optimized to maximize the system's energy efficiency or spectral efficiency while ensuring communication quality. 3) Establishing a joint optimization scheme for UAV three-dimensional trajectory and communication resources. The spatial flight path planning, time slot allocation, and power control of the UAV are incorporated into a unified optimization framework, guiding the UAV to actively approach the optimal AP cluster to improve energy harvesting and data interaction efficiency, comprehensively enhancing the system's overall transmission performance and continuous operation capability. Specifically, the method includes:

[0025] Step 1: Construct a cell-free air-ground collaborative transceiver architecture: Densely deploy distributed access points uniformly scheduled by the central processing unit, discretize the communication cycle into multiple time steps, and establish a "collect first, transmit later" communication protocol consisting of downlink wireless energy collection time slots and uplink data transmission time slots;

[0026] Step 2: Dynamically allocate time slots: Based on the UAV status and channel quality, dynamically adjust the time slot allocation ratio for downlink wireless energy harvesting and uplink data transmission within each time step;

[0027] Step 3: Construct a power control model: Set the downlink transmit power variable for each distributed access point and the uplink transmit power variable for the UAV;

[0028] Step 4: Plan the 3D trajectory: Build a 3D spatial kinematic model of the UAV using the 3D spatial coordinate sequence of the UAV at each time step, and dynamically plan the flight trajectory in conjunction with the channel state;

[0029] Step 5, Joint Optimization Solution: Construct an optimization model with the goal of maximizing energy efficiency. The time slot allocation ratio, downlink transmit power variables of each distributed access point, uplink transmit power variables of the UAV, and the flight trajectory are used as coupling variables. Energy causal constraints, maximum transmit power constraints, and kinematic constraints are introduced. An alternating optimization algorithm is used to iteratively solve the problem and output a convergent resource allocation scheme that satisfies the constraints.

[0030] like Figure 1The diagram shows a cellless air-to-ground collaborative transceiver architecture, which intuitively illustrates the topological relationship between densely deployed distributed access points (APs), central processing units (CPUs), and drones, and marks the downlink wireless power transfer (WPT) and uplink wireless information transfer (WIT) processes.

[0031] Furthermore, in step 1:

[0032] A large number of distributed access points (APs) are densely deployed within the task area and uniformly scheduled by the CPU, breaking down traditional cell boundaries. Assume the system includes... One single-antenna distributed access point (AP) and one single-antenna drone. In the downlink, the distributed AP cluster performs joint radio frequency power transmission to the drone; in the uplink, the drone uses the collected power to send communication data to the AP cluster, which is then globally decoded by the CPU.

[0033] The system adopts a "collect first, transmit later" communication protocol, which completes a communication cycle. Discretize into There are several time steps, each with a time step size of [number]. .

[0034] Furthermore, in step 2:

[0035] In the Within each time step, it is divided into two non-overlapping phases:

[0036] 1) Downlink wireless power harvesting time slot: duration is ,in , Optimize variables by dividing time into proportions.

[0037] 2) Uplink data transmission time slot: duration is .

[0038] To reflect the dynamic nature of time slot allocation, this invention comprehensively considers the remaining energy of the UAV, the current channel quality, and the amount of traffic to be transmitted within each time step. Let the UAV at the... The remaining energy for each time step is The amount of data to be transmitted is The equivalent channel gain between the AP and the UAV is Then the time slot allocation ratio optimization variable can be expressed as:

[0039] (1)

[0040] It is a dynamic decision-making criterion. When the UAV has low remaining energy or high equivalent channel gain, the system appropriately increases the downlink energy collection time slot; when the amount of data to be transmitted is large and the UAV has sufficient energy, the system appropriately increases the uplink data transmission time slot.

[0041] Let the amount of new arriving data at the nth time step be... The amount of data to be transmitted is calculated according to... renew.

[0042] Furthermore, in step 3:

[0043] Calculate the path loss of the air-to-ground line-of-sight link based on the three-dimensional physical distance between each distributed AP and the UAV. Assume the UAV is at the... The three-dimensional coordinates of the position at each time step are , No. The location coordinates of each AP are The distance between the two is The air-to-ground line-of-sight (LoS) channel gain can be expressed as... :

[0044] (2)

[0045] in, Let be the channel power gain at the reference distance. The total RF energy received by the UAV during the downlink phase can be modeled as... :

[0046] (3)

[0047] in, , For energy conversion efficiency, it is assumed that the energy signals transmitted by each AP are incoherently superimposed. This represents the downlink average transmit power of the k-th AP at the n-th time step.

[0048] In the uplink, the UAV sends communication data to the surrounding AP cluster. Each receiving AP aggregates the signals through the fronthaul link to the central processing unit for global joint decoding. During this phase, energy causality constraints must be strictly adhered to: the sum of the uplink transmission energy consumed by the UAV for data transmission and the static power consumption of the circuitry must not exceed the sum of the total effective electrical energy collected in the first phase and the initial reserve power. Let the uplink transmission power of the UAV be... Then the energy constraint is:

[0049] (4)

[0050] in, This indicates the static power consumption of the UAV communication module. Remaining energy is calculated according to...

[0051] (5)

[0052] Update and meet .

[0053] Under this constraint, calculate the uplink received signal-to-interference-plus-noise ratio (SINR) in multi-AP cooperative reception mode. This leads to the achievable transmission rate. :

[0054] , (6)

[0055] in, B is the equivalent noise power of each receiving AP after joint processing, and B is the system bandwidth. This represents the average achievable transmission rate after taking into account the uplink time slot ratio. This implementation assumes that the uplink and downlink channels are reciprocal, and the central processing unit uses maximum ratio combining to jointly process the received signals from each AP.

[0056] Furthermore, in step 4:

[0057] Set the initial takeoff position of the drone Final destination Maximum flight speed and the minimum flight altitude for collision avoidance and maximum flight altitude As kinematic physical constraints, the kinematic constraint formulas are as follows:

[0058]

[0059] (7)

[0060]

[0061] in, The time step number is represented by , and N represents the total number of time steps within the communication cycle. Through this modeling, the algorithm can guide the UAV to proactively approach densely distributed "energy hotspot" areas when energy is scarce to improve collection efficiency, and to occupy the optimal line-of-sight position when communication demand is high.

[0062] Furthermore, this invention does not rely solely on start and end positions and flight speed for path generation during UAV trajectory planning; instead, it considers the air-to-ground channel state as a crucial basis for trajectory updates. This is due to the channel gain between the k-th AP and the UAV. The position of the drone at the nth time step The decision is made regarding the distance from each access point (AP) to the drone when its location changes. Channel gain Wireless energy harvesting capacity and uplink transmission rate All of these will change accordingly. Therefore, within each time step, the central processing unit evaluates candidate locations for the next time step based on the currently estimated channel state information, the remaining energy of the UAV, and the amount of data to be transmitted, and selects a trajectory point that balances energy harvesting efficiency and uplink communication quality. Thus, when the UAV has insufficient energy, it can move to areas with higher channel gain and denser AP distribution; when the service transmission demand is high, it can move to locations with better uplink quality, thereby achieving joint dynamic planning of channel state and three-dimensional flight trajectory.

[0063] Furthermore, in step 5:

[0064] This invention constructs a joint optimization mathematical model with the objective function of maximizing the system's global energy efficiency (EE). Energy efficiency is defined as the ratio of the total number of data bits successfully transmitted by the UAV to the system during the entire communication cycle to the total energy consumed by the system. The joint optimization problem can be expressed as:

[0065] (8)

[0066] in, The system's fixed static power consumption is generated by the AP, central processing unit, and fronthaul link. The numerator of Equation (8) is the total number of data bits transmitted during the entire communication cycle, and the denominator is the total energy consumed by the AP downlink transmission, UAV communication module uplink transmission and circuit operation, and system fixed circuits.

[0067] Equation (8) is also subject to the energy causality constraint shown in Equation (4) and the kinematic constraint shown in Equation (7). as well as Constraints.

[0068] Since the above objective function involves the coupling relationship between time division ratio, power control and three-dimensional trajectory, and channel gain With drone location Due to the nonlinear correlation, this joint optimization problem is a non-convex optimization problem, making it difficult to directly obtain a closed-form global optimum. This invention employs an alternating optimization algorithm based on the block coordinate descent concept to solve the problem. It decomposes the original problem into a joint time slot and power optimization subproblem and a UAV trajectory optimization subproblem, and iterates alternately between the two subproblems to gradually improve the system's energy efficiency.

[0069] Specifically, the first step is to initialize the feasible trajectory of the drone. AP downlink transmit power UAV uplink transmission power and time slot allocation ratio And set the maximum number of iterations. and convergence threshold , This indicates the static power consumption of the drone's circuitry. Let represent the uplink transmit power of the UAV to be optimized at time step n. In the r-th iteration, the UAV trajectory is fixed. Channel gain Treating these as known quantities, let EE(·) denote the system energy efficiency function, and solve the time slot and power optimization subproblems:

[0070] (9)

[0071] s

[0072]

[0073] The corresponding result is obtained after the (r+1)th iteration. and Then, with the time slots and power variables fixed, the sub-problem of optimizing the UAV's three-dimensional trajectory is further solved:

[0074] (10)

[0075] st ,

[0076] ,

[0077] ,

[0078] Due to the trajectory subproblem and There is a non-linear relationship, which can be observed at the current iteration point. We approximate the non-convex terms with a first-order approximation, transforming them into a solvable convex approximation subproblem. Then we update the trajectory. And recalculate the channel gain. Energy harvesting volume and uplink speed .

[0079] When the energy efficiency of two consecutive iterations (such as the (r+1)th iteration and the rth iteration) satisfies:

[0080] (11)

[0081] Or the number of iterations reaches When the time is up, the algorithm stops and outputs the final time slot allocation ratio. AP downlink transmit power UAV uplink transmission power and the three-dimensional trajectory of the drone Through the above-described alternating optimization process, this invention can achieve joint optimization allocation of communication resources and UAV trajectories under the conditions of satisfying energy causality constraints, transmission power constraints, and flight kinematic constraints.

[0082] As one embodiment of the present invention, a cell-free UAV wireless power transmission communication scenario is constructed. K distributed access points (APs) are randomly deployed within the task area. Each AP is connected to the central processing unit (CPU) via a fronthaul link. The CPU uniformly acquires UAV status information, air-to-ground channel status information, and transmit power information of each AP. The UAV starts from its initial position... Departure, fly to the destination position within a given communication period T. The communication cycle is divided into N time steps, each of which includes a downlink wireless energy harvesting phase and an uplink data transmission phase.

[0083] In this embodiment, the CPU first initializes the drone's flight trajectory. Time slot allocation ratio AP downlink transmit power and the uplink transmission power of the drone Subsequently, based on the distance between the drone and the AP within each time step... Calculate channel gain And further calculate the energy that the drone can collect during the downlink phase. and uplink transmission rate In each iteration, the CPU first fixes the drone's trajectory and optimizes... , and Then, with time slots and power variables fixed, optimize the UAV's three-dimensional trajectory. Repeat the above process until the change in system energy efficiency (EE) between two consecutive iterations is less than a preset threshold. .

[0084] In contrast, three resource allocation schemes can be set: the first is a fixed time slot allocation scheme, that is, within all time steps... The first approach remains unchanged; the second is a fixed trajectory scheme, where the UAV flies along a preset straight path, optimizing only power and time slots; the third is the joint optimization scheme proposed in this invention, which simultaneously optimizes time slot allocation, AP downlink transmission power, UAV uplink transmission power, and UAV three-dimensional trajectory. The performance of different resource allocation schemes can be evaluated by comparing the system energy efficiency, remaining energy of the UAV communication module, and uplink transmission rate under the three schemes.

[0085] In a second aspect, the present invention provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the aforementioned method for allocating cellless unmanned aerial vehicle communication resources for wireless power transfer.

[0086] Thirdly, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enable the processor to implement the aforementioned method for allocating cellless unmanned aerial vehicle communication resources for wireless power transfer.

[0087] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A cellless UAV communication resource allocation method for wireless power transfer, characterized in that, include: Step 1: Construct a cell-free air-ground collaborative transceiver architecture: Densely deploy distributed access points uniformly scheduled by the central processing unit, discretize the communication cycle into multiple time steps, and establish a first-collect-then-transmit communication protocol consisting of downlink wireless energy collection time slots and uplink data transmission time slots; Step 2: Dynamically allocate time slots: Based on the UAV status and channel quality, dynamically adjust the time slot allocation ratio for downlink wireless energy harvesting and uplink data transmission within each time step; Step 3: Construct a power control model: Set the downlink transmit power variable for each distributed access point and the uplink transmit power variable for the UAV; Step 4: Plan the 3D trajectory: Build a 3D spatial kinematic model of the UAV using the 3D spatial coordinate sequence of the UAV at each time step, and dynamically plan the flight trajectory in conjunction with the channel state; Step 5, Joint Optimization Solution: Construct an optimization model with the goal of maximizing energy efficiency. The time slot allocation ratio, downlink transmit power variables of each distributed access point, uplink transmit power variables of the UAV, and the flight trajectory are used as coupling variables. Energy causal constraints, maximum transmit power constraints, and kinematic constraints are introduced. An alternating optimization algorithm is used to solve the problem and output a convergent resource allocation scheme that satisfies the constraints.

2. The cellless UAV communication resource allocation method for wireless power transfer according to claim 1, characterized in that, In step 1, multiple single-antenna distributed access points are densely deployed in the task area, and a single-antenna UAV is configured. In the downlink, the cluster of distributed access points performs joint radio frequency energy transmission to the UAV; in the uplink, the UAV uses the collected energy to send communication data to the cluster of distributed access points, which is then globally decoded by the central processing unit.

3. The cellless UAV communication resource allocation method for wireless power transfer according to claim 1, characterized in that, In step 2, two non-overlapping phases are set within each time step. The first time period is the downlink wireless energy collection time slot, and the second time period is the uplink data transmission time slot. The time slot allocation ratio is adaptively adjusted according to the remaining energy of the UAV, the amount of data to be transmitted, and the equivalent channel gain between the distributed access point and the UAV.

4. The cellless UAV communication resource allocation method for wireless power transfer according to claim 1, characterized in that, In step 3, the path loss of the air-to-ground line-of-sight link is calculated based on the three-dimensional physical distance from each of the distributed access points to the UAV, thereby obtaining the channel gain; based on the downlink transmit power of each of the distributed access points and the channel gain, the total radio frequency energy received by the UAV in the downlink phase is calculated.

5. A cellless UAV communication resource allocation method for wireless power transfer according to claim 4, characterized in that, The uplink transmit power is used by the UAV to send communication data to the surrounding distributed access points. Each receiving distributed access point aggregates the signal through the fronthaul link to the central processing unit for global joint decoding. Based on the uplink transmit power of the UAV, the uplink channel gain between each distributed access point and the UAV, and the received noise power, the uplink received signal-to-interference-plus-noise ratio in the multi-access point cooperative reception mode is calculated, thereby obtaining the achievable transmission rate.

6. A cellless UAV communication resource allocation method for wireless power transfer according to claim 1, characterized in that, In step 4, the initial takeoff position, final arrival position, maximum flight speed, minimum flight altitude, and maximum flight altitude of the UAV are set as kinematic physical constraints. Furthermore, within each time step, the central processing unit updates the next trajectory point of the UAV using a continuous convex approximation method based on the current channel state information, the remaining energy of the UAV, and the amount of data to be transmitted, so that the updated trajectory takes into account both energy harvesting efficiency and uplink communication quality.

7. A cellless UAV communication resource allocation method for wireless power transfer according to claim 1, characterized in that, In step 5, the energy efficiency is defined as the ratio of the total number of data bits transmitted by the UAV to the system during the entire communication cycle to the total energy consumed by the system; the energy causality constraint is that the sum of the UAV's transmission energy consumption and circuit static energy consumption in the uplink data transmission time slot shall not exceed the sum of the total effective electrical energy collected in the downlink wireless energy collection time slot and the initial reserve power.

8. A cellless UAV communication resource allocation method for wireless power transfer according to claim 7, characterized in that, The alternating optimization algorithm is based on the idea of ​​block coordinate descent, which decomposes the original optimization problem into a joint optimization subproblem of time slot and power and a UAV trajectory optimization subproblem, and iterates alternately between the two subproblems; When the UAV trajectory is fixed, the time slot allocation ratio and each transmission power variable are optimized. When the time slot allocation ratio and each transmission power variable are fixed, the UAV three-dimensional trajectory is optimized. When the energy efficiency change between two adjacent iterations is less than a preset threshold or the maximum number of iterations is reached, the iteration is stopped, and a convergent resource allocation scheme that satisfies the constraints is output.

9. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When one or more programs are executed by the one or more processors, the one or more processors implement the cellless unmanned aerial vehicle communication resource allocation method for wireless power transmission as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, It stores executable instructions that, when executed by a processor, enable the processor to implement the cellless unmanned aerial vehicle communication resource allocation method for wireless power transfer as described in any one of claims 1-8.

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