Information transmission optimization method and system of wireless power supply communication network

Abstract

The application discloses a kind of information transmission optimization method and system of wireless power supply communication network, the technical scheme solving the energy utility maximization model corresponding to wireless power supply communication network WPCN in actual scene, obtain the optimal value of the optimization parameter of wireless power supply communication network WPCN in the actual scene;Adjust the optimization parameter of wireless power supply communication network in the actual scene to the optimal value;The optimization parameter includes: the transmission power P of mixed access point HAP in wireless power supply communication network WPCN HAP , the duration T of energy collection EH stage eh , the transmission power vector P of each wireless node WN, the duration T of information transmission stage tx And the transmission power P of Internet of Things IoT device d ;In the process of adjusting optimization parameter, each wireless node WN in the wireless power supply communication network WPCN is stimulated to supply power for Internet of Things IoT device by energy incentive mechanism, improve the energy utility of IoT.

Description

Technical Field

[0001] This invention relates to the field of network optimization design, and specifically to a method and system for optimizing information transmission in a wireless power supply communication network. Background Technology

[0002] In recent years, with the rapid development of the Internet of Things (IoT), many real-time status update applications have emerged, such as autonomous driving, inventory forecasting, weather monitoring, and human health monitoring. These applications are highly sensitive to the freshness of information and have high requirements for information timeliness. Since the latest status update information is crucial for real-time online systems to make correct decisions, how to transmit status update information to target nodes as quickly as possible has attracted widespread attention from academia and industry.

[0003] To characterize the freshness of information, the performance metric of information age (AoI) has been proposed. AoI primarily refers to the time elapsed from the generation of the latest state update information to its reception by the destination node. Unlike traditional network performance metrics that use latency and throughput, AoI is powerful in describing the "timeliness" of information and can more accurately characterize the freshness of updated information. Destination nodes prefer to receive the freshest updates possible, i.e., the younger the information age. To date, the design of wireless systems based on AoI has rapidly attracted attention.

[0004] On the other hand, in most IoT networks that support state-update applications, small IoT devices are powered by batteries with limited capacity. Manually replacing and charging batteries can result in significant labor costs, especially in large-scale deployments or in toxic and harsh environments. To address this issue, energy harvesting (EH) technology has emerged. Compared to natural energy sources like wind and solar power, powering devices using radio-frequency (RF) energy offers greater controllability and can provide a stable power supply for low-power IoT devices under various physical conditions and environments. Therefore, RF-based EH has been extensively studied in various IoT scenarios within academia. Furthermore, the successful establishment of a hardware prototype for a Power over Wireless Communication Network (WPCN) demonstrates the practicality of RF-based EH systems.

[0005] Given the enormous potential of RF-based EH (Environmentally Optimal Heating) and its applicability to various energy-constrained IoT applications, and considering that data freshness is a crucial performance indicator for real-time status update IoT systems, designing an AoI-based WPCN (Power-Only Network Controller) for future real-time status update IoT systems is expected to simultaneously address the issues of energy supply and information freshness requirements.

[0006] Previous research has assumed that even if wireless nodes (WNs) are energy-limited, surrounding WNs are always willing to charge low-power IoT devices. However, in reality, WNs are powered by batteries with limited capacity or by a stable, paid energy supply, and therefore may be highly selfish, unwilling to expend their own resources to charge other nodes. Thus, an effective incentive mechanism is needed to encourage selfish WNs to charge neighboring low-power IoT devices. Furthermore, existing research has only discussed simple two- or three-node network models, but in practice, IoT systems often deploy a large number of WNs, necessitating research into multi-node network models. Summary of the Invention

[0007] To address the aforementioned shortcomings in existing technologies, this invention proposes an effective incentive mechanism for multi-node WPCN based on AoI, which overcomes the selfishness of wireless networks and improves AoI performance through energy incentives.

[0008] This invention provides a method for optimizing information transmission in a wireless power supply communication network, comprising:

[0009] Solve the energy utility maximization model corresponding to the Power Supply Communication Network (WPCN) in the actual scenario, and obtain the optimal values ​​of the optimization parameters of the WPCN in the actual scenario.

[0010] Adjust the optimization parameters of the wireless power supply communication network in the actual scenario to the optimal value;

[0011] The optimization parameters include: the transmission power P of the hybrid access point (HAP) in the Power over Wireless Communication Network (WPCN). HAP The duration T of the energy harvesting EH phase eh The transmit power vector P of each wireless node WN, and the duration T of the information transmission phase. tx and the transmission power P of IoT devices d .

[0012] Preferably, the construction of the energy utility maximization model includes:

[0013] With the goal of minimizing the information age (AoI) and energy required for each data packet when each wireless node (WN) transmits each data packet, an AoI energy utility function is constructed.

[0014] The transmission power constraints of each wireless node WN are constructed based on the energy deduction rate of each wireless node WN in the AoI energy utility function.

[0015] Furthermore, the construction of the AoI energy utility function includes:

[0016] The duration T of the energy harvesting EH phase eh The duration T of the information transmission phase tx The sum of these values ​​serves as the information age (AoI) for each data packet.

[0017] The Energy Harvesting (EH) phase is divided into a first sub-phase where the Hybrid Access Point (HAP) charges all Wireless Nodes (WN) and IoT devices via broadcast, and a second sub-phase where the HAP, together with each Wireless Node (WN), charges the IoT devices.

[0018] The sum of the transmission power of the hybrid access point (HAP) in the first sub-phase and the second sub-phase is used as the energy required to transmit each data packet.

[0019] The AoI energy utility function is constructed based on a predetermined reference utility value, the information age (AoI) of each data packet, and the energy required to transmit each data packet.

[0020] Furthermore, the AoI energy utility function is shown in the following equation:

[0021]

[0022] In the formula: F represents the energy utility function of AoI; P HAP Indicates the transmission power of the HAP device; T tx Indicates the duration of the information transmission phase; T eh Indicates the duration of the energy harvesting (EH) phase; T eh,1 T represents the time interval in the first sub-phase; eh,2 This indicates the time interval in the second sub-stage; α is a constant representing a predetermined reference utility value; α is the unit conversion scaling factor for the information age (AoI) of each data packet; and β is the unit conversion scaling factor for energy.

[0023] Preferably, the transmission power constraint of each wireless node WN is as shown in the following formula:

[0024]

[0025] In the formula: P m θ represents the transmission power of the m-th wireless node WN; m∈(0,1) represents the energy deduction rate of the m-th wireless node WN, where the m-th wireless node WN deducts θ. m E m The energy it uses serves as the incentive to charge IoT devices; E m This indicates that the m-th wireless node WN is in T eh,1 Energy collected from the hybrid access point (HAP) within a given time period This represents the set of all wireless nodes WN.

[0026] Furthermore, the construction of the energy utility maximization model also includes:

[0027] The following constraints are constructed for the AoI energy utility function:

[0028] T tx P d =B s

[0029] In the formula: P d Indicates the transmission power of IoT devices; T tx Indicates the duration of the information transmission phase; B s This indicates the energy caching capacity of Internet of Things (IoT) devices;

[0030]

[0031] In the formula: W is the system bandwidth, h a,d The channel coefficient represents the wireless link between the Hybrid Access Point (HAP) and the Internet of Things (IoT) devices; N0 is the noise spectral density; L represents the length of the data packet for each state update.

[0032] T eh,1 +T eh,2 ≥T tx

[0033] In the formula: T eh,1 T represents the time interval in the first sub-phase; eh,2 This indicates the time interval in the second sub-stage;

[0034] E eh,1 +E eh,2 =B s

[0035] In the formula: E eh,1 E represents the energy collected by IoT devices in the first sub-phase. eh,2 This represents the energy collected by IoT devices in the second sub-phase.

[0036] E eh,1 =T eh,1 min{η eh PHAP |h a,d | 2 M eh}

[0037] In the formula: η eh ∈(0,1] represents the energy harvesting efficiency, P HAP Indicates the transmission power of the HAP device; M eh Indicates the saturation value of the RF-EH circuit;

[0038]

[0039] In the formula: P m h represents the transmission power of the m-th wireless node WN; m,d The channel coefficient represents the wireless link between the m-th wireless node WN and the IoT device; M represents the total number of wireless nodes WN. This represents the set of all wireless nodes WN;

[0040]

[0041] In the formula: This indicates the maximum available transmit power of the hybrid access point (HAP). This represents the maximum available transmit power of the m-th wireless node WN;

[0042]

[0043] In the formula: This indicates the maximum available transmit power of an Internet of Things (IoT) device.

[0044] Preferably, the step of solving the energy utility maximization model corresponding to the Power over Wireless Communication Network (WPCN) in the actual scenario, and obtaining the optimal values ​​of the optimization parameters of the WPCN in the actual scenario, includes:

[0045] Solving for the duration T of the information transmission phase based on the energy conservation relationship during information transmission by IoT devices. tx The optimal value and transmission power P of IoT devices d The optimal value;

[0046] The duration T of the information transmission phase tx The optimal value and transmission power P of IoT devices d Substituting the optimal value into the energy utility maximization model, we obtain the zeroth intermediate energy utility maximization model P. A-0 ;

[0047] The zeroth intermediate energy utility maximization model P is analyzed using the Lagrange method. A-0The transmission power P of the hybrid access point (HAP) is obtained by solving the problem. HAP The optimal value, the time interval T of the first sub-stage eh,1 The optimal value and the time interval T between the second sub-stage eh,2 The optimal value;

[0048] Based on the transmission power P of the hybrid access point (HAP) HAP The optimal value, the time interval T of the first sub-stage eh,1 The optimal value and the time interval T between the second sub-stage eh,2 The optimal value of the transmit power vector P of each wireless node WN is calculated.

[0049] Wherein, the time interval T of the first sub-stage eh,1 The time interval T between the second and third sub-phases eh,2 The duration T of the energy harvesting EH phase eh .

[0050] Furthermore, the transmission power P of the IoT device d The optimal value is calculated using the following formula:

[0051]

[0052] In the formula: For the transmission power P of IoT devices d The optimal value; B s This indicates the energy caching capacity of Internet of Things (IoT) devices; It's the Lambert function. W is the system bandwidth, h a,d The channel coefficient represents the wireless link between the Hybrid Access Point (HAP) and the Internet of Things (IoT) devices; N0 is the noise spectral density; L represents the length of the data packet for each state update. For the transmission power P of IoT devices d The maximum value;

[0053] The duration T of the information transmission phase tx The optimal value is calculated using the following formula:

[0054]

[0055] In the formula: The duration T of the information transmission phase tx The optimal value.

[0056] Preferably, the Lagrange method is used to maximize the zeroth intermediate energy utility model P. A-0 The transmission power P of the hybrid access point (HAP) is obtained by solving the problem.HAP The optimal value, the time interval T of the first sub-stage eh,1 The optimal value and the time interval T between the second sub-stage eh,2 The optimal values ​​include:

[0057] When the energy collected by the IoT device from the hybrid access point (HAP) and each wireless node (WN) is less than or equal to the RF-EH circuit saturation value, then the energy E collected by the IoT device in the second sub-phase... eh,2 This equals the energy power collected by IoT devices from the hybrid access point (HAP) and each wireless node (WN), and is substituted into the zeroth intermediate energy utility maximization model P. A-0 The first intermediate energy utility maximization model P is obtained. A-1 ;

[0058] In the first intermediate energy utility maximization model P A-1 By introducing auxiliary variables and solving using the Lagrange method, the transmission power P of the hybrid access point (HAP) is obtained. HAP The optimal value, the time interval T of the first sub-stage eh,1 The optimal value and the time interval T between the second sub-stage eh,2 The optimal value;

[0059] When the energy collected by the IoT device from the hybrid access point (HAP) and each wireless node (WN) exceeds the saturation value of the RF-EH circuit, then the energy E collected by the IoT device in the second sub-phase... eh,2 This is equal to the saturation value of the RF-EH circuit, and substituted into the zeroth intermediate energy utility maximization model P. A-0 The third intermediate energy utility maximization model P is obtained. A-3 ;

[0060] In the third intermediate energy utility maximization model P A-3 By introducing auxiliary variables and solving using the Lagrange method, the transmission power P of the hybrid access point (HAP) is obtained. HAP The optimal value, the time interval T of the first sub-stage eh,1 The optimal value and the time interval T between the second sub-stage eh,2 The optimal value.

[0061] Furthermore, in the first intermediate energy utility maximization model P A-1 By introducing auxiliary variables and solving using the Lagrange method, the transmission power P of the hybrid access point (HAP) is obtained. HAP The optimal value, the time interval T of the first sub-stage eh,1 The optimal value and the time interval T between the second sub-stage eh,2 The optimal values ​​include:

[0062] In the first intermediate energy utility maximization model P A-1 By introducing auxiliary variables, a second solvable energy utility maximization model P is obtained. A-2 ;

[0063] The second solvable energy utility maximization model P is solved using the Lagrange method. A-2 The second solvable energy utility maximization model P is obtained. A-2 The time interval T in the first sub-phase eh,1 The optimal value of the auxiliary variable, and the optimal solution of the auxiliary variable;

[0064] Using the second solvable energy utility maximization model P A-2 The time interval T in the first sub-phase eh,1 The optimal value of the variable and the optimal solution of the auxiliary variable are used to obtain the transmission power P of the hybrid access point (HAP). HAP The optimal value, the time interval T of the first sub-stage eh,1 The optimal value and the time interval T between the second sub-stage eh,2 The optimal value.

[0065] Furthermore, the second solvable energy utility maximization model P is used... A-2 The time interval T in the first sub-phase eh,1 The optimal value of the variable and the optimal solution of the auxiliary variable are used to obtain the transmission power P of the hybrid access point (HAP) as shown in the following formula. HAP The optimal value, the time interval T of the first sub-stage eh,1 The optimal value and the time interval T between the second sub-stage eh,2 The optimal value:

[0066]

[0067] In the formula: Transmission power P for Hybrid Access Point (HAP) HAP The optimal value; The time interval T for the first sub-stage eh,1 The optimal value; The time interval T for the second sub-stage eh,2 The optimal value; B s Indicates the energy caching capability of IoT devices; η eh Indicates energy harvesting efficiency; h a,d This represents the channel coefficient of the wireless link between the hybrid access point (HAP) and IoT devices. θ represents the maximum available transmit power of the m-th wireless node WN; m h represents the energy deduction rate of the m-th wireless node WN; a,mThe channel coefficients are the wireless link coefficients between the hybrid access point HAP and the m-th wireless node WN. M represents the total number of wireless nodes (WN); h m,d This represents the channel coefficient of the wireless link between the m-th wireless node WN and the IoT device. The duration T of the information transmission phase tx The optimal value; Transmission power P for Hybrid Access Point (HAP) HAP The maximum value; For the second solvable energy utility maximization model P A-2 The time interval T in the first sub-phase eh,1 The optimal value; The optimal solution for the auxiliary variables.

[0068] Preferably, in the third intermediate energy utility maximization model P A-3 By introducing auxiliary variables and solving using the Lagrange method, the transmission power P of the hybrid access point (HAP) is obtained. HAP The optimal value, the time interval T of the first sub-stage eh,1 The optimal value and the time interval T between the second sub-stage eh,2 The optimal values ​​include:

[0069] In the third intermediate energy utility maximization model P A-3 By introducing auxiliary variables, we obtain the fourth solvable energy utility maximization model P. A-4 ;

[0070] The fourth solvable energy utility maximization model P is solved using the Lagrange method. A-4 The fourth solvable energy utility maximization model P is obtained. A-4 The time interval T in the first sub-phase eh,1 The optimal value and the time interval T in the first sub-stage eh,2 The optimal value;

[0071] Using the fourth solvable energy utility maximization model P A-4 The time interval T in the first sub-phase eh,1 The optimal value and the time interval T in the first sub-stage eh,2 The optimal value is obtained to determine the transmission power P of the hybrid access point (HAP). HAP The optimal value, the time interval T of the first sub-stage eh,1 The optimal value and the time interval T between the second sub-stage eh,2 The optimal value.

[0072] Furthermore, the fourth solvable energy utility maximization model P is used...A-4 The time interval T in the first sub-phase eh,1 The optimal value and the time interval T in the first sub-stage eh,2 The optimal value is obtained by calculating the transmission power P of the hybrid access point (HAP) as shown in the following formula. HAP The optimal value, the time interval T of the first sub-stage eh,1 The optimal value and the time interval T between the second sub-stage eh,2 The optimal value:

[0073]

[0074] In the formula: Transmission power P for Hybrid Access Point (HAP) HAP The optimal value; The time interval T for the first sub-stage eh,1 The optimal value; The time interval T for the second sub-stage eh,2 The optimal value; B s This indicates the energy caching capacity of Internet of Things (IoT) devices; The duration T of the information transmission phase tx The optimal value; M eh Indicates the saturation value of the RF-EH circuit; η eh Indicates energy harvesting efficiency; h a,d This represents the channel coefficient of the wireless link between the hybrid access point (HAP) and IoT devices. Transmission power P for Hybrid Access Point (HAP) HAP The maximum value; For the fourth solvable energy utility maximization model P A-4 The time interval T in the first sub-phase eh,1 The optimal value; For the fourth solvable energy utility maximization model P A-4 The time interval T in the first sub-phase eh,2 The optimal value.

[0075] Preferably, the transmission power P based on the hybrid access point (HAP) HAP The optimal value, the time interval T of the first sub-stage eh,1 The optimal value and the time interval T between the second sub-stage eh,2 The optimal value of the transmit power vector P of each wireless node WN is calculated using the following formula:

[0076]

[0077]

[0078]

[0079] In the formula: P opt The optimal value of the transmit power vector P for each wireless node WN; Transmission power P for Hybrid Access Point (HAP) HAP The optimal value; The time interval T for the first sub-stage eh,1 The optimal value; The time interval T for the second sub-stage eh,2 The optimal value of η; eh Indicates energy harvesting efficiency; h a,m M represents the channel coefficient of the wireless link between the hybrid access point HAP and the m-th wireless node WN; eh Indicates the saturation value of the RF-EH circuit; This represents the set of all wireless nodes WN; θ represents the optimal value of the WN transmission power of the m-th wireless node; m This represents the energy deduction rate of the m-th wireless node WN; This indicates that the m-th wireless node WN is in T eh,1 The optimal energy collected from the hybrid access point (HAP) within a given time period.

[0080] Based on the same inventive concept, the present invention also provides an information transmission optimization system for a wireless power supply communication network, used to implement the aforementioned information transmission optimization method for a wireless power supply communication network, comprising:

[0081] The solution module is used to solve the energy utility maximization model corresponding to the Power Supply Communication Network (WPCN) in the actual scenario, and obtain the optimal values ​​of the optimization parameters of the WPCN in the actual scenario.

[0082] An adjustment module is used to adjust the optimization parameters of the wireless power supply communication network in the actual scenario to the optimal value;

[0083] The optimization parameters include: the transmission power P of the hybrid access point (HAP) in the Power over Wireless Communication Network (WPCN). HAP The duration T of the energy harvesting EH phase eh The transmit power vector P of each wireless node WN, and the duration T of the information transmission phase. tx and the transmission power P of IoT devices d .

[0084] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0085] The technical solution provided by this invention solves the energy utility maximization model corresponding to the Power over Wireless Communication Network (WPCN) in a real-world scenario, obtains the optimal values ​​of the optimization parameters of the WPCN in the real-world scenario, and adjusts the optimization parameters of the WPCN in the real-world scenario to the optimal values. The optimization parameters include: the transmission power P of the hybrid access point (HAP) in the WPCN. HAP The duration T of the energy harvesting EH phase eh The transmit power vector P of each wireless node WN, and the duration T of the information transmission phase. tx and the transmission power P of IoT devices d During the parameter adjustment and optimization process, an energy incentive mechanism is used to incentivize each wireless node (WN) in the wireless power supply communication network (WPCN) to supply power to IoT devices, thereby improving the energy efficiency of IoT. Attached Figure Description

[0086] Figure 1 This is a flowchart illustrating the optimized information transmission process of the wireless power supply communication network in this embodiment.

[0087] Figure 2 This is a schematic diagram of the WPCN (Powered Wireless Communication Network) network model in this embodiment;

[0088] Figure 3 This is a schematic diagram of a single state update information transmission process in this embodiment. Detailed Implementation

[0089] To better understand this invention, the following description, in conjunction with the accompanying drawings and examples, will further illustrate the invention.

[0090] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0091] The core idea of ​​the Wireless Powered Communication Networks (WPCNs) involved in this embodiment is to use power base stations to wirelessly replenish the power of terminals, thereby extending the terminal's lifespan. In WPCNs, power base stations, i.e., nodes, only focus on their own interests and are unwilling to provide cooperative forwarding services to other nodes at the expense of their limited resources without any incentive. This selfish behavior of nodes is defined as node selfishness. The emergence of node selfishness reduces data transmission efficiency and network throughput, thus seriously affecting the network performance of the entire system and restricting the further development of wireless multi-hop networks. Therefore, how to effectively address node selfishness in the network to reduce its negative impact and improve network performance is currently a key research focus. Furthermore, since there is no existing WPCN based on AoI that includes multiple selfish WNs, this embodiment proposes an effective incentive mechanism for multi-node WPCNs based on AoI to fill this gap, overcoming the selfishness of wireless networks and improving AoI performance through energy incentives. By defining the AoI energy utility function and constructing an optimization problem to maximize the AoI energy utility value of a HAP-IoT device pair, a closed-form solution is derived and the optimal solution is obtained by using equality constraint elimination and the Lagrange method. In addition, this embodiment shows that the multi-node network model is more practical than the two-node or three-node network model.

[0092] like Figure 1 As shown, the information transmission optimization method for wireless power supply communication networks provided in this embodiment mainly includes the following steps:

[0093] S1 solves the energy utility maximization model corresponding to the Power Supply Communication Network (WPCN) in the actual scenario to obtain the optimal values ​​of the optimization parameters of the WPCN in the actual scenario.

[0094] S2 adjusts the optimization parameters of the wireless power supply communication network in the actual scenario to the optimal value;

[0095] The optimization parameters include: the transmission power P of the hybrid access point (HAP) in the Power over Wireless Communication Network (WPCN). HAP The duration T of the energy harvesting EH phase eh The transmit power vector P of each wireless node WN, and the duration T of the information transmission phase. tx and the transmission power P of IoT devices d .

[0096] The process of adjusting and optimizing parameters in this embodiment can maximize the energy efficiency of outdoor wireless energy harvesting driven networks.

[0097] To better understand the technical solution provided in this implementation, the basic idea of ​​the technical solution will be introduced first: such as Figure 2 The illustrated multi-node wireless power supply communication network (WPCN) consists of one hybrid access point (HAP), one IoT device, and M wireless network nodes (WNs). The IoT device senses its surrounding physical environment and wishes to transmit this information to the HAP. Since the IoT device is energy-limited and equipped with an RF-based energy storage module (EH), it requires charging first. An "energy harvesting and then transmitting" protocol is employed, and the IoT device is equipped with a small-capacity energy buffer or capacitor. Both the HAP and WNs can charge the IoT device via wireless power transfer (WPT). The network topology and transmission strategy in this embodiment can adopt a commercial battery-free WPT prototype system, which has been widely adopted in the literature and is therefore very close to practical applications. For example, Powercast's wireless power supply sensor network prototype, Powerharvester, integrates low-power IoT nodes with temperature and humidity sensors and an energy capacitor. It can harvest energy from an RF energy transmitter and then use the harvested energy to collect heat and humidity data and transmit it to a small data receiver. It performs energy and information transmission in orthogonal channels (e.g., different frequency bands) to avoid interference. Therefore, IoT devices can harvest energy while sending status updates. Once their energy buffer is full, the IoT device will generate a status update and immediately use all the energy in the buffer for transmission.

[0098] In WPCN, since IoT devices can only generate and send state update packets when their energy buffers are fully charged, there is a causal relationship between energy harvesting and energy use. This is significantly different from traditional wireless networks where nodes have a fixed power supply. This results in the original long-term average AoI failing to accurately reflect the energy constraints of each packet transmission. Therefore, the long-term average AoI is unsuitable for the WPCN system considered in this embodiment. Since a trade-off must be made between energy transmission and information freshness for each state update packet, minimizing the AoI of each state update packet is crucial. To effectively characterize the AoI performance associated with each packet in WPCN, as an alternative, the AoI is arranged and optimized for each state update packet; to distinguish it from the original long-term average AoI value, it is referred to as "per-packet AoI".

[0099] In this energy incentive scheme, the HAP (Heated Power Access Point) first wirelessly provides some energy to the WN (Wireless Network) and IoT devices. The WN deducts a portion of the collected energy as an incentive and wirelessly charges the IoT devices with the remaining energy. In practical IoT systems, multiple WNs may typically exist in the surrounding environment. Considering the broadcast nature of wireless channels, the energy signal emitted by the HAP can also be collected by surrounding WNs. Therefore, the energy collected by these WNs can also be used to charge IoT devices, improving system performance. However, due to the currently low efficiency of wireless energy transfer, it is more suitable for near-field applications within a radius of a few meters, such as smart greenhouse monitoring, smart industrial monitoring, health monitoring, and smart museum monitoring.

[0100] In this embodiment, an AoI energy utility function is defined, and an optimization problem is proposed to maximize the network model. Figure 2 Energy efficiency value of AoI for HAP-IoT devices.

[0101] In one implementation, the energy utility maximization model in S1 can be constructed using the following steps:

[0102] With the goal of minimizing the information age (AoI) and energy required for each data packet when each wireless node (WN) transmits each data packet, an AoI energy utility function is constructed.

[0103] The transmission power constraints of each wireless node WN are constructed based on the energy deduction rate of each wireless node WN in the AoI energy utility function.

[0104] In one implementation, the constructed AoI energy utility function is shown in the following equation:

[0105]

[0106] Among them, P HAP and P d These represent the transmission power of the HAP and IoT devices, respectively.

[0107] This represents the transmit power vector of M WN vectors; [·] T This represents the matrix transpose operation; It is a constant representing a predetermined reference utility value. α > 0 is the unit conversion scaling factor for AoI, which will... Converted to utility; β > 0 is the energy unit conversion factor, which converts P... HAP (T eh,1 +T eh,2 This can be converted into utility. It can be seen that the smaller the AoI and energy required for each data packet, the larger the output value of the utility function.

[0108] like Figure 3The operation of transmitting each state update packet, as shown, includes two time phases: the EH phase (duration T). eh ) and information transmission phase (duration T) tx The EH phase consists of two sub-phases, in which the time interval is T in the first sub-phase. eh,1 HAP charges WN and IoT devices via broadcast signals, while in the second sub-phase, the time interval is T. eh,2 HAP, together with WN, charges IoT devices, therefore, T eh =T eh,1 +T eh,2 .

[0109] In one specific implementation, the energy utility maximization model is constructed as shown in the following equation:

[0110]

[0111] stT tx P d =B s ,

[0112]

[0113] T eh,1 +T eh,2 ≥T tx ,

[0114]

[0115] E eh,1 +E eh,2 =B s ,

[0116] E eh,1 =T eh,1 min{η eh P HAP |h a,d | 2 M eh},

[0117]

[0118]

[0119]

[0120] B s This indicates the energy buffering capacity of IoT devices, measured in joules (h). a,m This represents the channel coefficient of the wireless link between the HAP and the m-th WN. a,dThis represents the channel coefficient of the wireless link between the HAP and the IoT device. m,d E represents the channel coefficient of the wireless link between the m-th WN and the IoT device. m This indicates that the m-th WN is in T eh,1 Energy collected from HAP within a time period Where h a,m η represents the channel coefficient of the wireless link between the HAP and the m-th WN. eh ∈(0,1] represents the energy harvesting efficiency, M eh This represents the saturation value of the RF-EH circuit. θ m ∈(0,1) represents the deduction rate of the m-th WN, where the m-th WN is deducted by θ. m E m It uses its energy as an incentive to charge IoT devices. m represents the m-th WN, Let represent the set of all M WNs. Let P represent the maximum available transmit power of the HAP, the m-th WN, and the IoT device, respectively. m This represents the transmission power of the m-th WN. W is the system bandwidth, and N0 is the noise spectral density. L represents the length of the data packet for each state update, in bits.

[0121] The objective function of this energy utility maximization model is the system's AoI energy utility function, and the optimization objective is to select a set of parameters (P) HAP ,T eh,1 ,T eh,2 ,P,T tx ,P d This allows the system's AoI energy utility function to reach its maximum value.

[0122] To better understand the constraints in the energy utility maximization model, further explanation is provided:

[0123] Among them, T tx P d =B s This indicates that the IoT device has enough stored energy to transmit the current status update information;

[0124] This indicates that the information transmission capacity of the IoT device is greater than the amount of data in this status update, and that the information transmission can be completed within the specified time.

[0125] T eh,1 +T eh,2 ≥T tx This indicates that the energy harvesting phase is longer than the data transmission phase, ensuring the system continues to operate smoothly.

[0126] Eeh,1 +E eh,2 =B s This means that the energy collected by the IoT device this time can be stored in its entirety, without exceeding the energy cache capacity of the IoT device.

[0127] E eh,1 =T eh,1 min{η eh P HAP |h a,d | 2 M eh} indicates that in the first sub-stage of the EH phase, the energy power collected by the IoT device from the HAP cannot exceed the saturation value of the RF-EH circuit;

[0128] This indicates that in the second sub-phase of the EH phase, the energy power collected by the IoT device from the HAP and WN cannot exceed the saturation value of the RF-EH circuit;

[0129] This indicates that the transmit power of HAP and WN cannot exceed their respective maximum transmit power;

[0130] This indicates that the transmission power of the IoT device cannot exceed its own maximum transmission power. The durations of the first sub-phase of the EH phase, the second sub-phase of the EH phase, and the information transmission phase are all positive numbers.

[0131] In this embodiment, S1 can be solved using the following process:

[0132] S101 solves for the duration T of the information transmission phase based on the energy conservation relationship during information transmission by IoT devices. tx The optimal value and transmission power P of IoT devices d The optimal value;

[0133] S102 sets the duration T of the information transmission phase. tx The optimal value and transmission power P of IoT devices d Substituting the optimal value into the energy utility maximization model, we obtain the zeroth intermediate energy utility maximization model P. A-0 ;

[0134] S103 uses the Lagrange method to optimize the zeroth intermediate energy utility maximization model P. A-0 The transmission power P of the hybrid access point (HAP) is obtained by solving the problem. HAP The optimal value, the time interval T of the first sub-stage eh,1 The optimal value and the time interval T between the second sub-stage eh,2 The optimal value;

[0135] S104 is based on the transmission power P of the hybrid access point (HAP). HAP The optimal value, the time interval T of the first sub-stage eh,1 The optimal value and the time interval T between the second sub-stage eh,2 The optimal value of the transmit power vector P of each wireless node WN is calculated.

[0136] Wherein, the time interval T of the first sub-stage eh,1 The time interval T between the second and third sub-phases eh,2 The duration T of the energy harvesting EH phase eh .

[0137] In this embodiment, the transmission power P of the hybrid access point (HAP) in S103 can be solved by the following steps. HAP The optimal value, the time interval T of the first sub-stage eh,1 The optimal value and the time interval T between the second sub-stage eh,2 The optimal values ​​include:

[0138] Based on the relationship between the energy collected by IoT devices from the hybrid access point (HAP) and each wireless node (WN) and the saturation value of the RF-EH circuit, the following two cases are discussed:

[0139] Case 1: When the energy collected by the IoT device from the hybrid access point (HAP) and each wireless node (WN) is less than or equal to the RF-EH circuit saturation value, then the energy E collected by the IoT device in the second sub-stage... eh,2 This equals the energy power collected by IoT devices from the hybrid access point (HAP) and each wireless node (WN), and is substituted into the zeroth intermediate energy utility maximization model P. A-0 The first intermediate energy utility maximization model P is obtained. A-1 ;

[0140] In the first intermediate energy utility maximization model P A-1 By introducing auxiliary variables and solving using the Lagrange method, the transmission power P of the hybrid access point (HAP) is obtained. HAP The optimal value, the time interval T of the first sub-stage eh,1 The optimal value and the time interval T between the second sub-stage eh,2 The optimal value;

[0141] The second scenario: When the energy collected by the IoT device from the hybrid access point (HAP) and each wireless node (WN) is greater than the RF-EH circuit saturation value, then the energy E collected by the IoT device in the second sub-stage... eh,2 This is equal to the saturation value of the RF-EH circuit, and substituted into the zeroth intermediate energy utility maximization model P. A-0The third intermediate energy utility maximization model P is obtained. A-3 ;

[0142] In the third intermediate energy utility maximization model P A-3 By introducing auxiliary variables and solving using the Lagrange method, the transmission power P of the hybrid access point (HAP) is obtained. HAP The optimal value, the time interval T of the first sub-stage eh,1 The optimal value and the time interval T between the second sub-stage eh,2 The optimal value.

[0143] Furthermore, in the first case, in the first intermediate energy utility maximization model P A-1 By introducing auxiliary variables and solving using the Lagrange method, the transmission power P of the hybrid access point (HAP) is obtained. HAP The optimal value, the time interval T of the first sub-stage eh,1 The optimal value and the time interval T between the second sub-stage eh,2 The optimal values ​​specifically include:

[0144] In the first intermediate energy utility maximization model P A-1 By introducing auxiliary variables, a second solvable energy utility maximization model P is obtained. A-2 ;

[0145] The second solvable energy utility maximization model P is solved using the Lagrange method. A-2 The second solvable energy utility maximization model P is obtained. A-2 The time interval T in the first sub-phase eh,1 The optimal value of the auxiliary variable, and the optimal solution of the auxiliary variable;

[0146] Using the second solvable energy utility maximization model P A-2 The time interval T in the first sub-phase eh,1 The optimal value of the variable and the optimal solution of the auxiliary variable are used to obtain the transmission power P of the hybrid access point (HAP). HAP The optimal value, the time interval T of the first sub-stage eh,1 The optimal value and the time interval T between the second sub-stage eh,2 The optimal value.

[0147] Furthermore, in the second case, in the third intermediate energy utility maximization model P A-3 By introducing auxiliary variables and solving using the Lagrange method, the transmission power P of the hybrid access point (HAP) is obtained. HAP The optimal value, the time interval T of the first sub-stage eh,1 The optimal value and the time interval T between the second sub-stage eh,2 The optimal values ​​include:

[0148] In the third intermediate energy utility maximization model P A-3 By introducing auxiliary variables, we obtain the fourth solvable energy utility maximization model P. A-4 ;

[0149] The fourth solvable energy utility maximization model P is solved using the Lagrange method. A-4 The fourth solvable energy utility maximization model P is obtained. A-4 The time interval T in the first sub-phase eh,1 The optimal value and the time interval T in the first sub-stage eh,2 The optimal value;

[0150] Using the fourth solvable energy utility maximization model P A-4 The time interval T in the first sub-phase eh,1 The optimal value and the time interval T in the first sub-stage eh,2 The optimal value is obtained to determine the transmission power P of the hybrid access point (HAP). HAP The optimal value, the time interval T of the first sub-stage eh,1 The optimal value and the time interval T between the second sub-stage eh,2 The optimal value.

[0151] In one specific implementation, S1 can be solved by the following steps to achieve the solution of each parameter P of the optimization model. HAP ,T eh,1 ,T eh,2 ,P,T tx ,P d optimal value

[0152] ①Solve

[0153] From the AoI energy utility function, it can be seen that T tx When P reaches its minimum value A It has reached its maximum value.

[0154] From T tx P d =B s It can be seen that P d When T reaches its maximum value tx It reaches the minimum value.

[0155] It can be obtained through derivation.

[0156]

[0157] in It is the Lambert function, i.e., f(x) = xex The inverse function of .

[0158]

[0159] ② Transformation and optimization problem

[0160] Will Substitute P A And from E m and P m The expression shows that P opt can be Calculations show that P A Can be converted to P A-0 .

[0161]

[0162] stT eh,1 +T eh,2 ≥T tx ,

[0163] E eh,1 +E eh,2 =B s ,

[0164] E eh,1 =T eh,1 min{η eh P HAP |h a,d | 2 M eh},

[0165]

[0166]

[0167] T eh,1 ,T eh,2 ≥0.

[0168] Due to coupling variable P HAP and T eh,1 The expression is non-convex, therefore P A-0 It's not a convex problem yet.

[0169] ③ Calculate according to different situations

[0170] Based on E eh,2 The function that minimizes the expression is analyzed from the following two cases regarding problem P. A-0 .

[0171] Case 1: At this time P A-0 Can be converted to P A-1 .

[0172]

[0173] st

[0174]

[0175]

[0176]

[0177] Introducing auxiliary variables At this time P A-1 Can be converted to P A-2 .

[0178]

[0179] st

[0180]

[0181]

[0182] T eh,1 ,T eh,2 ≥0.

[0183] P is obtained using the Lagrange method. A-2 Optimal solution

[0184]

[0185] in

[0186]

[0187] Therefore P A-1 Optimal solution for:

[0188]

[0189] Case 2: At this time P A-0 Can be converted to P A-3 .

[0190]

[0191] st

[0192] M eh Teh,2 +η eh P HAP |h a,d | 2 T eh,1 =B s ,

[0193]

[0194]

[0195] Due to coupling variable P HAP and T eh,1 The expression is non-convex, therefore P A-3 It's not a convex problem yet. We need to introduce an auxiliary variable. At this time P A-3 Can be converted to P A-4 .

[0196]

[0197] st

[0198]

[0199]

[0200]

[0201] T eh,1 ,T eh,2 ≥0.

[0202] P A-4 It is convex because its objective function and all constraints are linear, therefore P can be obtained. A-4 Optimal solution

[0203]

[0204] in

[0205] Therefore P A-3 Optimal solution for:

[0206]

[0207] ④ By Calculate P opt :

[0208]

[0209]

[0210]

[0211] This solution algorithm guarantees the solution to the original problem P. A global optimal solution

[0212] In the above solution process, the optimization problem was solved by using equality constraint elimination and the Lagrange method, and the network model was derived. Figure 2 Some closed-form solutions are used to obtain the optimal solution that maximizes AoI energy utility. The equality constraint elimination involved in this implementation refers to the process of substituting the values ​​of known optimization parameters into the energy utility maximization model during the solution process to continue solving for unknown optimization parameters.

[0213] It should be noted that although the steps in the above embodiments are described in a specific order, those skilled in the art will understand that in order to achieve the effects of the present invention, different steps do not necessarily have to be executed in such an order. They can be executed simultaneously (in parallel) or in other orders, and these variations are all within the scope of protection of the present invention.

[0214] Based on the same inventive concept, this embodiment also provides an information transmission optimization system for a wireless power supply communication network, used to implement the above-mentioned information transmission optimization method for a wireless power supply communication network, including:

[0215] The solution module is used to solve the energy utility maximization model corresponding to the Power Supply Communication Network (WPCN) in the actual scenario, and obtain the optimal values ​​of the optimization parameters of the WPCN in the actual scenario.

[0216] An adjustment module is used to adjust the optimization parameters of the wireless power supply communication network in the actual scenario to the optimal value;

[0217] The optimization parameters include: the transmission power P of the hybrid access point (HAP) in the Power over Wireless Communication Network (WPCN). HAP The duration T of the energy harvesting EH phase eh The transmit power vector P of each wireless node WN, and the duration T of the information transmission phase. tx and the transmission power P of IoT devices d .

[0218] Those skilled in the art will understand that all or part of the processes in the method of the above embodiment of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include any entity or device capable of carrying the computer program code, a medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0219] Furthermore, the present invention also provides a storage device. In one embodiment of the storage device according to the present invention, the storage device can be configured to store a program for executing the information transmission optimization method for a wireless power communication network described in the above-described method embodiments. This program can be loaded and run by a processor to implement the information transmission optimization method for the wireless power communication network described above. For ease of explanation, only the parts related to the embodiments of the present invention are shown; for specific technical details not disclosed, please refer to the method section of the embodiments of the present invention. The storage device can be a storage device device comprising various electronic devices. Optionally, in the embodiments of the present invention, the storage is a non-transitory computer-readable storage medium.

[0220] Furthermore, the present invention also provides a control device. In one embodiment of the control device according to the present invention, the control device includes a processor and a storage device. The storage device can be configured to store a program for executing the information transmission optimization method for a wireless power communication network of the above-described method embodiments. The processor can be configured to execute the program in the storage device, which includes, but is not limited to, the program for executing the information transmission optimization method for a wireless power communication network of the above-described method embodiments. For ease of explanation, only the parts related to the embodiments of the present invention are shown; for specific technical details not disclosed, please refer to the method section of the embodiments of the present invention. This control device can be a control device device comprising various electronic devices.

[0221] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0222] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0223] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0224] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0225] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing information transmission in a wireless power supply communication network, characterized in that, include: Solve the energy utility maximization model corresponding to the Power Supply Communication Network (WPCN) in the actual scenario, and obtain the optimal values ​​of the optimization parameters of the WPCN in the actual scenario. Adjust the optimization parameters of the wireless power supply communication network in the actual scenario to the optimal value; The optimization parameters include: the transmission power of the hybrid access point (HAP) in the Power over Wireless Communication Network (WPCN). Duration of the Energy Harvesting (EH) phase T eh Transmit power vector of each wireless node WN Duration of the information transmission phase T tx Transmission power of IoT devices ; The construction of the energy utility maximization model includes: With the goal of minimizing the information age (AoI) and energy required for each data packet when each wireless node (WN) transmits each data packet, an AoI energy utility function is constructed. The transmission power constraints of each wireless node WN are constructed based on the energy deduction rate of each wireless node WN in the AoI energy utility function. The construction of the AoI energy utility function includes: The duration of the energy harvesting EH phase T eh and the duration of the information transmission phase T tx The sum of these values ​​serves as the information age (AoI) for each data packet. The Energy Harvesting (EH) phase is divided into a first sub-phase where the Hybrid Access Point (HAP) charges all Wireless Nodes (WN) and IoT devices via broadcast, and a second sub-phase where the HAP, together with each Wireless Node (WN), charges the IoT devices. The sum of the transmission power of the hybrid access point (HAP) in the first sub-phase and the second sub-phase is used as the energy required to transmit each data packet. The AoI energy utility function is constructed based on a predetermined reference utility value, the information age (AoI) of each data packet, and the energy required to transmit each data packet. The operation of transmitting each state update data packet includes two time phases: the EH phase and the information transmission phase. The duration of the EH phase is... T eh The duration of the information transmission phase is... T tx ; The EH phase consists of two sub-phases, wherein in the first sub-phase, the time interval is... T eh,1 HAP charges WN and IoT devices via broadcast signals, while in the second sub-phase, the time interval is... T eh,2 HAP, together with WN, charges IoT devices, therefore... T eh = T eh,1 + T eh,2 .

2. The method as described in claim 1, characterized in that, The AoI energy utility function is shown in the following equation: In the formula: Represents the energy utility function of AoI; Indicates the transmission power of the HAP device; T tx Indicates the duration of the information transmission phase; T eh Indicates the duration of the energy harvesting (EH) phase; T eh,1 This represents the time interval within the first sub-phase; T eh,2 This indicates the time interval in the second sub-stage; It is a constant representing a predetermined reference utility value; It is the unit conversion scaling factor for the age (AoI) of each data packet. It is a unit conversion factor for energy.

3. The method as described in claim 2, characterized in that, The transmission power constraints of each wireless node WN are shown in the following formula: In the formula: Indicates the first m The transmission power of each wireless node WN; m ∈(0,1) represents the first... m Energy deduction rate of wireless node WN, the first m Each wireless node WN deduction m E m The energy it uses serves as the incentive to charge IoT devices; E m Indicates the first m One wireless node WN T eh,1 Energy collected from the hybrid access point (HAP) within a given time period This represents the set of all wireless nodes WN.

4. The method as described in claim 1, characterized in that, The construction of the energy utility maximization model also includes: The following constraints are constructed for the AoI energy utility function: In the formula: Indicates the transmission power of IoT devices; T tx Indicates the duration of the information transmission phase; B s This indicates the energy caching capacity of Internet of Things (IoT) devices; In the formula: W It is system bandwidth. h a,d This represents the channel coefficient of the wireless link between the hybrid access point (HAP) and IoT devices. N 0 represents the noise spectral density; L This indicates the length of the data packet for each state update; In the formula: T eh,1 This represents the time interval within the first sub-phase; T eh,2 This indicates the time interval in the second sub-stage; In the formula: This represents the energy collected by IoT devices in the first sub-phase. This represents the energy collected by IoT devices in the second sub-phase. In the formula: Indicates energy harvesting efficiency. P HAP Indicates the transmission power of the HAP device; M eh Indicates the saturation value of the RF-EH circuit; In the formula: Indicates the first m The transmission power of each wireless node WN; h m,d Indicates the first m Channel coefficients of the wireless link between a wireless node WN and an IoT device; M This indicates the total number of wireless nodes (WN). This represents the set of all wireless nodes WN; In the formula: This indicates the maximum available transmit power of the hybrid access point (HAP). Indicates the first m The maximum available transmit power of each wireless node WN; In the formula: This indicates the maximum available transmit power of an Internet of Things (IoT) device.

5. The method as described in claim 1, characterized in that, The method for solving the energy utility maximization model corresponding to the Power over Wireless Communication Network (WPCN) in the actual scenario, and obtaining the optimal values ​​of the optimization parameters of the WPCN in the actual scenario, includes: Solving for the duration of the information transmission phase based on the energy conservation relationship during information transmission using IoT devices. T tx The optimal value and transmission power P of IoT devices d The optimal value; The duration of the information transmission phase T tx Optimal values ​​and transmission power of IoT devices Substituting the optimal value into the energy utility maximization model, we obtain the zeroth intermediate energy utility maximization model P. A-0 ; The zeroth intermediate energy utility maximization model P is analyzed using the Lagrange method. A-0 The transmission power P of the hybrid access point (HAP) is obtained by solving the problem. HAP The optimal value, the time interval of the first sub-stage T eh,1 The optimal value and the time interval between the second sub-stage T eh,2 The optimal value; Based on the transmission power P of the hybrid access point (HAP) HAP The optimal value, the time interval of the first sub-stage T eh,1 The optimal value and the time interval between the second sub-stage T eh,2 The optimal value is calculated for the transmit power vector of each wireless node WN. The optimal value; Wherein, the time interval of the first sub-stage T eh,1 The time interval between the second and third sub-phases T eh,2 Duration of the Energy Harvesting (EH) phase T eh .

6. The method as described in claim 5, characterized in that, The transmission power of the Internet of Things (IoT) device The optimal value is calculated using the following formula: In the formula: Transmission power for IoT devices The optimal value; B s This indicates the energy caching capacity of Internet of Things (IoT) devices; It's the Lambert function. ; W It is system bandwidth. h a,d This represents the channel coefficient of the wireless link between the hybrid access point (HAP) and IoT devices. N 0 represents the noise spectral density; L This indicates the length of the data packet for each state update; Transmission power for IoT devices The maximum value; Duration of the information transmission phase T tx The optimal value is calculated using the following formula: In the formula: Duration of the information transmission phase T tx The optimal value.

7. The method as described in claim 5, characterized in that, The Lagrange method is used to maximize the zeroth intermediate energy utility model P. A-0 The transmission power of the hybrid access point (HAP) is obtained by solving the problem. The optimal value, the time interval of the first sub-stage T eh,1 The optimal value and the time interval between the second sub-stage T eh,2 The optimal values ​​include: When the energy collected by the IoT device from the hybrid access point (HAP) and each wireless node (WN) is less than or equal to the RF-EH circuit saturation value, then the energy collected by the IoT device in the second sub-phase... This equals the energy power collected by IoT devices from the hybrid access point (HAP) and each wireless node (WN), and is substituted into the zeroth intermediate energy utility maximization model P. A-0 The first intermediate energy utility maximization model P is obtained. A-1 ; In the first intermediate energy utility maximization model P A-1 By introducing auxiliary variables and solving using the Lagrange method, the transmission power of the hybrid access point (HAP) is obtained. The optimal value, the time interval of the first sub-stage T eh,1 The optimal value and the time interval between the second sub-stage T eh,2 The optimal value; When the energy collected by the IoT device from the hybrid access point (HAP) and each wireless node (WN) exceeds the RF-EH circuit saturation value, then the energy collected by the IoT device in the second sub-phase... This is equal to the saturation value of the RF-EH circuit, and substituted into the zeroth intermediate energy utility maximization model P. A-0 The third intermediate energy utility maximization model P is obtained. A-3 ; In the third intermediate energy utility maximization model P A-3 By introducing auxiliary variables and solving using the Lagrange method, the transmission power of the hybrid access point (HAP) is obtained. The optimal value, the time interval of the first sub-stage T eh,1 The optimal value and the time interval between the second sub-stage T eh,2 The optimal value.

8. The method as described in claim 7, characterized in that, The first intermediate energy utility maximization model P A-1 By introducing auxiliary variables and solving using the Lagrange method, the transmission power of the hybrid access point (HAP) is obtained. The optimal value, the time interval of the first sub-stage T eh,1 The optimal value and the time interval between the second sub-stage T eh,2 The optimal values ​​include: In the first intermediate energy utility maximization model P A-1 By introducing auxiliary variables, a second solvable energy utility maximization model P is obtained. A-2 ; The second solvable energy utility maximization model P is solved using the Lagrange method. A-2 The second solvable energy utility maximization model P is obtained. A-2 The time interval in the first sub-phase T eh,1 The optimal value of the auxiliary variable, and the optimal solution of the auxiliary variable; Using the second solvable energy utility maximization model P A-2 The time interval in the first sub-phase T eh,1 The optimal value of the auxiliary variable is obtained to determine the transmission power of the hybrid access point (HAP). The optimal value, the time interval of the first sub-stage T eh,1 The optimal value and the time interval between the second sub-stage T eh,2 The optimal value.

9. The method as described in claim 8, characterized in that, The second solvable energy utility maximization model P is used. A-2 The time interval in the first sub-phase T eh,1 The optimal value of the variable and the optimal solution of the auxiliary variable are used to obtain the transmission power of the hybrid access point (HAP) as shown in the following formula. The optimal value, the time interval of the first sub-stage T eh,1 The optimal value and the time interval between the second sub-stage T eh,2 The optimal value: In the formula: Transmission power for Hybrid Access Points (HAPs) The optimal value; The time interval for the first sub-phase T eh,1 The optimal value; The time interval for the second sub-stage T eh,2 The optimal value; B s This indicates the energy caching capacity of Internet of Things (IoT) devices; Indicates energy harvesting efficiency; h a,d This represents the channel coefficient of the wireless link between the hybrid access point (HAP) and IoT devices. ; Indicates the first m The maximum available transmit power of each wireless node WN; m Indicates the first m Energy deduction rate of each wireless node WN; For the hybrid access point HAP and the first m Channel coefficients of the wireless links between wireless nodes WN; ; M This indicates the total number of wireless nodes (WN). h m,d Indicates the first m Channel coefficients of the wireless link between a wireless node WN and an IoT device; ; Duration of the information transmission phase T tx The optimal value; Transmission power for Hybrid Access Points (HAPs) The maximum value; For the second solvable energy utility maximization model P A-2 The time interval in the first sub-phase T eh,1 The optimal value; The optimal solution for the auxiliary variables.

10. The method as described in claim 7, characterized in that, The third intermediate energy utility maximization model P A-3 By introducing auxiliary variables and solving using the Lagrange method, the transmission power of the hybrid access point (HAP) is obtained. The optimal value, the time interval of the first sub-stage T eh,1 The optimal value and the time interval between the second sub-stage T eh,2 The optimal values ​​include: In the third intermediate energy utility maximization model P A-3 By introducing auxiliary variables, we obtain the fourth solvable energy utility maximization model P. A-4 ; The fourth solvable energy utility maximization model P is solved using the Lagrange method. A-4 The fourth solvable energy utility maximization model P is obtained. A-4 The time interval in the first sub-phase T eh,1 The optimal value and the time interval in the first sub-stage T eh,2 The optimal value; Using the fourth solvable energy utility maximization model P A-4 The time interval in the first sub-phase T eh,1 The optimal value and the time interval in the first sub-stage T eh,2 The optimal value is used to obtain the transmission power of the hybrid access point (HAP). The optimal value, the time interval of the first sub-stage T eh,1 The optimal value and the time interval between the second sub-stage T eh,2 The optimal value.

11. The method as described in claim 10, characterized in that, The fourth solvable energy utility maximization model P is used. A-4 The time interval in the first sub-phase T eh,1 The optimal value and the time interval in the first sub-stage T eh,2 The optimal value is obtained by the following formula for the transmission power of the hybrid access point (HAP). The optimal value, the time interval of the first sub-stage T eh,1 The optimal value and the time interval between the second sub-stage T eh,2 The optimal value: In the formula: Transmission power for Hybrid Access Points (HAPs) The optimal value; The time interval for the first sub-phase T eh,1 The optimal value; The time interval for the second sub-stage T eh,2 The optimal value; ; B s This indicates the energy caching capacity of Internet of Things (IoT) devices; Duration of the information transmission phase T tx The optimal value; M eh Indicates the saturation value of the RF-EH circuit; Indicates energy harvesting efficiency; h a,d This represents the channel coefficient of the wireless link between the hybrid access point (HAP) and IoT devices. Transmission power for Hybrid Access Points (HAPs) The maximum value; For the fourth solvable energy utility maximization model P A-4 The time interval in the first sub-phase T eh,1 The optimal value; For the fourth solvable energy utility maximization model P A-4 The time interval in the first sub-phase T eh,2 The optimal value; Indicates the first m The maximum available transmit power of each wireless node WN; m Indicates the first m Energy deduction rate of each wireless node WN; For the hybrid access point HAP and the first m Channel coefficients of the wireless links between wireless nodes WN.

12. The method as described in claim 5, characterized in that, The transmission power based on the hybrid access point (HAP) The optimal value, the time interval of the first sub-stage T eh,1 The optimal value and the time interval between the second sub-stage T eh,2 The optimal value is calculated using the following formula to determine the transmit power vector of each wireless node WN. The optimal value: In the formula: The transmit power vector of each wireless node WN The optimal value; Transmission power for Hybrid Access Points (HAPs) The optimal value; The time interval for the first sub-phase T eh,1 The optimal value; The time interval for the second sub-stage T eh,2 The optimal value; Indicates energy harvesting efficiency; For the hybrid access point HAP and the first m Channel coefficients of the wireless links between wireless nodes WN; M eh Indicates the saturation value of the RF-EH circuit; This represents the set of all wireless nodes WN; Indicates the first m The optimal value of WN transmission power for each wireless node; m Indicates the first m Energy deduction rate of each wireless node WN; Indicates the first m One wireless node WN T eh,1 The optimal energy collected from the hybrid access point (HAP) within a given time period.

13. An information transmission optimization system for a wireless power supply communication network, characterized in that, A method for optimizing information transmission in a wireless power supply communication network as described in any one of claims 1 to 12, comprising: The solution module is used to solve the energy utility maximization model corresponding to the Power Supply Communication Network (WPCN) in the actual scenario, and obtain the optimal values ​​of the optimization parameters of the WPCN in the actual scenario. An adjustment module is used to adjust the optimization parameters of the wireless power supply communication network in the actual scenario to the optimal value; The optimization parameters include: the transmission power of the hybrid access point (HAP) in the Power over Wireless Communication Network (WPCN). Duration of the Energy Harvesting (EH) phase T eh Transmit power vector of each wireless node WN Duration of the information transmission phase T tx Transmission power of IoT devices .

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