A wireless energy-carrying communication network resource allocation optimization method

Abstract

The present invention discloses a method for optimizing resource allocation of a wireless energy-carrying communication network. The method comprises: determining the distribution characteristics and transmission protocols of power beacons and wireless nodes for the wireless energy-carrying communication network, wherein a time frame is divided into an idle phase and a transmission phase, in which an activated power beacon broadcasts an energy signal, and the wireless node captures energy from the energy broadcast signal, and in the transmission phase, the wireless node that has collected the required energy randomly selects a time slot to transmit information to its receiver, and the power beacon stops broadcasting the energy signal; under the set spatial capacity constraint, the energy consumption of each time frame is considered, and the energy minimization problem of the wireless energy-carrying communication network is set; the energy minimization problem is solved to determine the coverage of the optimized power beacon and the number of time slots allocated in the idle phase. The present invention can effectively reduce the total energy consumption of the wireless energy-carrying communication network while meeting the performance metric constraint.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and more specifically, to a method for optimizing resource allocation in a wireless energy-carrying communication network. Background Art

[0002] With the development of 6G communication and Internet of Things (IoT), wireless powered sensor networks (WPSNs) as an important component are attracting more and more attention. Wireless powered sensor networks are a new type of wireless communication. Different from traditional wireless communication that only transmits information, wireless powered sensor networks can transmit energy signals to wireless devices while transmitting traditional information wireless signals. After the energy signals are received by wireless devices with energy-harvesting circuits, they can be converted into wireless energy and stored in the batteries of the wireless devices themselves. The captured energy will be used for the energy consumption of the normal information interaction circuit of the wireless device and the energy capture circuit. The use of wireless powered sensor communication technology can reduce the cost of wires and wiring, and can avoid the trouble of replacing batteries for wireless devices.

[0003] Wireless energy communication network is the product of the combination of wireless energy transfer (WET) and wireless information transmission (WIT), and is expected to achieve the effect of "one plus one is greater than two". The combination of WPT and WIT technology is a manifestation of the essential properties of matter, which will further expand their respective application areas and bring profound changes to people's lives.

[0004] WET generally refers to electromagnetic energy transfer, which refers to the transmission of electrical energy without wires as a physical link. In a wireless power transmission system, a transmitter device driven by electricity from a power source generates a time-varying electromagnetic field that transmits energy wirelessly to a receiver device, which collects energy from the electromagnetic field and supplies it to a load circuit. Wireless power transmission technology can eliminate the dependence of electronic devices on wires and batteries, thereby increasing the mobility, convenience and safety of electronic devices. Wireless energy transmission is particularly useful for power devices where interconnecting wires is inconvenient, dangerous or impossible.

[0005] In a wireless energy-carrying communication network, wireless sensor nodes can collect energy from the surrounding environment or receive energy from energy transmitters for wireless communication. Wireless sensor nodes generally collect relevant data information, and then summarize these data through wireless communication for analysis and processing. However, due to the generally low efficiency of end-to-end wireless energy transmission between power beacons and wireless sensors, power beacons require higher transmission power or dense deployment to meet the energy needs of wireless sensors, which will lead to excessive energy consumption of the entire wireless energy-carrying communication network. In addition, with the rapid increase in the number of IoT devices in the future 6G network, this high energy consumption problem of power beacons may become more serious. Therefore, how to effectively reduce the energy consumption of energy supply equipment under the premise of maintaining sustainable communication of wireless sensors to achieve a green wireless energy-carrying communication network has become an urgent problem to be solved.

[0006] At present, most of the research on WPSNs is based on cellular communication networks or aims to optimize the performance of WPSNs. The optimization design based on cellular communication networks cannot be directly applied to WPSNs. The optimization schemes for wireless power-carrying communication networks usually focus on performance optimization, resource allocation to maximize network performance or throughput, or optimize channel estimation and power allocation to improve network arrival rate, etc., but they all ignore the high power consumption of power beacons (PBs) caused by the pursuit of performance optimization. Summary of the invention

[0007] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a method for optimizing resource allocation of a wireless energy-carrying communication network. The method comprises:

[0008] For wireless power-carrying communication networks, distribution characteristics of power beacons and wireless nodes and transmission protocols are determined, wherein a time frame is divided into an idle phase and a transmission phase. In the idle phase, an activated power beacon broadcasts an energy signal, and wireless nodes capture energy from the energy broadcast signal. In the transmission phase, a wireless node that has collected the required energy randomly selects a time slot to transmit information to its receiver, and the power beacon stops broadcasting the energy signal.

[0009] Under the set space capacity constraint, the energy consumption of each time frame is considered and the energy minimization problem of wireless energy-carrying communication network is set;

[0010] The energy minimization problem is solved to determine the coverage of the optimized power beacon and the number of time slots allocated in the idle phase.

[0011] Compared with the prior art, the advantage of the present invention is that it provides a resource allocation optimization design solution for a green wireless power-carrying communication network, which can solve the problem of excessive energy consumption of the wireless power-carrying communication network while meeting performance requirements, improve resource utilization and reduce energy consumption.

[0012] Further features and advantages of the present invention will become apparent from the following detailed description of exemplary embodiments of the present invention with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention.

[0014] Figure 1 is a flow chart of a method for optimizing wireless energy-carrying communication network resource allocation according to an embodiment of the present invention;

[0015] Figure 2 is a schematic diagram of a system model of a wireless energy-carrying communication network according to an embodiment of the present invention;

[0016] Figure 3 is a schematic diagram of a time frame-based transmission protocol according to an embodiment of the present invention;

[0017] Figure 4 is a schematic diagram of Monte Carlo simulation verification results according to an embodiment of the present invention;

[0018] Figure 5 4 is a schematic diagram of energy consumption comparison of a wireless energy-carrying communication network according to an embodiment of the present invention. DETAILED DESCRIPTION

[0019] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present invention unless otherwise specifically stated.

[0020] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.

[0021] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0022] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0023] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0024] The present invention designs a resource allocation optimization scheme for green wireless energy-carrying communication networks, which can effectively reduce the total energy consumption of WPSNs while meeting performance metric constraints. Figure 1 As shown, the provided wireless energy-carrying communication network resource allocation optimization method includes the following steps:

[0025] Step S110 , performing system modeling for the wireless power-carrying communication network to determine distribution characteristics of wireless nodes and power beacons.

[0026] In one embodiment, the wireless energy-carrying communication network is system modeled based on a Poisson point process.

[0027] See also Figure 2 As shown in Figure 1, a wireless power communication network with randomly deployed power beacons (PBs) and wireless nodes (WSs) is considered. By using the Poisson Point Process (PPP), the wireless nodes and power beacons are modeled as two independent homogeneous Poisson point processes. It is assumed that when there is a wireless node in a circular coverage area of ​​the power beacon, at the beginning of a time frame (e.g. Figure 3 ) activates the power beacon and broadcasts an energy signal to the surrounding area. Then, in the idle phase of a time frame, the wireless node captures energy from the energy signal broadcast by the surrounding power beacons so that it has enough energy to send information during the transmission phase. When the wireless node collects enough energy (such as enough to meet its information transmission needs), it will transmit information during the transmission phase. The wireless node that has collected enough energy will randomly select a time slot to transmit information during the transmission phase to avoid causing greater signal interference. Among them, since the wireless nodes currently used do not have the function of storing power, each time frame is independent of each other and does not affect each other.

[0028] Step S120: designing a transmission protocol used by the wireless power-carrying communication network based on the established system model.

[0029] To simplify the time frame-based network model, consider Figure 3The time frame-based transmission protocol shown. In this embodiment, a time frame (including T time slots) is divided into two phases, an idle phase and a transmission phase. In the idle phase, the activated power beacon broadcasts energy signals to the surroundings, and the wireless nodes capture energy from the surrounding energy broadcast signals so that they have enough energy to send information in the transmission phase. In the transmission phase, the wireless node that has collected enough energy randomly selects a time slot in the entire transmission phase to transmit information to its receiver. At this time, the power beacon stops broadcasting energy signals to reduce energy consumption and reduce channel interference with the transmission of information by the wireless node.

[0030] Step S130, based on the established system model, calculate the activation probability of the power beacon, the information transmission probability of the wireless node in the information transmission phase, and the information successful transmission probability of the wireless node in the information transmission phase.

[0031] Based on the system model and transmission protocol established above, the relevant information of the power beacon and the wireless node is calculated, including:

[0032] Step S131, calculating the activation probability of the power beacon

[0033] In the wireless energy-carrying communication network system, the power beacon senses whether there are wireless nodes in the circular coverage area with a radius of d around it. When there is at least one wireless node, the power beacon enters the activation state at the beginning of the frame, and then broadcasts the energy signal during the frame transmission phase. Based on this, the activation probability of the power beacon at the beginning of the frame can be obtained, which is expressed as:

[0034] ρ B =P[N W (A(X k , d))>0] (1)

[0035] Among them, N W (A(X k , d)) indicates that X k The number of wireless nodes in the circular area with d as the radius and centered at .

[0036] Step S132, calculating the information transmission probability of the wireless node

[0037] Since wireless nodes need to collect energy from the energy broadcast signal of the activated power beacon, they will transmit information in the frame transmission phase only after collecting enough energy. Therefore, it is necessary to first obtain all the energy collected by the wireless node in the frame idle phase, which is expressed as:

[0038]

[0039] Among them, Z irepresents the total energy collected by wireless node i during the frame idle phase, η is the energy collection efficiency, and X k is the coordinate of the kth power beacon PB-k, Y i is the coordinate of the ith wireless node WS-i, h ki is the channel model between PB-k and WS-i. The energy collected in each time slot is calculated first, and then the energy collected in all time slots during the frame idle phase is accumulated to obtain the total energy collected during the frame idle phase. N represents the number of time slots during the frame idle phase, t represents the time slot index, and P B represents the transmission power of the power beacon, λ B represents the density of power beacons in the Poisson point distribution, α represents the path loss exponent, Φ B Denote the density as λ B The distribution of a Poisson point process.

[0040] Based on the total energy collected by the wireless node in the frame idle phase, it is assumed that the energy collected by the wireless node can meet the information transmission requirements before it will choose to transmit information in the frame transmission phase. Therefore, the information transmission probability of the wireless node in the frame transmission phase can be obtained, which is expressed as:

[0041] ρ W =P(Z i ≥P W ) (3)

[0042] Among them, P W represents the transmit power of the wireless node (i.e. the energy required for information transmission). We already know the relevant expression of Z, and then we can obtain the Laplace transform of Z by applying the PGFL (probability generating functional) of the Poisson point process:

[0043]

[0044] Where N is the number of slots allocated during the idle phase of the frame, α is the path loss exponent, and λ B represents the density of power beacons in the Poisson point distribution, ρ B represents the activation probability of the power beacon, η represents the energy harvesting efficiency, and s represents the Laplace transform variable.

[0045] Thus, the closed expression of the wireless node information transmission probability can be obtained:

[0046]

[0047] in, is the gamma function, is the error function.

[0048] Step S133, calculating the probability of successful transmission of wireless node information

[0049] Based on the information transmission probability of wireless nodes, we can know how many wireless nodes will transmit information during the frame transmission phase. When the SINR received at the receiver of a wireless node with sufficient energy for information transmission is not less than the threshold β, the information transmission is considered successful. Based on this, we can get the conditional information transmission probability P of the wireless node under the condition of obtaining sufficient energy. suc，i_con and the probability of successful information transmission P suc，i as follows:

[0050]

[0051] Based on formula (6), the probability of successful transmission of wireless node information P has been obtained: suc，i . For the conditional information successful transmission probability P suc，i_con , we can first obtain the expression of SINRi:

[0052]

[0053] Among them, SINR i represents the signal interference noise ratio received at the receiver of the i-th wireless node, P W represents the transmission power of the wireless node, L is the distance between the wireless node and its receiver, and g ii (t) represents the channel model between the wireless node and its receiver in time slot t, Φ W (λ a ) represents the a is the Poisson point distribution of density, Y j and Y i are the coordinates of the i-th wireless node WS-i and the j-th wireless node WS-j, g ji (t) represents the channel model between the i-th wireless node WS-i and the j-th wireless node WS-j in time slot t, σ 2 represents the noise power and α is the path loss exponent.

[0054] The numerator is the information signal received at the receiver of the wireless node, and the denominator is the received interference information plus noise. Based on the expression of SINRi, the conditional information successful transmission probability P can be obtained. suc，i_con The closed expression of:

[0055]

[0056] Where: P W is the transmission power of the wireless node, L is the distance between the wireless node and its receiver, σ 2represents the noise power, α is the path loss index, β is a constant (indicating that when SINR exceeds this threshold, the information is considered to be sent successfully), and λ W represents the density of wireless nodes in the Poisson point distribution, T represents the total number of time slots in a frame, and ρ W represents the transmission probability of the wireless node, and ∞ represents positive infinity.

[0057] Combined with the obtained conditional information, the probability of successful transmission P suc，i_con And the probability of successful information transmission of wireless nodes, we can get the probability of successful information transmission P suc，i , expressed as:

[0058]

[0059] Step S140, taking the set space capacity as a constraint and considering the energy consumption in each time frame, designs the energy consumption minimization problem of the wireless energy-carrying communication network.

[0060] In the wireless power-carrying communication network, all energy sources are assumed to be the energy consumption of the power beacon. Therefore, the energy consumption problem of the wireless power-carrying communication network can be attributed to the energy consumption problem of the power beacon. The energy consumption of the power beacon is divided into two parts, one is the energy consumed by the power beacon in the activation stage, and the other is the energy consumed by the power beacon in the inactive stage. The energy consumption of the wireless power-carrying communication network is expressed as:

[0061] E(d,N)=λ B ρ B (P B +C1)N+λ B (1-ρ B )C0N+λ B C0(TN) (10)

[0062] Among them, λ B represents the density of power beacons in the Poisson point distribution, ρ B represents the activation probability of the power beacon, P B represents the transmission power of the power beacon, C1 represents the fixed energy consumption of the power beacon in the active state in each time slot, C0 represents the fixed energy consumption of the power beacon in the inactive state in each time slot, T represents the total number of time slots in a frame, and N represents the number of time slots allocated in the idle phase of the frame.

[0063] In addition, the concept of spatial capacity is introduced. For example, spatial capacity is defined as the number of wireless nodes that successfully transmit information in a unit space, expressed as:

[0064] S=λ W ·P suc_i (11)

[0065] The energy consumption of the wireless power-carrying communication network is the total energy consumed by all power beacons in each frame. Therefore, in one embodiment, the energy consumption minimization problem in each time frame is focused on. Under the spatial capacity constraint of the wireless node, the energy consumption minimization problem of the wireless power-carrying communication network is expressed as:

[0066]

[0067] Wherein, δ>0, δ represents a set threshold, and d represents the radius of a circular area covered by the power beacon.

[0068] Step S150, designing a resource allocation optimization solution based on the energy consumption minimization problem.

[0069] Based on the above analysis, by transforming the energy minimization problem, we find the equivalent problem of this problem:

[0070]

[0071] in, g1 is the only solution of erf(x)=g0, g0 is S(ρ W )=δ is the only solution.

[0072] Where E(d, N) represents the total energy consumption of a frame, ln() represents the logarithm, d represents the radius of the circular area covered by the power beacon, and λ B represents the density of power beacons in the Poisson point distribution, P B Indicates the power of the power beacon, P W represents the power of the wireless node, N represents the number of time slots allocated during the idle phase of the frame, and η represents the energy collection efficiency.

[0073] Based on the above equivalence problem, a solution algorithm is designed to solve the problem. See Table 1 below. The overall process of the algorithm is: iterate the time frame division variable N, process under each N, find the d corresponding to the minimum energy consumption under the current N, and then update N and d under the global minimum energy consumption, iterate in sequence, and finally find the optimal N and optimal d under the minimum energy consumption.

[0074] Table 1: Solution algorithm

[0075]

[0076] It should be noted that, without violating the spirit and scope of the present invention, those skilled in the art may make appropriate changes or modifications to the above embodiments. For example, wireless nodes and power beacons in a wireless power-carrying communication network may also adopt other distribution types (such as random distribution) or adopt other channel models to calculate SINR, etc.

[0077] In order to further verify the effect of the present invention, simulation experiments were conducted to obtain relevant experimental result data. In the experiment, when obtaining the probability of successful information transmission, the results obtained were analyzed and compared through Monte Carlo simulation experiments. Figure 4 As shown, the ordinate is the probability of successful transmission, and the abscissa is the number of time slots in the frame idle phase. It can be seen that the Monte Carlo simulation experiment has a high degree of fit with the theoretical analysis results. In addition, the energy consumption of the wireless energy-carrying communication network based on the present invention is compared with other standards. Figure 5 As shown, the ordinate represents energy consumption, the abscissa represents the density of wireless nodes in the Poisson point distribution, and other standards include energy consumption without optimizing d and N; energy consumption only optimizing d and energy consumption only optimizing N. As can be seen from FIG5, the present invention has a certain optimization effect compared with the prior art, and can effectively reduce the total energy consumption of the wireless energy-carrying communication network while meeting the performance metric constraints.

[0078] In summary, the present invention designs a resource allocation optimization scheme for a green wireless power-carrying communication network, which can effectively reduce the total energy consumption of the wireless power-carrying communication network while satisfying the performance metric constraints. The present invention focuses on the trade-off between performance and energy consumption in wireless power-carrying communication networks. By modeling the power beacon and the wireless node as two independent homogeneous Poisson point processes, a novel method of jointly optimizing the activation probability of the power beacon and the wireless information transmission operation cycle of the wireless node is proposed, and the problem of minimizing the energy consumption of the wireless power-carrying communication network is simplified under the constraint of the probability of successful information transmission of the wireless node. An effective algorithm is proposed to solve the problem, which can be applied to large-scale wireless power-carrying communication networks to minimize energy consumption.

[0079] The present invention may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present invention.

[0080] Computer readable storage medium can be a tangible device that can hold and store instructions used by an instruction execution device. Computer readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (non-exhaustive list) of computer readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, for example, a punch card or a convex structure in a groove on which instructions are stored, and any suitable combination thereof. The computer readable storage medium used here is not interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated by a waveguide or other transmission medium (for example, a light pulse by an optical fiber cable), or an electrical signal transmitted by a wire.

[0081] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0082] The computer program instructions for performing the operation of the present invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, Python, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present invention.

[0083] Various aspects of the present invention are described herein with reference to the flow charts and / or block diagrams of the methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each box of the flow chart and / or block diagram and the combination of each box in the flow chart and / or block diagram can be implemented by computer-readable program instructions.

[0084] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0085] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0086] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a part of a module, a program segment or an instruction, and a part of the module, a program segment or an instruction contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions. It is well known to those skilled in the art that it is equivalent to implement it by hardware, implement it by software, and implement it by combining software and hardware.

[0087] Embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the marketplace, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the present invention is defined by the appended claims.

Claims

1. A method for optimizing resource allocation in a wireless energy-carrying communication network, comprising the following steps: For wireless power-carrying communication networks, distribution characteristics of power beacons and wireless nodes and transmission protocols are determined, wherein a time frame is divided into an idle phase and a transmission phase. In the idle phase, an activated power beacon broadcasts an energy signal, and wireless nodes capture energy from the energy broadcast signal. In the transmission phase, a wireless node that has collected the required energy randomly selects a time slot to transmit information to its receiver, and the power beacon stops broadcasting the energy signal. Under the set spatial capacity constraint, considering the energy consumption of each time frame, the energy minimization problem of the wireless energy-carrying communication network is set as: Solving the energy minimization problem to determine optimized d and N; Where E(d, N) represents the energy consumption of the wireless energy-carrying communication network, δ is a constant set to be greater than 0, the spatial capacity S represents the number of wireless nodes that successfully transmit information in a unit space, d represents the radius of the circular area covered by the power beacon, N represents the number of time slots in the idle phase within a time frame, and T represents the total number of time slots within a time frame; Wherein, the space capacity S is expressed as: S=λ W ·P suc_i Among them, P suc，i represents the probability of successful information transmission of wireless nodes, λ W Indicates the density of wireless nodes; The probability of successful transmission of information of the wireless node is expressed as: P suc，i =P{SINR≥β,Z i ≥P W } =P{SINR≥β|Z i ≥P W }·P{Z i ≥P W } =P suc，i_con ·r W Among them, SINR represents the signal-to-interference-noise ratio received at the receiver of the wireless node, p W represents the transmission power of the wireless node, Z i Indicates that wireless node i collects all the energy in the idle phase, β is a set constant, ρ W is the probability of wireless node information transmission; The information transmission probability of the wireless node is expressed as: in, is the gamma function, is the error function, λ B is the density of power beacons, η represents the energy harvesting efficiency, P B represents the transmission power of the power beacon, α represents the path loss exponent, and ρ B represents the activation probability of the power beacon; Wherein, solving the energy minimization problem to determine the optimized d and N comprises the following steps: The energy minimization problem is transformed into an equivalent problem: in, g1 is the only solution of erf(x)=g0, g0 is S(ρ W )=the only solution of δ; Iterate the variable N, process it at each N, find the d corresponding to the minimum energy consumption under the current N, then update N and d under the global minimum energy consumption, and then find the optimal N and optimal d under the minimum energy consumption.

2. The method according to claim 1, characterized in that The energy consumption of the wireless energy-carrying communication network is expressed as: E(d,N)=λ B r B (P B +C1)N+λ B (1-p B )C0N+λ B C0(TN) Among them, λ B The density of power beacons, ρ B represents the activation probability of the power beacon, P B represents the transmission power of the power beacon, C1 represents the fixed energy consumption of the power beacon in the active state in each time slot, and C0 represents the fixed energy consumption of the power beacon in the inactive state in each time slot.

3. The method according to claim 1, characterized in that The wireless nodes and power beacons are modeled as two independent homogeneous Poisson point processes using the Poisson point process to determine the distribution characteristics of the power beacons and wireless nodes.

4. The method according to claim 1, characterized in that: Wireless node i collects all energy Z in the idle phase i It is expressed as: Where η is the energy collection efficiency, X k is the coordinate of the kth power beacon PB-k, Y i is the coordinate of the ith wireless node WS-i, h ki is the channel model between PB-k and WS-i, λ B represents the density of power beacons, α represents the path loss exponent, Φ B Denote the density as λ B The distribution of B Indicates the activation probability of the power beacon.

5. A computer-readable storage medium having a computer program stored thereon, wherein: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

6. A computer device comprising a memory and a processor, wherein a computer program capable of being run on the processor is stored in the memory, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

Images