Load-based time-phased energy resource pricing method

Abstract

The invention discloses a load-based time-phased energy resource pricing method, which comprises the following steps of: constructing an energy collection model and a throughput model of Internet of Things equipment in a hybrid access point mode and an inherent energy node mode based on a time block model; enabling the Internet of Things equipment to collect energy in a hybrid access point mode and / or an inherent energy node mode; according to the energy supply demand of the Internet of Things equipment, energy collection of the Internet of Things equipment is divided into a peak period and an off-peak period, and a corresponding resource pricing model is established; and realizing time-phased energy resource pricing through a resource pricing model. According to the method, an energy resource price is properly increased in a peak period through a resource pricing model, and access of Internet of Things equipment is inhibited; the energy resource price is properly reduced in the off-peak period, and the Internet of Things equipment is stimulated to access to obtain energy; therefore, the energy supply resource pricing problem of the Internet of Things equipment in a wireless power supply communication network scene can be effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless power supply communication network, and more specifically to a load-based time-segment energy resource pricing method. Background Art

[0002] At present, the development of the Internet of Things (IoT) has spawned a variety of applications. The execution of various tasks in IoT applications requires the use of energy-constrained IoT devices, which brings challenges to the actual deployment of IoT. Wireless Powered Communication Networks (WPCN) can overcome this problem. Wireless Powered Communication Networks use radio frequency energy harvesting technology to provide sustainable energy supply for IoT devices, and then use the harvested energy to support information transmission of IoT devices. However, in actual applications, due to the "double attenuation" faced by wireless power communication networks, the transmission efficiency of energy and information needs to be improved.

[0003] Reconfigurable Intelligent Surface (RIS) can form a more favorable wireless propagation environment by changing the amplitude and phase of the incident signal, thereby improving the efficiency of energy and information transmission. The wireless power supply communication network assisted by reconfigurable intelligent surface can combine the advantages of both and improve the performance of traditional wireless power supply communication network. At present, there has been preliminary research on wireless power supply communication network assisted by reconfigurable intelligent surface. However, in the process of studying the prior art, the inventor found that it has the following disadvantages:

[0004] First, a large number of previous papers focused on improving the throughput and energy performance of wireless power communication networks, and rarely paid attention to the resource cost issues in wireless power communication networks from the perspective of service providers. Secondly, a large number of works focus on the situation where wireless energy supply nodes provide energy for IoT devices for free, without considering the selfishness and service capabilities (energy resource status) of energy supply nodes, which is not realistic. Finally, the resource pricing issue of energy supply nodes in wireless power communication networks has not been addressed.

[0005] Therefore, there is an urgent need for a method to solve the energy supply resource pricing problem of IoT devices in the wireless power communication network scenario, so as to study the resource cost from the perspective of the service provider. Summary of the invention

[0006] In view of the above problems, the present invention provides a load-based time-sharing energy resource pricing method for a reconfigurable smart surface-assisted wireless power supply communication network, so as to at least solve some of the technical problems mentioned in the above background technology.

[0007] In order to achieve the above object, the present invention adopts the following technical solution:

[0008] A load-based time-division energy resource pricing method, applied to a time-division energy resource pricing system, comprises the following steps:

[0009] S1. Based on the time block model, the energy harvesting model and throughput model of IoT devices are constructed in the hybrid access point mode and the inherent energy node mode;

[0010] S2. enabling the IoT device to collect energy in the hybrid access point mode and / or the inherent energy node mode;

[0011] S3. According to the energy supply demand of IoT devices, the energy collection of IoT devices is divided into peak hours and non-peak hours, and a corresponding resource pricing model is established; and the energy resource pricing of each time period is realized through the resource pricing model.

[0012] Further, the energy supply node in the hybrid access point mode is a hybrid access point; and the energy supply node in the inherent energy node mode is an inherent energy node.

[0013] Furthermore, in the time block model, a time block with a duration of T is divided into an energy transmission duration and an information transmission duration.

[0014] Furthermore, the step S1 specifically includes:

[0015] (1) During the energy transmission duration, in the hybrid access point mode, an energy collection model of the IoT device is constructed; specifically, it is expressed as follows:

[0016]

[0017] Among them, E h,k represents the energy collected by IoT device k through the hybrid access point during the energy transmission time; η represents the energy conversion efficiency; τT represents the energy transmission time; P h represents the transmission power of the hybrid access point; w t,k represents the transmit beamforming vector of the hybrid access point, and represents a complex set, represents a complex matrix set of size M×1, where M represents the number of antennas equipped by the hybrid access point; h d,k represents the downlink channel between the hybrid access point and IoT device k, and h r,k represents the downlink channel between the reconfigurable smart surface and IoT device k, and N represents the number of reflective units constituting the reconfigurable smart surface; (·) Hrepresents the conjugate transpose operation; Indicates h r,k The conjugate transpose of H d represents the downlink channel between the hybrid access point and the reconfigurable smart surface, and Θ0 represents the energy reflection phase shift matrix of the reconfigurable smart surface, and θ 0,n represents the phase reflection coefficient of the nth reflection unit of the reconfigurable smart surface during energy transmission, and θ 0,n ∈[0,2π); represents the phase reflection coefficient θ 0,n The complex exponential form of represents the imaginary unit; let v0 represents the reflection coefficient vector of the reconfigurable smart surface during energy transfer, (·) T Represents the transpose operation;

[0018] (2) During the energy transmission time, in the inherent energy node mode, an energy collection model of the IoT device is constructed; specifically, it is expressed as follows:

[0019]

[0020] Among them, E c,k represents the energy collected by IoT device k through the inherent energy node during the energy transmission duration; represents the charging power of IoT device k in the inherent energy node mode; Indicates charging efficiency; represents the discharge power of IoT device k in the inherent energy node mode; Indicates discharge efficiency;

[0021] (3) During the information transmission time, in the hybrid access point mode, a throughput model of the IoT device is constructed; specifically, it is expressed as follows:

[0022]

[0023] Among them, R h,k represents the throughput of IoT device k when transmitting information through the hybrid access point to collect energy within the information transmission time; (1-τ)T represents the information transmission time; represents the transmission power of IoT device k in the hybrid access point mode; g u,k represents the uplink channel between IoT device k and the hybrid access point, and g r,k represents the uplink channel between IoT device k and the reconfigurable smart surface, and G urepresents the uplink channel between the reconfigurable smart surface and the hybrid access point, and Θ1 represents the information reflection phase shift matrix of the reconfigurable smart surface, and θ 1,n represents the phase reflection coefficient of the nth reflection unit of the reconfigurable smart surface during information transmission, and θ 1,n ∈[0,2π); let v1 represents the reflection coefficient vector of the reconstructible smart surface during information transmission; represents the beamforming vector when decoding the information transmitted by IoT device k; K represents the number of single-antenna IoT devices; P h,i represents the transmission power of IoT device i in the hybrid access point mode; g u,i represents the uplink channel between IoT device i and the hybrid access point; g r,i represents the uplink channel between IoT device i and the reconfigurable smart surface; Indicates g r,i The conjugate transpose of r,i represents the transmit beamforming vector when decoding the information transmitted by IoT device i; σ 2 represents the additive white Gaussian noise power;

[0024] (4) Within the information transmission time, under the inherent energy node mode, a throughput model of the IoT device is constructed; specifically, it is expressed as follows:

[0025]

[0026] Among them, R c,k It represents the throughput of IoT device k when transmitting information through inherent energy nodes within the information transmission duration; It represents the transmission power of IoT device k in transmitting information in the inherent energy node mode.

[0027] Furthermore, in the step S2, when the IoT device collects energy in the hybrid access point mode and the inherent energy node mode, it preferentially obtains energy in the hybrid access point mode; when the hybrid access point load exceeds a preset value, it obtains energy in the inherent energy node mode.

[0028] Further, in the step S2, when the IoT device collects energy in the inherent energy node mode, an inherent energy node is determined to provide energy for the IoT device according to the reputation value of the inherent energy node.

[0029] Furthermore, the reputation value of the inherent energy node is expressed as:

[0030]

[0031] Among them, S j Represents the reputation value of the inherent energy node; N j N represents the number of times the inherent energy node j has been selected in history; max Indicates the maximum number of times the history is selected among all intrinsic energy nodes.

[0032] Furthermore, the resource pricing model is expressed as:

[0033]

[0034] Where p represents the energy resource pricing of the hybrid access point or the inherent energy node; B represents the current load of the hybrid access point or the inherent energy node, and 0≤B≤B max , B max represents the maximum load; K represents the number of single-antenna IoT devices; k represents the kth IoT device; α k represents the energy demand indicator variable of IoT device k; α k =1 means that IoT device k needs energy supply; α k =0 means that IoT device k does not need energy supply; p0 represents the initial price of energy resources of hybrid access points or inherent energy nodes.

[0035] Furthermore, it also includes:

[0036] S4. Taking the IoT device as the leader and the energy supply node as the follower, a Stackelberg game model of energy transaction between the energy supply node and the IoT device is established to maximize the utility function of the IoT device and the utility function of the energy supply node.

[0037] Furthermore, in the Stackelberg game model:

[0038] (1) The utility function maximization problem of IoT devices is expressed as:

[0039]

[0040] stC1:0≤B≤B max

[0041] C2:0<τ<1

[0042] C3:|v1|=1

[0043] Among them, U I represents the utility function of the IoT device; r represents the reward for the IoT device to obtain unit throughput benefit; p represents the energy resource pricing of the hybrid access point or inherent energy node; wr,k represents the beamforming vector when decoding the information transmitted by IoT device k; τ represents the time allocation ratio; τT represents the energy transmission duration; R k represents the throughput of IoT device k when transmitting information through hybrid access points or inherent energy nodes to collect energy within the information transmission time; E k represents the energy collected by IoT device k through the hybrid access point or inherent energy node during the energy transmission duration; B represents the current load of the hybrid access point or inherent energy node, and B max represents the maximum load; Θ1 represents the information reflection phase shift matrix of the reconfigurable smart surface, and θ 1,n represents the phase reflection coefficient of the nth reflection unit of the reconfigurable smart surface during information transmission, and θ 1,n ∈[0,2π); let v1 represents the reflection coefficient vector of the reconstructible smart surface during information transmission; C1 represents the first constraint; C2 represents the second constraint; C3 represents the third constraint;

[0044] (2) The utility function maximization problem of the energy supply node is expressed as:

[0045]

[0046] stC4:P e ≥0

[0047] C5:|v0|=1

[0048] Among them, U e represents the utility function of the energy supply node; w t,k represents the transmit beamforming vector of the hybrid access point; Θ0 represents the energy reflection phase shift matrix of the reconfigurable smart surface; P represents the cost function of energy resources provided by energy supply nodes per unit time; e It represents the transmission power of the hybrid access point or the charging power of the IoT device k in the inherent energy node mode; the charging power of the IoT device k in the inherent energy node mode is equal to the transmission power of the inherent energy node; C4 represents the fourth constraint; C5 represents the fifth constraint.

[0049] It can be seen from the above technical solution that, compared with the prior art, the present invention discloses a load-based time-sharing energy resource pricing method, which has the following beneficial effects:

[0050] The present invention uses a resource pricing model to appropriately increase the price of energy resources during peak hours to inhibit the access of Internet of Things devices; and appropriately reduce the price of energy resources during non-peak hours to encourage Internet of Things devices to access and obtain energy; thereby effectively solving the problem of pricing energy supply resources for Internet of Things devices in wireless power supply communication network scenarios.

[0051] The present invention constructs a Stackelberg game model for energy trading between energy supply nodes and IoT devices. Appropriate rewards are given to IoT devices during the information transmission stage of IoT devices to encourage IoT devices to transmit information with greater transmission power, thereby obtaining higher throughput. Through the game between energy supply nodes and IoT devices, the utility of both parties is ultimately maximized.

[0052] Other features and advantages of the present invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the present invention.

[0053] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0055] Figure 1 A schematic diagram of a usage scenario of a time-divided energy resource pricing system provided in an embodiment of the present invention.

[0056] Figure 2 A schematic flow chart of a load-based time-sharing energy resource pricing method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] The embodiment of the present invention discloses a load-based time-division energy resource pricing method, which is applied to a time-division energy resource pricing system. Figure 1As shown, it includes a hybrid access point equipped with M antennas, a reconfigurable smart surface composed of N reflection units, a RIS controller embedded in the reconfigurable smart surface, K single-antenna IoT devices, J inherent energy nodes and a central control unit embedded in the hybrid access point; wherein:

[0059] Hybrid access point: used to broadcast energy to IoT devices and receive information transmitted by IoT devices;

[0060] Intrinsic energy nodes: provide energy to IoT devices through leasing;

[0061] Reconfigurable Smart Surface (RIS): used to adjust the wireless propagation environment between hybrid access points and IoT devices;

[0062] Reconfigurable intelligent surface controller (RIS controller): used to control the reconfigurable intelligent surface;

[0063] IoT devices: used for energy reception and information transmission;

[0064] Central control unit: used to adjust energy trading prices and energy acquisition methods in different time periods.

[0065] Based on the above-mentioned time-divided energy resource pricing system, the load-based time-divided energy resource pricing method provided by the present invention is as follows: Figure 2 As shown, the following steps are included

[0066] S1. Based on the time block model, the energy harvesting model and throughput model of IoT devices are constructed in the hybrid access point mode and the inherent energy node mode;

[0067] S2, enabling IoT devices to harvest energy in a hybrid access point mode and / or an inherent energy node mode;

[0068] S3. According to the energy supply demand of IoT devices, the energy collection of IoT devices is divided into peak hours and non-peak hours, and a corresponding resource pricing model is established; the energy resource pricing of each time period is realized through the resource pricing model.

[0069] The following is a detailed description of each of the above steps.

[0070] In the above step S1, an energy collection model and a throughput model of the IoT device under two energy supply modes are established based on the time block model;

[0071] In the above time block model, the time block with a duration of T is divided into two parts, which are used for energy transmission and information transmission respectively. The energy transmission duration is defined as τT, and the information transmission duration is (1-τ)T, where τ represents the time allocation ratio, and τ∈(0,1);

[0072] The above two energy supply modes include a hybrid access point mode and an inherent energy node mode; wherein the energy supply node of the hybrid access point mode is a hybrid access point; the energy supply node of the inherent energy node mode is an inherent energy node; the resources provided by the hybrid access point are called radio frequency resources, and the resources provided by the inherent energy node are called leased resources;

[0073] Based on the above content, based on the time block model, the energy collection model and throughput model of IoT devices are constructed in the hybrid access point mode and the inherent energy node mode respectively; specifically including:

[0074] (1) During the energy transmission duration, in the hybrid access point mode, an energy collection model of the IoT device is constructed; specifically, it is expressed as follows:

[0075]

[0076] Among them, E h,k represents the energy collected by IoT device k through the hybrid access point during the energy transmission time; η represents the energy conversion efficiency; τT represents the energy transmission time; P h represents the transmission power of the hybrid access point; w t,k represents the transmit beamforming vector of the hybrid access point, and represents a complex set, represents a complex matrix set of size M×1, where M represents the number of antennas equipped by the hybrid access point; h d,k represents the downlink channel between the hybrid access point and IoT device k, and h r,k represents the downlink channel between the reconfigurable smart surface and IoT device k, and N represents the number of reflective units constituting the reconfigurable smart surface; (·) H represents the conjugate transpose operation; Indicates h r,k The conjugate transpose of H d represents the downlink channel between the hybrid access point and the reconfigurable smart surface, and Θ0 represents the energy reflection phase shift matrix of the reconfigurable smart surface, and θ 0,n represents the phase reflection coefficient of the nth reflection unit of the reconfigurable smart surface during energy transmission, and θ 0,n ∈[0,2π); represents the phase reflection coefficient θ 0,n The complex exponential form of represents the imaginary unit; let v0 represents the reflection coefficient vector of the reconfigurable smart surface during energy transfer, (·) T Represents the transpose operation;

[0077] (2) During the energy transmission time, in the inherent energy node mode, an energy collection model of the IoT device is constructed; specifically, it is expressed as follows:

[0078]

[0079] Among them, E c,k represents the energy collected by IoT device k through the inherent energy node during the energy transmission duration; represents the charging power of IoT device k in the inherent energy node mode; Indicates charging efficiency; represents the discharge power of IoT device k in the inherent energy node mode; Indicates discharge efficiency;

[0080] (3) During the information transmission time, in the hybrid access point mode, a throughput model of the IoT device is constructed; specifically, it is expressed as follows:

[0081]

[0082] Among them, R h,k represents the throughput of IoT device k when transmitting information through the hybrid access point to collect energy within the information transmission time; (1-τ)T represents the information transmission time; represents the transmission power of IoT device k in the hybrid access point mode; g u,k represents the uplink channel between IoT device k and the hybrid access point, and g r,k represents the uplink channel between IoT device k and the reconfigurable smart surface, and G u represents the uplink channel between the reconfigurable smart surface and the hybrid access point, and Θ1 represents the information reflection phase shift matrix of the reconfigurable smart surface, and θ 1,n represents the phase reflection coefficient of the nth reflection unit of the reconfigurable smart surface during information transmission, and θ 1,n ∈[0,2π); let v1 represents the reflection coefficient vector of the reconstructible smart surface during information transmission; represents the beamforming vector when decoding the information transmitted by IoT device k; K represents the number of single-antenna IoT devices; P h,i represents the transmission power of IoT device i in the hybrid access point mode; g u,i represents the uplink channel between IoT device i and the hybrid access point; g r,irepresents the uplink channel between IoT device i and the reconfigurable smart surface; Indicates g r,i The conjugate transpose of r,i represents the transmit beamforming vector when decoding the information transmitted by IoT device i; σ 2 represents the additive white Gaussian noise power;

[0083] (4) Within the information transmission time, under the inherent energy node mode, a throughput model of the IoT device is constructed; specifically, it is expressed as follows:

[0084]

[0085] Among them, R c,k It represents the throughput of IoT device k when transmitting information through inherent energy nodes within the information transmission duration; It represents the transmission power of IoT device k in transmitting information in the inherent energy node mode.

[0086] In the above step S2, the IoT device collects energy in the hybrid access point mode and / or the inherent energy node mode;

[0087] When the IoT device collects energy in the hybrid access point mode and the inherent energy node mode, it obtains energy in the hybrid access point mode first; when the load of the hybrid access point exceeds a preset value (e.g., 80%), it obtains energy in the inherent energy node mode;

[0088] When the IoT device collects energy in the inherent energy node mode, an inherent energy node is determined to provide energy to the IoT device based on the reputation value of the inherent energy node;

[0089] The reputation value of the inherent energy node is expressed as:

[0090]

[0091] Among them, S j Represents the reputation value of the inherent energy node; N j N represents the number of times the inherent energy node j has been selected in history; max Indicates the maximum number of times all inherent energy nodes have been selected in history; the inherent energy node with the highest reputation value is selected to supply energy to the IoT device. At the same time, the IoT device can only choose one energy supply method.

[0092] In the above step S3, according to the energy supply demand of the IoT devices, the energy collection of the IoT devices is divided into peak time and non-peak time, and a corresponding resource pricing model is established; the time-divided energy resource pricing is realized through the resource pricing model; the resource pricing model is expressed as:

[0093]

[0094] Where p represents the energy resource pricing of the hybrid access point or the inherent energy node; B represents the current load of the hybrid access point or the inherent energy node, and 0≤B≤B max , B max represents the maximum load; K represents the number of single-antenna IoT devices; k represents the kth IoT device; α k represents the energy demand indicator variable of IoT device k; α k =1 means that IoT device k needs energy supply; α k =0 means that IoT device k does not need energy supply; p0 represents the initial price of energy resources of hybrid access points or inherent energy nodes.

[0095] In another embodiment, it also includes:

[0096] S4. Taking the IoT device as the leader and the energy supply node as the follower, a Stackelberg game model of energy transaction between the energy supply node and the IoT device is established to maximize the utility function of the IoT device and the utility function of the energy supply node; specifically:

[0097] Energy supply nodes include hybrid access points or inherent energy nodes, which are used to provide radio frequency resources or lease resources for IoT devices. Energy supply nodes charge fees to IoT devices. The energy collected by IoT devices is used for information transmission. IoT devices obtain rewards during the information transmission stage, which encourages IoT devices to transmit information with higher transmission power to obtain higher throughput. Therefore, the utility function of IoT devices is expressed as the difference between throughput benefit and energy expenditure, which is expressed as:

[0098] U I =rR k -pE k

[0099] The utility function of the energy supply node is expressed as the difference between energy benefit and energy cost, which is expressed as:

[0100] U e = pE k -τTC(P e )

[0101] Among them, R k = {R h,k ,R c,k}, E k ={E h,k ,E c,k}, It represents the cost of energy resources provided by energy supply nodes per unit time, where P h Indicates the transmit power of the hybrid access point; represents the charging power of IoT device k in the inherent energy node mode; represents the charging power of IoT device k in the inherent energy node mode It is equal to the transmission power of the inherent energy node; a and b are both positive numbers and can be obtained by fitting actual data;

[0102] The IoT device decides whether to purchase energy, so the IoT device is taken as the leader and the energy supply node as the follower to establish the following Stackelberg game model:

[0103] (1) The utility function maximization problem of IoT devices is expressed as:

[0104]

[0105] stC1:0≤B≤B max

[0106] C2:0<τ<1

[0107] C3:|v1|=1

[0108] Among them, U I represents the utility function of the IoT device; r represents the reward for the IoT device to obtain unit throughput benefit; p represents the energy resource pricing of the hybrid access point or inherent energy node; w r,k represents the beamforming vector when decoding the information transmitted by IoT device k; τ represents the time allocation ratio; τT represents the energy transmission duration; R k represents the throughput of IoT device k when transmitting information through hybrid access points or inherent energy nodes to collect energy within the information transmission time; E k represents the energy collected by IoT device k through the hybrid access point or inherent energy node during the energy transmission duration; B represents the current load of the hybrid access point or inherent energy node, and B max represents the maximum load; Θ1 represents the information reflection phase shift matrix of the reconfigurable smart surface, and θ 1,n represents the phase reflection coefficient of the nth reflection unit of the reconfigurable smart surface during information transmission, and θ 1,n ∈[0,2π); let v1 represents the reflection coefficient vector of the reconfigurable smart surface during information transmission; C1 represents the first constraint, which means that the load of the energy supply node cannot exceed the maximum value B max; C2 represents the second constraint, which means that the energy and information transmission time allocation ratio is between (0,1); C3 represents the third constraint, which means that the phase reflection coefficient modulus of the reconfigurable smart surface during information transmission is 1;

[0109] (2) The utility function maximization problem of the energy supply node is expressed as:

[0110]

[0111] stC4:P e ≥0

[0112] C5:|v0|=1

[0113] Among them, U e represents the utility function of the energy supply node; w t,k represents the transmit beamforming vector of the hybrid access point; Θ0 represents the energy reflection phase shift matrix of the reconfigurable smart surface; P represents the cost function of energy resources provided by energy supply nodes per unit time; e represents the transmission power of the hybrid access point or the charging power of the IoT device k in the inherent energy node mode; the charging power of the IoT device k in the inherent energy node mode is equal to the transmission power of the inherent energy node; C4 represents the fourth constraint, indicating that the transmission power of the energy supply node is non-negative; C5 represents the fifth constraint, indicating that the phase reflection coefficient modulus of the reconfigurable smart surface during energy transmission is 1.

[0114] In summary, the present invention focuses on the resource cost issue from the perspective of service providers for the wireless power supply communication network of the Internet of Things assisted by reconfigurable smart surfaces, and provides a load-based time-sharing energy resource pricing method. The energy resource price is appropriately increased during peak hours to inhibit the access of Internet of Things devices; the energy resource price is appropriately reduced during non-peak hours to encourage Internet of Things devices to access and obtain energy. The present invention gives appropriate rewards to Internet of Things devices during the information transmission stage of Internet of Things devices to encourage Internet of Things devices to transmit information with a larger transmission power, thereby obtaining a higher throughput. The present invention constructs a Stackelberg game model for energy transactions between energy supply nodes and Internet of Things devices, and ultimately maximizes the utility of both parties through the game between energy supply nodes and Internet of Things devices.

[0115] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other. The above description of the disclosed embodiments enables professionals and technicians in this field to implement or use the present invention. Various modifications to these embodiments will be obvious to professionals and technicians in this field, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown in this article, but will comply with the widest range consistent with the principles and novel features disclosed herein.

Claims

1. A load-based time-divided energy resource pricing method, applied to a time-divided energy resource pricing system, It is characterized in that The steps include: S1. Based on the time block model, the energy harvesting model and throughput model of IoT devices are constructed in the hybrid access point mode and the inherent energy node mode; S2. enabling the IoT device to collect energy in the hybrid access point mode and / or the inherent energy node mode; S3. According to the energy supply demand of IoT devices, the energy collection of IoT devices is divided into peak hours and off-peak hours, and a corresponding resource pricing model is established; The resource pricing model is used to implement time-divided energy resource pricing.

2. A load-based time-sharing energy resource pricing method according to claim 1, It is characterized in that The energy supply node in the hybrid access point mode is a hybrid access point; the energy supply node in the inherent energy node mode is an inherent energy node.

3. A load-based time-sharing energy resource pricing method according to claim 2, It is characterized in that In the time block model, a time block with a duration of T is divided into an energy transmission duration and an information transmission duration.

4. A load-based time-sharing energy resource pricing method according to claim 3, It is characterized in that The step S1 specifically includes: (1) During the energy transmission duration, in the hybrid access point mode, an energy collection model of the IoT device is constructed; specifically, it is expressed as follows: Among them, E h,k represents the energy collected by IoT device k through the hybrid access point during the energy transmission time; η represents the energy conversion efficiency; τT represents the energy transmission time; P h represents the transmission power of the hybrid access point; w t,k represents the transmit beamforming vector of the hybrid access point, and represents a complex set, represents a complex matrix set of size M×1, where M represents the number of antennas equipped by the hybrid access point; h d,k represents the downlink channel between the hybrid access point and IoT device k, and h r,k represents the downlink channel between the reconfigurable smart surface and IoT device k, and N represents the number of reflective units constituting the reconfigurable smart surface; (·) H represents the conjugate transpose operation; represents the conjugate transpose of hr,k; H d represents the downlink channel between the hybrid access point and the reconfigurable smart surface, and Θ 0 represents the energy reflection phase shift matrix of the reconfigurable smart surface, and θ 0,n represents the phase reflection coefficient of the nth reflection unit of the reconfigurable smart surface during energy transmission, and θ 0,n ∈[0,2π); represents the phase reflection coefficient θ 0,n The complex exponential form of represents the imaginary unit; let v 0 represents the reflection coefficient vector of the reconfigurable smart surface during energy transfer, (·) T Represents the transpose operation; (2) During the energy transmission time, in the inherent energy node mode, an energy collection model of the IoT device is constructed; specifically, it is expressed as follows: Among them, E c,k represents the energy collected by IoT device k through the inherent energy node during the energy transmission duration; represents the charging power of IoT device k in the inherent energy node mode; Indicates charging efficiency; represents the discharge power of IoT device k in the inherent energy node mode; Indicates discharge efficiency; (3) During the information transmission time, in the hybrid access point mode, a throughput model of the IoT device is constructed; specifically, it is expressed as follows: Among them, R h,k represents the throughput of IoT device k when transmitting information through the hybrid access point to collect energy within the information transmission time; (1-τ)T represents the information transmission time; represents the transmission power of IoT device k in the hybrid access point mode; g u,k represents the uplink channel between IoT device k and the hybrid access point, and g r,k represents the uplink channel between IoT device k and the reconfigurable smart surface, and G u represents the uplink channel between the reconfigurable smart surface and the hybrid access point, and Θ 1 represents the information reflection phase shift matrix of the reconfigurable smart surface, and θ 1,n represents the phase reflection coefficient of the nth reflection unit of the reconfigurable smart surface during information transmission, and θ 1,n ∈[0,2π); let v 1 Represents the reflection coefficient vector of the reconstructible smart surface during information transmission; represents the beamforming vector when decoding the information transmitted by IoT device k; K represents the number of single-antenna IoT devices; P h,i represents the transmission power of IoT device i in the hybrid access point mode; g u,i represents the uplink channel between IoT device i and the hybrid access point; g r,i represents the uplink channel between IoT device i and the reconfigurable smart surface; Indicates g r,i The conjugate transpose of r,i represents the transmit beamforming vector when decoding the information transmitted by IoT device i; σ 2 represents the additive white Gaussian noise power; (4) Within the information transmission time, under the inherent energy node mode, a throughput model of the IoT device is constructed; specifically, it is expressed as follows: Among them, R c,k It represents the throughput of IoT device k when transmitting information through inherent energy nodes within the information transmission duration; It represents the transmission power of IoT device k in transmitting information in the inherent energy node mode.

5. A load-based time-sharing energy resource pricing method according to claim 1, It is characterized in that In the step S2, when the IoT device collects energy in the hybrid access point mode and the inherent energy node mode, it obtains energy in the hybrid access point mode first; when the hybrid access point load exceeds a preset value, it obtains energy in the inherent energy node mode.

6. A load-based time-sharing energy resource pricing method according to claim 1, It is characterized in that In the step S2, when the IoT device collects energy in the inherent energy node mode, an inherent energy node is determined to provide energy for the IoT device according to the reputation value of the inherent energy node.

7. A load-based time-sharing energy resource pricing method according to claim 6, It is characterized in that The reputation value of the inherent energy node is expressed as: Among them, S j Represents the reputation value of the inherent energy node; N j N represents the number of times the inherent energy node j has been selected in history; max Indicates the maximum number of times the history is selected among all intrinsic energy nodes.

8. A load-based time-sharing energy resource pricing method according to claim 1, It is characterized in that The resource pricing model is expressed as: Where p represents the energy resource pricing of the hybrid access point or the inherent energy node; B represents the current load of the hybrid access point or the inherent energy node, and 0≤B≤B max , B max represents the maximum load; K represents the number of single-antenna IoT devices; k represents the kth IoT device; α k represents the energy demand indicator variable of IoT device k; α k =1 means that IoT device k needs energy supply; α k =0 means that IoT device k does not need energy supply; p 0 Indicates the initial price of energy resources of a hybrid access point or an inherent energy node.

9. A load-based time-sharing energy resource pricing method according to claim 2, It is characterized in that Also includes: S4. Taking the IoT device as the leader and the energy supply node as the follower, a Stackelberg game model of energy transaction between the energy supply node and the IoT device is established to maximize the utility function of the IoT device and the utility function of the energy supply node.

10. A load-based time-divided energy resource pricing method according to claim 9, It is characterized in that In the Stackelberg game model: (1) The utility function maximization problem of IoT devices is expressed as: s.t.C1:0≤B≤B max C2:0<τ<1 C3:|v 1 |=1 Among them, U I represents the utility function of the IoT device; r represents the reward for the IoT device to obtain unit throughput benefit; p represents the energy resource pricing of the hybrid access point or inherent energy node; w r,k represents the beamforming vector when decoding the information transmitted by IoT device k; τ represents the time allocation ratio; τT represents the energy transmission duration; R k represents the throughput of IoT device k when transmitting information through hybrid access points or inherent energy nodes to collect energy within the information transmission time; E k represents the energy collected by IoT device k through the hybrid access point or inherent energy node during the energy transmission duration; B represents the current load of the hybrid access point or inherent energy node, and B max Indicates the maximum load value; Θ 1 represents the information reflection phase shift matrix of the reconfigurable smart surface, and θ 1,n represents the phase reflection coefficient of the nth reflection unit of the reconfigurable smart surface during information transmission, and θ 1,n ∈[0,2π); let v 1 represents the reflection coefficient vector of the reconstructible smart surface during information transmission; C1 represents the first constraint; C2 represents the second constraint; C3 represents the third constraint; (2) The utility function maximization problem of the energy supply node is expressed as: s.t.C4:P e ≥0 C5:|v 0 |=1 Among them, U e represents the utility function of the energy supply node; w t,k represents the transmit beamforming vector of the hybrid access point; Θ 0 represents the energy reflection phase shift matrix of the reconfigurable smart surface; C(P e )=aP e 2 +bP e P represents the cost function of energy resources provided by energy supply nodes per unit time; e represents the transmission power of the hybrid access point or the charging power of the IoT device k in the inherent energy node mode; C4 represents the fourth constraint; C5 represents the fifth constraint.

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