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CN119130505B - Time-period energy resource pricing method based on load - Google Patents

Time-period energy resource pricing method based on load Download PDF

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CN119130505B
CN119130505B CN202411111485.1A CN202411111485A CN119130505B CN 119130505 B CN119130505 B CN 119130505B CN 202411111485 A CN202411111485 A CN 202411111485A CN 119130505 B CN119130505 B CN 119130505B
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关向瑞
薛建彬
张寒
许嘉玲
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Lanzhou University of Technology
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Abstract

本发明公开了一种基于负荷的分时段能量资源定价方法，包括如下步骤：基于时间块模型，在混合接入点模式和固有能量节点模式下，构建物联网设备的能量收集模型和吞吐量模型；使物联网设备在混合接入点模式和/或固有能量节点模式下收集能量；根据物联网设备能量供应需求，将物联网设备的能量收集划分为高峰时段和非高峰时段，并建立对应的资源定价模型；通过资源定价模型实现分时段能量资源定价。该方法通过资源定价模型在高峰时段适当上调能量资源价格，抑制物联网设备的接入；在非高峰时段适当降低能量资源价格，激励物联网设备接入获取能量；从而能够有效解决无线供电通信网络场景下物联网设备能量供应资源定价问题。

The present invention discloses a load-based time-division energy resource pricing method, comprising the following steps: based on a time block model, in a hybrid access point mode and an inherent energy node mode, construct an energy collection model and a throughput model for an Internet of Things device; enable the Internet of Things device to collect energy in a hybrid access point mode and/or an inherent energy node mode; divide the energy collection of the Internet of Things device into peak time periods and off-peak time periods according to the energy supply demand of the Internet of Things device, and establish a corresponding resource pricing model; and implement time-division energy resource pricing through the resource pricing model. The method appropriately raises the energy resource price during peak time periods through the resource pricing model to inhibit the access of Internet of Things devices; appropriately reduces the energy resource price during off-peak time periods to encourage Internet of Things devices to access and obtain energy; thereby effectively solving the energy supply resource pricing problem of Internet of Things devices in wireless power supply communication network scenarios.

Description

Time-period energy resource pricing method based on load
Technical Field
The invention relates to the technical field of wireless power supply communication networks, in particular to a load-based time-period energy resource pricing method.
Background
At present, the development of the internet of things (Internet of Things, ioT) has spawned various applications, and the execution of various tasks in the internet of things application needs to rely on energy-limited internet of things equipment, which brings challenges to the actual deployment of the internet of things, and the wireless power supply communication network (Wireless Powered Communication Networks, WPCN) can overcome the difficulty. The wireless power supply communication network provides sustainable energy supply for the Internet of things equipment by utilizing a radio frequency energy collection technology, and then supports information transmission of the Internet of things equipment by utilizing the collected energy. However, in practical applications, the efficiency of energy and information transmission is to be improved, as wireless powered communication networks face "dual attenuation".
The reconfigurable smart surface (Reconfigurable Intelligent Surface, RIS) can form a more favorable wireless propagation environment by changing the amplitude and phase of the incident signal, thereby improving energy and information transfer efficiency. The reconfigurable intelligent surface-assisted wireless power supply communication network can combine the advantages of the two, and the performance of the traditional wireless power supply communication network is improved. There is a preliminary study on reconfigurable intelligent surface-assisted wireless powered communication networks, however, the inventors have found that they have the following drawbacks in the course of studying the prior art:
First, a great deal of literature in the early days focused mainly on improving throughput and energy performance of wireless powered communication networks, with little concern from the perspective of service providers regarding resource cost issues in wireless powered communication networks. Second, much work is focused on the situation that wireless energy supply nodes provide energy for internet of things devices for free, and the li-liability and service capability (energy resource status) of the energy supply nodes are not considered, which is not practical. Finally, the problem of resource pricing of energy supply nodes in wireless powered communication networks is not addressed.
Therefore, there is a need for a method for solving the problem of pricing energy supply resources of devices of the internet of things in the context of a wireless power supply communication network, so as to study the cost of resources from the perspective of service providers.
Disclosure of Invention
In view of the above, the present invention provides a method for pricing load-based time-phased energy resources for reconfigurable intelligent surface-assisted wireless power communication networks, so as to solve at least some of the technical problems mentioned in the background art.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
A time-sharing energy resource pricing method based on load is applied to a time-sharing energy resource pricing system, and comprises the following steps:
s1, constructing an energy collection model and a throughput model of the Internet of things equipment under a hybrid access point mode and an inherent energy node mode based on a time block model;
S2, enabling the Internet of things equipment to collect energy in the hybrid access point mode and/or the inherent energy node mode;
S3, dividing energy collection of the Internet of things equipment into a peak period and a non-peak period according to the energy supply demand of the Internet of things equipment, establishing a corresponding resource pricing model, and realizing time-division energy resource pricing through the resource pricing model.
Further, the energy supply node of the hybrid access point mode is a hybrid access point, and the energy supply node of the inherent energy node mode is an inherent energy node.
Further, the time block model divides the time block with the duration of T into an energy transmission duration and an information transmission duration.
Further, the step S1 specifically includes:
(1) In the energy transmission time length, under the hybrid access point mode, an energy collection model of the Internet of things equipment is built, wherein the energy collection model is specifically expressed as follows:
wherein E h,k represents energy collected by the Internet of things device k through the hybrid access point in the energy transmission time period, eta represents energy conversion efficiency, tau represents energy transmission time period, P h represents transmission power of the hybrid access point, w t,k represents a transmission beam forming vector of the hybrid access point, and Represents a set of plural numbers,Represents a complex matrix set with the size of Mx1, M represents the number of antennas equipped by the hybrid access point, h d,k represents a downlink channel between the hybrid access point and the Internet of things equipment k, andH r,k denotes a downlink channel between the reconfigurable intelligent surface and the internet of things device k, andN represents the number of reflecting units constituting the reconfigurable intelligent surface, (. Cndot.) H represents the conjugate transpose operation; Represents the conjugate transpose of H r,k, H d represents the downlink channel between the hybrid access point and the reconfigurable intelligent surface, and Θ 0 represents the energy reflective phase shift matrix of the reconfigurable smart surface, andΘ 0,n represents the phase reflection coefficient of the nth reflection unit of the reconfigurable intelligent surface during energy transmission, and θ 0,n ε [0,2π); Representing the complex exponential form of the phase reflection coefficient theta 0,n, Representing imaginary units, letV 0 denotes the reflection coefficient vector of the reconfigurable intelligent surface upon energy transfer, (·) T denotes the transpose operation;
(2) In the energy transmission time length, under the inherent energy node mode, an energy collection model of the Internet of things equipment is built, and the method specifically comprises the following steps:
E c,k represents energy collected by the internet of things device k through an inherent energy node in energy transmission time; the charging power of the internet of things equipment k in the inherent energy node mode is represented; Indicating the charging efficiency; The discharging power of the internet of things equipment k in the inherent energy node mode is represented; Indicating discharge efficiency;
(3) In the information transmission time length, under the mixed access point mode, a throughput model of the Internet of things equipment is built, and the method specifically comprises the following steps:
Wherein R h,k represents throughput of the Internet of things device k when energy transmission information is collected through the hybrid access point in the information transmission time period (1-tau) T represents the information transmission time period; G u,k represents an uplink channel between the Internet of things equipment k and the hybrid access point, and G r,k denotes an uplink channel between the internet of things device k and the reconfigurable intelligent surface, andG u denotes the uplink channel between the reconfigurable intelligent surface and the hybrid access point, andΘ 1 represents the information reflective phase shift matrix of the reconfigurable smart surface, andTheta 1,n represents the phase reflection coefficient of the nth reflection unit of the reconfigurable intelligent surface during information transmission, and theta 1,n E [0,2 pi ], letV 1 represents a reflection coefficient vector of the reconfigurable intelligent surface during information transmission; The method comprises the steps of decoding a beam forming vector when information transmitted by an Internet of things device K is decoded, wherein K represents the number of single-antenna Internet of things devices, P h,i represents the transmitting power of the information transmitted by the Internet of things device i in a hybrid access point mode, g u,i represents an uplink channel between the Internet of things device i and a hybrid access point, and g r,i represents an uplink channel between the Internet of things device i and a reconfigurable intelligent surface; Represents the conjugate transpose of g r,i, w r,i represents the transmit beamforming vector when decoding the information transmitted by the internet of things device i, σ 2 represents the additive white gaussian noise power;
(4) In the information transmission time length, under the inherent energy node mode, a throughput model of the Internet of things equipment is built, and the method specifically comprises the following steps:
wherein, R c,k represents throughput when the internet of things device k collects energy transmission information through the inherent energy node in the information transmission time; and the transmission power of the information transmitted by the internet of things device k in the inherent energy node mode is represented.
Further, in the step S2, when the internet of things device collects energy in the hybrid access point mode and the inherent energy node mode, energy is preferentially acquired in the hybrid access point mode, and when the hybrid access point load exceeds a preset value, energy is acquired in the inherent energy node mode.
Further, in the step S2, when the internet of things device collects energy in the intrinsic energy node mode, it is determined that one intrinsic energy node provides energy for the internet of things device according to the reputation value of the intrinsic energy node.
Further, the reputation value of the inherent energy node is expressed as:
Where S j represents the reputation value of the inherent energy node, N j represents the number of times the history of inherent energy node j was selected, and N max represents the maximum number of times the history of all inherent energy nodes was selected.
Further, the resource pricing model is expressed as:
wherein p represents energy resource pricing of the hybrid access point or the inherent energy node, B represents current load of the hybrid access point or the inherent energy node, and B is more than or equal to 0 and less than or equal to B max,Bmax, K is the number of single-antenna internet of things equipment, K is the kth internet of things equipment, alpha k is an energy demand indicating variable of the internet of things equipment K, alpha k =1 is that the internet of things equipment K needs energy supply, alpha k =0 is that the internet of things equipment K does not need energy supply, and p 0 is an energy resource initial price of a hybrid access point or an inherent energy node.
Further, the method further comprises the following steps:
S4, taking the Internet of things equipment as a leader, taking the energy supply node as a follower, and establishing a Stackelberg game model of energy transaction between the energy supply node and the Internet of things equipment, so as to maximize utility functions of the Internet of things equipment and the energy supply node.
Further, in the Stackelberg gaming model:
(1) The utility function maximization problem of the Internet of things equipment is expressed as:
s.t.C1:0≤B≤Bmax
C2:0<τ<1
C3:|v1|=1
Wherein U I represents a utility function of the Internet of things device, R represents rewards of unit throughput gain of the Internet of things device, p represents energy resource pricing of a hybrid access point or an inherent energy node, w r,k represents a beamforming vector when decoding information transmitted by the Internet of things device k, τ represents a time distribution ratio, τT represents an energy transmission duration, R k represents throughput when the Internet of things device k collects energy transmission information through the hybrid access point or the inherent energy node within the information transmission duration, E k represents energy collected by the Internet of things device k through the hybrid access point or the inherent energy node within the energy transmission duration, B represents current load of the hybrid access point or the inherent energy node, B max represents a load maximum, Θ 1 represents an information reflection phase shift matrix of a reconfigurable intelligent surface, and Theta 1,n represents the phase reflection coefficient of the nth reflection unit of the reconfigurable intelligent surface during information transmission, and theta 1,n E [0,2 pi ], letV 1 represents a reflection coefficient vector of the reconfigurable intelligent surface during information transmission, C1 represents a first constraint condition, C2 represents a second constraint condition, and C3 represents a third constraint condition;
(2) The energy supply node utility function maximization problem is expressed as:
s.t.C4:Pe≥0
C5:|v0|=1
Wherein 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 reflective phase shift matrix of the reconfigurable smart surface; The method comprises the steps of representing a cost function of energy resources provided by an energy supply node in unit time, P e representing the transmitting power of a hybrid access point or the charging power of the Internet of things equipment k in an inherent energy node mode, wherein the charging power of the Internet of things equipment k in the inherent energy node mode is equal to the transmitting power of the inherent energy node, C4 representing a fourth constraint condition, and C5 representing a fifth constraint condition.
Compared with the prior art, the invention discloses a time-period energy resource pricing method based on load, which has the following beneficial effects:
According to the method, the energy resource price is properly adjusted in the peak time through the resource pricing model, the access of the Internet of things equipment is restrained, the energy resource price is properly reduced in the off-peak time, the Internet of things equipment is stimulated to access to obtain energy, and therefore the problem of pricing of the energy supply resources of the Internet of things equipment in a wireless power supply communication network scene can be effectively solved.
The method builds a Stackelberg game model of energy transaction between the energy supply node and the Internet of things equipment, gives the Internet of things equipment proper rewards in the information transmission stage of the Internet of things equipment so as to excite the Internet of things equipment to transmit information with larger transmitting power, thereby obtaining higher throughput, and finally enables the utility of both parties to be maximum through games between the energy supply node and the Internet of things equipment.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of a usage scenario of a time-phased energy resource pricing system according to an embodiment of the present invention.
Fig. 2 is a schematic flow chart of a method for pricing a load-based time-phased energy resource according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment of the invention discloses a load-based time-sharing energy resource pricing method, which is applied to a time-sharing energy resource pricing system, wherein the time-sharing energy resource pricing system is shown in figure 1 and comprises a hybrid access point provided with M antennas, a reconfigurable intelligent surface formed by N reflecting units, a RIS controller embedded into the reconfigurable intelligent surface, K single-antenna Internet of things devices, J inherent energy nodes and a central control unit embedded into the hybrid access point, wherein:
The hybrid access point is used for broadcasting energy to the Internet of things equipment and receiving information transmitted by the Internet of things equipment;
the inherent energy node provides energy for the equipment of the Internet of things in a leasing mode;
a Reconfigurable Intelligent Surface (RIS) for adjusting a wireless propagation environment between a hybrid access point and an Internet of things device;
a reconfigurable intelligent surface controller (RIS controller) for controlling the reconfigurable intelligent surface;
the internet of things equipment is used for energy receiving and information sending;
And the central control unit is used for adjusting the energy transaction prices and the energy acquisition modes in different time periods.
Based on the time-sharing energy resource pricing system, the load-based time-sharing energy resource pricing method provided by the invention is shown in figure 2, and comprises the following steps of
S1, constructing an energy collection model and a throughput model of the Internet of things equipment under a hybrid access point mode and an inherent energy node mode based on a time block model;
S2, the Internet of things equipment collects energy in a hybrid access point mode and/or an inherent energy node mode;
S3, dividing energy collection of the Internet of things equipment into a peak period and a non-peak period according to the energy supply demand of the Internet of things equipment, establishing a corresponding resource pricing model, and realizing time-interval energy resource pricing through the resource pricing model.
The respective steps described above are described in detail below.
In the step S1, an energy collection model and a throughput model of the internet of things device in two energy supply modes are established based on the time block model;
In the time block model, a time block with the duration of T is divided into two parts for energy transmission and information transmission respectively, and the energy transmission duration is defined as tau (1-tau) T, wherein tau represents the time distribution proportion and tau epsilon (0, 1);
The two energy supply modes comprise 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 resource provided by the hybrid access point is called radio frequency resource, and the resource provided by the inherent energy node is called leased resource;
Based on the above, based on the time block model, respectively in a hybrid access point mode and an inherent energy node mode, an energy collection model and a throughput model of the internet of things device are constructed, which specifically comprises:
(1) In the energy transmission time length, under the hybrid access point mode, an energy collection model of the Internet of things equipment is built, wherein the energy collection model is specifically expressed as follows:
wherein E h,k represents energy collected by the Internet of things device k through the hybrid access point in the energy transmission time period, eta represents energy conversion efficiency, tau represents energy transmission time period, P h represents transmission power of the hybrid access point, w t,k represents a transmission beam forming vector of the hybrid access point, and Represents a set of plural numbers,Represents a complex matrix set with the size of Mx1, M represents the number of antennas equipped by the hybrid access point, h d,k represents a downlink channel between the hybrid access point and the Internet of things equipment k, andH r,k denotes a downlink channel between the reconfigurable intelligent surface and the internet of things device k, andN represents the number of reflecting units constituting the reconfigurable intelligent surface, (. Cndot.) H represents the conjugate transpose operation; Represents the conjugate transpose of H r,k, H d represents the downlink channel between the hybrid access point and the reconfigurable intelligent surface, and Θ 0 represents the energy reflective phase shift matrix of the reconfigurable smart surface, andΘ 0,n represents the phase reflection coefficient of the nth reflection unit of the reconfigurable intelligent surface during energy transmission, and θ 0,n ε [0,2π); Representing the complex exponential form of the phase reflection coefficient theta 0,n, Representing imaginary units, letV 0 denotes the reflection coefficient vector of the reconfigurable intelligent surface upon energy transfer, (·) T denotes the transpose operation;
(2) In the energy transmission time length, under the inherent energy node mode, an energy collection model of the Internet of things equipment is built, and the method specifically comprises the following steps:
E c,k represents energy collected by the internet of things device k through an inherent energy node in energy transmission time; the charging power of the internet of things equipment k in the inherent energy node mode is represented; Indicating the charging efficiency; The discharging power of the internet of things equipment k in the inherent energy node mode is represented; Indicating discharge efficiency;
(3) In the information transmission time length, under the mixed access point mode, a throughput model of the Internet of things equipment is built, and the method specifically comprises the following steps:
Wherein R h,k represents throughput of the Internet of things device k when energy transmission information is collected through the hybrid access point in the information transmission time period (1-tau) T represents the information transmission time period; G u,k represents an uplink channel between the Internet of things equipment k and the hybrid access point, and G r,k denotes an uplink channel between the internet of things device k and the reconfigurable intelligent surface, andG u denotes the uplink channel between the reconfigurable intelligent surface and the hybrid access point, andΘ 1 represents the information reflective phase shift matrix of the reconfigurable smart surface, andTheta 1,n represents the phase reflection coefficient of the nth reflection unit of the reconfigurable intelligent surface during information transmission, and theta 1,n E [0,2 pi ], letV 1 represents a reflection coefficient vector of the reconfigurable intelligent surface during information transmission; The method comprises the steps of decoding a beam forming vector when information transmitted by an Internet of things device K is decoded, wherein K represents the number of single-antenna Internet of things devices, P h,i represents the transmitting power of the information transmitted by the Internet of things device i in a hybrid access point mode, g u,i represents an uplink channel between the Internet of things device i and a hybrid access point, and g r,i represents an uplink channel between the Internet of things device i and a reconfigurable intelligent surface; Represents the conjugate transpose of g r,i, w r,i represents the transmit beamforming vector when decoding the information transmitted by the internet of things device i, σ 2 represents the additive white gaussian noise power;
(4) In the information transmission time length, under the inherent energy node mode, a throughput model of the Internet of things equipment is built, and the method specifically comprises the following steps:
wherein, R c,k represents throughput when the internet of things device k collects energy transmission information through the inherent energy node in the information transmission time; and the transmission power of the information transmitted by the internet of things device k in the inherent energy node mode is represented.
In the step S2, the internet of things device collects energy in the hybrid access point mode and/or the inherent energy node mode;
When the load of the hybrid access point exceeds a preset value (for example, 80%), the energy is acquired in the inherent energy node mode;
when the Internet of things equipment collects energy in an inherent energy node mode, determining one inherent energy node to provide energy for the Internet of things equipment according to the reputation value of the inherent energy node;
the reputation value of an inherent energy node is expressed as:
The method comprises the steps of S j, N j, N max and N max, wherein the S j represents the reputation value of an inherent energy node, the N j represents the historical selected times of the inherent energy node, the N max represents the maximum historical selected times of all inherent energy nodes, and the inherent energy node with the highest reputation value is selected to supply energy for the Internet of things equipment. At the same time, the internet of things equipment can only select one energy supply mode.
In the step S3, according to the energy supply requirement of the internet of things device, the energy collection of the internet of things device is divided into a peak period and a non-peak period, and a corresponding resource pricing model is established, the time-division energy resource pricing is realized through the resource pricing model, and the resource pricing model is expressed as:
wherein p represents energy resource pricing of the hybrid access point or the inherent energy node, B represents current load of the hybrid access point or the inherent energy node, and B is more than or equal to 0 and less than or equal to B max,Bmax, K is the number of single-antenna internet of things equipment, K is the kth internet of things equipment, alpha k is an energy demand indicating variable of the internet of things equipment K, alpha k =1 is that the internet of things equipment K needs energy supply, alpha k =0 is that the internet of things equipment K does not need energy supply, and p 0 is an energy resource initial price of a hybrid access point or an inherent energy node.
In another embodiment, the method further comprises:
S4, taking the Internet of things equipment as a leader, taking the energy supply node as a follower, establishing a Stackelberg game model of energy transaction between the energy supply node and the Internet of things equipment, and maximizing utility functions of the Internet of things equipment and the energy supply node, wherein the method comprises the following steps:
The energy supply node comprises a hybrid access point or an inherent energy node and is used for providing radio frequency resources or leasing resources for the Internet of things equipment, the energy supply node charges the Internet of things equipment, the energy collected by the Internet of things equipment is used for information transmission, the Internet of things equipment obtains rewards in an information transmission stage, and the Internet of things equipment is stimulated to transmit information with higher transmitting power to obtain higher throughput, so that the utility function of the Internet of things equipment is expressed as the difference between throughput benefit and energy expenditure and is expressed as:
UI＝rRk-pEk
The energy supply node utility function is expressed as the difference between the energy return and the energy cost, expressed as:
Ue＝pEk-τTC(Pe)
wherein, R k＝{Rh,k,Rc,k},Ek＝{Eh,k,Ec,k }, Representing the cost of providing energy resources per unit time by an energy supply node, whereinP h denotes the transmit power of the hybrid access point; the charging power of the Internet of things equipment k in the inherent energy node mode is represented The transmitting power of the node is equal to that of the inherent energy node, and a and b are normal numbers which can be obtained through actual data fitting;
the internet of things equipment decides whether to purchase energy or not, so the internet of things equipment is taken as a leader, an energy supply node is taken as a follower, and the following Stackelberg game model is established:
(1) The utility function maximization problem of the Internet of things equipment is expressed as:
s.t.C1:0≤B≤Bmax
C2:0<τ<1
C3:|v1|=1
Wherein U I represents a utility function of the Internet of things device, R represents rewards of unit throughput gain of the Internet of things device, p represents energy resource pricing of a hybrid access point or an inherent energy node, w r,k represents a beamforming vector when decoding information transmitted by the Internet of things device k, τ represents a time distribution ratio, τT represents an energy transmission duration, R k represents throughput when the Internet of things device k collects energy transmission information through the hybrid access point or the inherent energy node within the information transmission duration, E k represents energy collected by the Internet of things device k through the hybrid access point or the inherent energy node within the energy transmission duration, B represents current load of the hybrid access point or the inherent energy node, B max represents a load maximum, Θ 1 represents an information reflection phase shift matrix of a reconfigurable intelligent surface, and Theta 1,n represents the phase reflection coefficient of the nth reflection unit of the reconfigurable intelligent surface during information transmission, and theta 1,n E [0,2 pi ], letV 1 denotes a reflection coefficient vector of the reconfigurable intelligent surface in information transmission, C1 denotes a first constraint condition that the load of the energy supply node cannot exceed a maximum value B max, C2 denotes a second constraint condition that the distribution ratio of energy and information transmission time is between (0, 1), C3 denotes a third constraint condition that the phase reflection coefficient module of the reconfigurable intelligent surface in information transmission is 1;
(2) The energy supply node utility function maximization problem is expressed as:
s.t.C4:Pe≥0
C5:|v0|=1
Wherein 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 reflective phase shift matrix of the reconfigurable smart surface; The method comprises the steps of representing a cost function of energy resources provided by an energy supply node in unit time, P e representing the transmitting power of a hybrid access point or the charging power of an Internet of things device k in an inherent energy node mode, wherein the charging power of the Internet of things device k in the inherent energy node mode is equal to the transmitting power of the inherent energy node, C4 representing a fourth constraint condition, representing that the transmitting power of the energy supply node is non-negative, C5 representing a fifth constraint condition, and representing that the phase reflection coefficient of a reconfigurable intelligent surface is 1 during energy transmission.
In summary, aiming at the problem that the wireless power supply communication network of the Internet of things is assisted by a reconfigurable intelligent surface, the invention aims at the problem of resource cost from the viewpoint of a service provider, provides a time-sharing energy resource pricing method based on load, which is used for properly adjusting the energy resource price in peak time and inhibiting the access of Internet of things equipment, properly reducing the energy resource price in off-peak time and exciting the access of the Internet of things equipment to acquire energy.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (8)

1.一种基于负荷的分时段能量资源定价方法，应用于分时段能量资源定价系统，其特征在于，包括如下步骤：1. A load-based time-division energy resource pricing method, applied to a time-division energy resource pricing system, characterized in that it comprises the following steps: S1、基于时间块模型，在混合接入点模式和固有能量节点模式下，构建物联网设备的能量收集模型和吞吐量模型；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、使所述物联网设备在所述混合接入点模式和/或所述固有能量节点模式下收集能量；S2. enabling the IoT device to collect energy in the hybrid access point mode and/or the inherent energy node mode; S3、根据物联网设备能量供应需求，将物联网设备的能量收集划分为高峰时段和非高峰时段，并建立对应的资源定价模型；通过所述资源定价模型实现分时段能量资源定价；S3. According to the energy supply demand of the IoT devices, the energy collection of the 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; 所述步骤S1具体包括：The step S1 specifically includes: (1)在能量传输时长内，在所述混合接入点模式下，构建物联网设备的能量收集模型；具体表示为：(1) During the energy transmission time, in the hybrid access point mode, an energy collection model of the IoT device is constructed; specifically, it is expressed as follows: 其中，Eh,k表示物联网设备k在能量传输时长内通过混合接入点收集的能量；η表示能量转换效率；τT表示能量传输时长；Ph表示混合接入点的发射功率；wt,k表示混合接入点的发射波束成形矢量，且 表示复数集，表示大小为M×1的复矩阵集合，M表示混合接入点配备的天线数量；hd,k表示混合接入点与物联网设备k之间的下行信道，且hr,k表示可重构智能表面与物联网设备k之间的下行信道，且N表示构成可重构智能表面的反射单元数量；(·)H表示共轭转置运算；表示hr,k的共轭转置；Hd表示混合接入点与可重构智能表面之间的下行信道，且Θ0表示可重构智能表面的能量反射相移矩阵，且θ0,n表示能量传输时可重构智能表面的第n个反射单元的相位反射系数，且θ0,n∈[0,2π)；表示相位反射系数θ0,n的复指数形式，表示虚数单位；令v0表示能量传输时可重构智能表面的反射系数矢量，表示转置运算；Where E h,k represents the energy collected by IoT device k through the hybrid access point during the energy transmission duration; η represents the energy conversion efficiency; τT represents the energy transmission duration; P h represents the transmit 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 set of complex matrices of size M×1, 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; Hd 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 complex exponential form of the phase reflection coefficient θ 0,n , represents the imaginary unit; let v 0 represents the reflection coefficient vector of the reconfigurable smart surface during energy transfer, Represents the transpose operation; (2)在所述能量传输时长内，在所述固有能量节点模式下，构建物联网设备的能量收集模型；具体表示为：(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: 其中，Ec,k表示物联网设备k在能量传输时长内通过固有能量节点收集的能量；表示物联网设备k在固有能量节点模式下的充电功率；表示充电效率；表示物联网设备k在固有能量节点模式下的放电功率；表示放电效率；Where 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)在信息传输时长内，在所述混合接入点模式下，构建物联网设备的吞吐量模型；具体表示为：(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: 其中，Rh,k表示物联网设备k在信息传输时长内通过混合接入点收集能量传输信息时的吞吐量；(1-τ)T表示信息传输时长；表示物联网设备k在混合接入点模式下传输信息的发射功率；gu,k表示物联网设备k与混合接入点之间的上行信道，且gr,k表示物联网设备k与可重构智能表面之间的上行信道，且Gu表示可重构智能表面与混合接入点之间的上行信道，且Θ1表示可重构智能表面的信息反射相移矩阵，且θ1,n表示信息传输时可重构智能表面的第n个反射单元的相位反射系数，且θ1,n∈[0,2π)；令v1表示信息传输时可重构智能表面的反射系数矢量；表示解码物联网设备k所传输信息时的波束成形矢量；K表示单天线物联网设备的数量；Ph,i表示物联网设备i在混合接入点模式下传输信息的发射功率；gu,i表示物联网设备i与混合接入点之间的上行信道；gr,i表示物联网设备i与可重构智能表面之间的上行信道；表示gr,i的共轭转置；wr,i表示解码物联网设备i所传输信息时的发射波束成形矢量；σ2表示加性高斯白噪声功率；Where R h,k represents the throughput of IoT device k when it collects energy and transmits information through the hybrid access point 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; Ph,i represents the transmit power of IoT device i in the hybrid access point mode; gu ,i represents the uplink channel between IoT device i and the hybrid access point; gr ,i represents the uplink channel between IoT device i and the reconfigurable smart surface; represents the conjugate transpose of g r,i ; w 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)在所述信息传输时长内，在所述固有能量节点模式下，构建物联网设备的吞吐量模型；具体表示为：(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: 其中，Rc,k表示物联网设备k在信息传输时长内通过固有能量节点收集能量传输信息时的吞吐量；表示物联网设备k在固有能量节点模式下传输信息的发射功率；Where R c,k represents the throughput of IoT device k when transmitting information through inherent energy nodes during information transmission time; represents the transmission power of IoT device k in transmitting information in the inherent energy node mode; 所述资源定价模型表示为：The resource pricing model is expressed as: 其中，p表示混合接入点或固有能量节点的能量资源定价；B表示混合接入点或固有能量节点的当前负荷，且0≤B≤Bmax，Bmax表示负荷最大值；K表示单天线物联网设备的数量；k表示第k个物联网设备；αk表示物联网设备k的能量需求指示变量；αk＝1表示物联网设备k需要能量供应；αk＝0表示物联网设备k不需要能量供应；p0表示混合接入点或固有能量节点的能量资源初始价格。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 represents that IoT device k requires energy supply; α k =0 represents that IoT device k does not require energy supply; p 0 represents the initial price of energy resources of the hybrid access point or inherent energy node. 2.根据权利要求1所述的一种基于负荷的分时段能量资源定价方法，其特征在于，所述混合接入点模式的能量供应节点为混合接入点；所述固有能量节点模式的能量供应节点为固有能量节点。2. A load-based time-segment energy resource pricing method according to claim 1, 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.根据权利要求2所述的一种基于负荷的分时段能量资源定价方法，其特征在于，所述时间块模型中将时长为T的时间块分为能量传输时长和信息传输时长。3. A load-based time-period energy resource pricing method according to claim 2, characterized in that in the time block model, a time block with a duration of T is divided into energy transmission duration and information transmission duration. 4.根据权利要求1所述的一种基于负荷的分时段能量资源定价方法，其特征在于，在所述步骤S2中，当所述物联网设备在所述混合接入点模式和所述固有能量节点模式下收集能量时，优先在所述混合接入点模式下获取能量；当混合接入点负荷超过预设值时，再在所述固有能量节点模式下获取能量。4. A load-based time-segment energy resource pricing method according to claim 1, characterized in that, in step S2, when the Internet of Things 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. 5.根据权利要求1所述的一种基于负荷的分时段能量资源定价方法，其特征在于，在所述步骤S2中，当所述物联网设备在所述固有能量节点模式下收集能量时，根据固有能量节点的信誉值，来确定一个固有能量节点为物联网设备提供能量。5. According to a load-based time-segment energy resource pricing method as described in claim 1, it is characterized in that, in the step S2, when the Internet of Things device collects energy in the inherent energy node mode, an inherent energy node is determined to provide energy for the Internet of Things device based on the credibility value of the inherent energy node. 6.根据权利要求5所述的一种基于负荷的分时段能量资源定价方法，其特征在于，所述固有能量节点的信誉值，表示为：6. A load-based time-divided energy resource pricing method according to claim 5, characterized in that the reputation value of the inherent energy node is expressed as: 其中，Sj表示固有能量节点的信誉值；Nj表示固有能量节点j历史被选择次数；Nmax表示所有固有能量节点中历史被选择的最大次数。Wherein, Sj represents the reputation value of the inherent energy node; Nj represents the number of times inherent energy node j has been selected historically; and Nmax represents the maximum number of times all inherent energy nodes have been selected historically. 7.根据权利要求2所述的一种基于负荷的分时段能量资源定价方法，其特征在于，还包括：7. The load-based time-division energy resource pricing method according to claim 2, characterized in that it also includes: S4、将物联网设备作为领导者，将能量供应节点作为跟随者，建立能量供应节点与物联网设备之间能量交易的Stackelberg博弈模型，实现将物联网设备效用函数和能量供应节点效用函数最大化。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. 8.根据权利要求7所述的一种基于负荷的分时段能量资源定价方法，其特征在于，在所述Stackelberg博弈模型中：8. A load-based time-divided energy resource pricing method according to claim 7, characterized in that, in the Stackelberg game model: (1)将物联网设备效用函数最大化问题表示为：(1) The utility function maximization problem of IoT devices is expressed as: s.t.C1:0≤B≤Bmax stC1:0≤B≤B max C2:0＜τ＜1C2:0＜τ＜1 C3:|v1|＝1C3:|v 1 |＝1 其中，UI表示物联网设备的效用函数；r表示物联网设备获得单位吞吐量收益的奖励；p表示混合接入点或固有能量节点的能量资源定价；wr,k表示解码物联网设备k所传输信息时的波束成形矢量；τ表示时间分配比例；τT表示能量传输时长；Rk表示物联网设备k在信息传输时长内通过混合接入点或固有能量节点收集能量传输信息时的吞吐量；Ek表示物联网设备k在能量传输时长内通过混合接入点或固有能量节点收集的能量；B表示混合接入点或固有能量节点的当前负荷，Bmax表示负荷最大值；Θ1表示可重构智能表面的信息反射相移矩阵，且θ1,n表示信息传输时可重构智能表面的第n个反射单元的相位反射系数，且θ1,n∈[0,2π)；令v1表示信息传输时可重构智能表面的反射系数矢量；C1表示第一约束条件；C2表示第二约束条件；C3表示第三约束条件；Wherein, UI 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 the intrinsic energy node; wr,k represents the beamforming vector when decoding the information transmitted by the IoT device k; τ represents the time allocation ratio; τT represents the energy transmission duration; Rk represents the throughput of the IoT device k when collecting energy to transmit information through the hybrid access point or the intrinsic energy node during the information transmission duration; Ek represents the energy collected by the IoT device k through the hybrid access point or the intrinsic energy node during the energy transmission duration; B represents the current load of the hybrid access point or the intrinsic energy node, and Bmax 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 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)将能量供应节点效用函数最大化问题表示为：(2) The utility function maximization problem of the energy supply node is expressed as: s.t.C4:Pe≥0stC4:P e ≥0 C5:|v0|＝1C5:|v 0 |＝1 其中，Ue表示能量供应节点的效用函数；wt,k表示混合接入点的发射波束成形矢量；Θ0表示可重构智能表面的能量反射相移矩阵；C(Pe)＝aPe 2+bPe表示单位时间内能量供应节点提供能量资源的成本函数；Pe表示混合接入点的发射功率或物联网设备k在固有能量节点模式下的充电功率；C4表示第四约束条件；C5表示第五约束条件。Wherein, Ue represents the utility function of the energy supply node; wt,k represents the transmit beamforming vector of the hybrid access point; Θ0 represents the energy reflection phase shift matrix of the reconfigurable smart surface; C( Pe ) = aPe2 + bPe represents the cost function of the energy supply node providing energy resources per unit time; Pe represents the transmit 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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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113162798A (en) * 2021-03-03 2021-07-23 国网能源研究院有限公司 Information transmission optimization method and system of wireless power supply communication network
CN113810233A (en) * 2021-09-17 2021-12-17 重庆邮电大学 A Distributed Computing Offloading Method Based on Computational Network Collaboration in Random Networks

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10552294B2 (en) * 2017-03-31 2020-02-04 Commvault Systems, Inc. Management of internet of things devices
WO2021154610A1 (en) * 2020-01-30 2021-08-05 Idac Holdings, Inc. Method of network-assisted beamformed energy harvesting signaling and corresponding apparatus
CN115150791B (en) * 2022-06-29 2025-04-08 重庆邮电大学 D2D communication resource reuse method, system, terminal and medium based on STACKELBERG game
CN116367194A (en) * 2023-03-29 2023-06-30 重庆邮电大学 A full-duplex wireless power supply backscatter communication system resource allocation method

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113162798A (en) * 2021-03-03 2021-07-23 国网能源研究院有限公司 Information transmission optimization method and system of wireless power supply communication network
CN113810233A (en) * 2021-09-17 2021-12-17 重庆邮电大学 A Distributed Computing Offloading Method Based on Computational Network Collaboration in Random Networks

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