CN112533275A - Power control and interference pricing method and device for renewable energy heterogeneous network - Google Patents

Power control and interference pricing method and device for renewable energy heterogeneous network Download PDF

Info

Publication number
CN112533275A
CN112533275A CN202011273448.2A CN202011273448A CN112533275A CN 112533275 A CN112533275 A CN 112533275A CN 202011273448 A CN202011273448 A CN 202011273448A CN 112533275 A CN112533275 A CN 112533275A
Authority
CN
China
Prior art keywords
mbs
sbs
interference
renewable energy
heterogeneous network
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202011273448.2A
Other languages
Chinese (zh)
Other versions
CN112533275B (en
Inventor
许海涛
张颖
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
University of Science and Technology Beijing USTB
Original Assignee
University of Science and Technology Beijing USTB
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by University of Science and Technology Beijing USTB filed Critical University of Science and Technology Beijing USTB
Priority to CN202011273448.2A priority Critical patent/CN112533275B/en
Publication of CN112533275A publication Critical patent/CN112533275A/en
Application granted granted Critical
Publication of CN112533275B publication Critical patent/CN112533275B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/04—TPC
    • H04W52/18—TPC being performed according to specific parameters
    • H04W52/24—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters
    • H04W52/243—TPC being performed according to specific parameters using SIR [Signal to Interference Ratio] or other wireless path parameters taking into account interferences
    • H04W52/244—Interferences in heterogeneous networks, e.g. among macro and femto or pico cells or other sector / system interference [OSI]

Abstract

The invention discloses a power control and interference pricing method and a device of a renewable energy heterogeneous network, wherein the method comprises the following steps: constructing a renewable energy heterogeneous network system model; constructing state models of MBS and SBS in the heterogeneous network; constructing MBS and SBS cost objective functions; the MBS target is used for searching an optimal interference pricing strategy, and the SBS target is used for controlling the transmitting power of the SBS target according to the interference price provided by the MBS so as to minimize the cost; and constructing a dynamic game model by taking the MBS as a leader and the SBS as a follower, and solving Nash equilibrium solutions of the dynamic game model in open-loop and feedback modes to obtain an optimal power distribution and optimal interference pricing strategy. The invention can optimize the power resource allocation and effectively manage the power resource to reduce the interference in the heterogeneous network.

Description

Power control and interference pricing method and device for renewable energy heterogeneous network
Technical Field
The invention relates to the technical field of wireless communication, in particular to a power control and interference pricing method and device for a renewable energy heterogeneous network.
Background
With the exponential growth of intelligent terminals and the increase of traffic demands, more infrastructure nodes need to be developed to improve the network service quality and effectively improve the system capacity. One approach is to deploy more Macro Base Stations (MBS), which can be difficult and expensive. Another approach is to introduce small cells in the macro-cell. Introducing more Small Cell Base Stations (SBS) of different sizes, service capabilities and coverage into macro cells can be considered as a very efficient approach to meet the ever-increasing data throughput demands. Meanwhile, the network structure will also be shifted from the conventional mobile network to the heterogeneous network. Heterogeneous networks are networks consisting of Base Stations (BSs) produced by different manufacturers, including MBSs, SBSs, etc., that have been implemented to support high service and capacity requirements. The increase in the number of BSs in heterogeneous networks has even made it possible for smart devices to be associated with individual BSs in the future.
On the other hand, intensive deployment of a large number of SBSs in a heterogeneous network may lead to problems of lack of coordination and inefficient operation. Interference, particularly intra-cell interference, is a major limitation and challenge for heterogeneous networks. The intra-cell interference is mainly caused by spectrum sharing between the MBSs and the SBSs, and can affect the network performance. It can be divided into two categories: cross-layer interference and co-layer interference. The former is interference between MBS and SBS due to coverage overlap and spectrum sharing, also referred to as inter-layer interference. The latter is the interference between SBSs, which can be mitigated if the BSs are well coordinated. The focus is how to solve the cross-layer interference problem.
In addition, the widespread development of heterogeneous BSs in heterogeneous networks has led to a proliferation of problems such as energy consumption and electricity charges. Due to the rapid development of renewable energy technologies, including wind energy, solar energy, and so on, BSs can use renewable energy to solve the problem of increasingly severe energy consumption and improve the energy efficiency of heterogeneous networks. A large amount of renewable energy sources such as wind energy, solar energy and the like need to be integrated into a heterogeneous network, so that green communication is realized. Renewable energy is becoming an important component in achieving sustainable development.
However, how to implement power control and interference pricing strategies between MBS and SBSs in a renewable energy Heterogeneous network (HetNets) to achieve power resource allocation optimization and power resource effective management, so as to reduce interference in the Heterogeneous network is still a difficult problem.
Disclosure of Invention
The invention provides a power control and interference pricing method and device for a renewable energy heterogeneous network, which aim to realize optimized power resource allocation and effective management of power resources in the renewable energy heterogeneous network, thereby reducing the interference problem in the renewable energy heterogeneous network.
In order to solve the technical problems, the invention provides the following technical scheme:
in one aspect, the present invention provides a power control and interference pricing method for a renewable energy heterogeneous network, where the power control and interference pricing method for the renewable energy heterogeneous network includes:
constructing a renewable energy heterogeneous network system model; the renewable energy heterogeneous network system model comprises a macro base station MBS and a plurality of small cell base stations SBS, wherein the MBS and the SBS use the same frequency spectrum resource, the MBS controls the interference price, and the SBS pays the interference cost to the MBS according to the interference price provided by the MBS and controls the transmitting power of the SBS according to the interference price provided by the MBS;
constructing state models of MBS and SBS in the renewable energy heterogeneous network;
constructing MBS and SBS cost objective functions; the MBS target is used for searching an optimal interference pricing strategy, and the SBS target is used for controlling the transmitting power of the SBS target according to the interference price provided by the MBS so as to minimize the cost;
and constructing a dynamic game model by taking the MBS as a leader and the SBS as a follower, and solving Nash equilibrium solutions of the dynamic game model in open-loop and feedback modes to obtain an optimal power distribution and optimal interference pricing strategy.
Further, the renewable energy heterogeneous network system model is a two-layer heterogeneous network system, the top layer of the system is an MBS, the bottom layer of the system comprises a plurality of SBS, and the co-layer interference between SBS is negligible.
Further, the interference between SBS and MBS is paid for:
Figure BDA0002778391480000021
wherein，Iim(t) payment for interference between the ith SBS and MBS um(t) interference price for MBS, gim(t) channel gain in MBS of i-th SBS, pi(t) is the transmission power of the ith SBS.
Further, in the renewable energy heterogeneous network system model, the energy of the MBS is provided by a conventional power grid, the energy source of the SBS is heterogeneous energy, and includes the conventional power grid and the renewable energy, and the SBS collects energy from the renewable energy and stores the collected energy in the SBS under the driving of the heterogeneous energy.
Further, in the state model, the energy storage dynamics of SBS is characterized as:
Figure BDA0002778391480000022
wherein x isi(t) represents the energy storage state of the ith SBS, qi(t) represents the charging power level of the ith SBS in the renewable energy source, pi(t) represents the transmission power of the ith SBS, ηiRepresents the energy transfer coefficient of the system from renewable energy sources to the i-th SBS, epsilon represents the system loss coefficient,
Figure BDA0002778391480000031
represents the initial energy storage state of the ith SBS;
the launch power of SBS is limited by the energy available in the energy source, limited by:
pi(t)≤xi(t)
the state of the MBS is a dynamic variable, which indicates that the MBS shares the available spectrum with the SBS as follows:
Figure BDA0002778391480000032
wherein x ism(t) denotes MBS status, um(t) represents a control variable, ηmDenotes xm(t) and um(t) spectral transfer coefficient between (t), epsilonmRepresenting system consumption systemThe number of the first and second groups is,
Figure BDA0002778391480000033
representing the MBS initial state;
control variable um(t) interference price constrained by lower limit value:
Figure BDA0002778391480000034
wherein the content of the first and second substances,
Figure BDA0002778391480000035
a lower limit value of the interference price unit price set for the MBS to the co-channel interference.
Further, the objective function of MBS is as follows:
Figure BDA0002778391480000036
Figure BDA0002778391480000037
wherein, γmIndicating possession of a particular number of spectra xm(t) cost per unit of spectrum of MBS, pi(t) represents the transmission power of the i-th SBS, um(t) interference price, α, charged by MBSm、βmAnd vmIs a weight constant, gimIndicating the channel gain of the ith SBS in the MBS,
Figure BDA0002778391480000038
lower limit value of interference price unit price representing MBS to co-channel interference setting-r(t-t0)Denotes the discount factor, xm(T) indicates the final spectrum state that the MBS wants to reach at the end of the time interval.
Further, the objective function of SBS is as follows:
Figure BDA0002778391480000039
Figure BDA00027783914800000310
wherein p isi(t) represents the transmission power of the ith SBS, γ (t) represents the energy cost coefficient of the first SBS, xi(t) represents the energy consumption of the ith SBS,
Figure BDA00027783914800000311
represents the interference payment of the ith SBS on the MBS, alphai，βiAnd viRepresents a weight constant, e-r(t-t0)Denotes the discount factor, xi(T) represents the terminal energy state that the i-th SBS wishes to reach at the end of the time interval.
Further, the dynamic game model is a Stackelberg dynamic game model;
solving Nash equilibrium solution under the open loop mode of the dynamic game model, comprising the following steps:
defining the unchanged state of the MBS and SBS credit standing strategy at the beginning of the game through a differential equation; the follower adjusts the transmission power according to the interference price, so that a target cost function is minimized, and an open-loop Nash equilibrium solution of the follower is obtained through solving; the leader makes an interference price according to the strategy of the follower, so that a target cost function is minimized, and an open-loop Nash equilibrium solution of the leader is obtained through solving.
Further, solving a nash equilibrium solution in a dynamic game model feedback mode includes:
defining dynamic change states of the leader and the follower after the game starts through a differential equation; the follower selects an optimal resource strategy within a limited time and solves to obtain a feedback Nash equilibrium solution of the follower; and the leader selects an optimal pricing strategy based on the strategy of the follower, and solves to obtain a feedback Nash equilibrium solution of the leader.
In another aspect, the present invention further provides a power control and interference pricing apparatus for a renewable energy heterogeneous network, including:
the system model building module is used for building a renewable energy heterogeneous network system model; the renewable energy heterogeneous network system model comprises a macro base station MBS and a plurality of small cell base stations SBS, wherein the MBS and the SBS use the same frequency spectrum resource, the MBS controls the interference price, and the SBS pays the interference cost to the MBS according to the interference price provided by the MBS and controls the transmitting power of the SBS according to the interference price provided by the MBS;
the state model building module is used for building state models of MBS and SBS in the renewable energy heterogeneous network;
the target function constructing module is used for constructing MBS and SBS cost target functions; the MBS target is used for searching an optimal interference pricing strategy, and the SBS target is used for controlling the transmitting power of the SBS target according to the interference price provided by the MBS so as to minimize the cost;
and the dynamic game model building and solving module is used for building a dynamic game model by taking the MBS as a leader and the SBS as a follower, and solving a Nash equilibrium solution of the dynamic game model in an open-loop and feedback mode to obtain an optimal power distribution and an optimal interference pricing strategy.
In yet another aspect, the present invention also provides an electronic device comprising a processor and a memory; wherein the memory has stored therein at least one instruction that is loaded and executed by the processor to implement the above-described method.
In yet another aspect, the present invention also provides a computer-readable storage medium having at least one instruction stored therein, the instruction being loaded and executed by a processor to implement the above method.
The technical scheme provided by the invention has the beneficial effects that at least:
the invention provides a model based on the principle of Stackelberg dynamic game based on the theoretical thought of game theory to effectively manage power resources in a heterogeneous network so as to reduce interference. The method comprises the steps of constructing a renewable energy heterogeneous network model of a leader and a plurality of followers, describing a power resource allocation game process based on a differential dynamic game, solving optimal power resource allocation and optimal interference pricing strategies of the leader and the followers through Nash equilibrium solution in an open loop and feedback mode, and realizing optimal power resource allocation optimization and effective management of power resources in the renewable energy heterogeneous network, thereby effectively reducing interference in the heterogeneous network.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flowchart of a power control and interference pricing method for a renewable energy heterogeneous network according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the MBS and SBS cost objective function construction process provided by the embodiment of the present invention;
FIG. 3 is a schematic diagram of a solution flow of Nash equilibrium solution in open-loop mode according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a solution flow of a nash equilibrium solution in a feedback mode according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
First embodiment
Aiming at the problem of cross-layer interference in the heterogeneous network, the embodiment provides a power control and interference pricing method for the renewable energy heterogeneous network, which can be used in the heterogeneous network to reduce interference in the heterogeneous network.
In general, one possible solution to the cross-layer interference problem in heterogeneous networks is to efficiently manage transmission resources between SBS and MBS. Power control is an important means of network transmission resource management. By carefully adjusting the transmit power of each SBS, interference between MBS and SBSs can be greatly reduced. In this regard, the present embodiment employs an interference pricing method for transmission power resource adjustment. Specifically, the MBS charges the SBS a fee based on interference caused by co-channel data transmission. Then, the interaction between MBS, which is the leader that decides the optimal interference price, and SBS, which is the follower that controls the transmission power, and SBS, is written as the Stackelberg differential game. Under the framework of the Stackelberg differential game, the requirements for interference pricing and power control are inseparable. The interference pricing is charged based on how much interference the SBS generates. Therefore, the SBS dynamically adjusts the transmit power to minimize cost.
In this embodiment, we propose to equip SBS with a renewable energy source. The dynamic variation of the energy affects the power control strategy in differential form. At the same time, dynamic changes in the spectrum are taken into account in the interference pricing strategy. On the basis, the dynamic characteristics of the energy and the spectrum of the objective function are described by using differential equations, and the differential equations are combined with the Stackelberg game to establish an optimization model based on the Stackelberg differential game.
Based on the above, the execution flow of the power control and interference pricing method for the renewable energy heterogeneous network of the embodiment is shown in fig. 1, and includes the following steps:
s101, constructing a renewable energy heterogeneous network system model;
specifically, the embodiment constructs a two-layer HetNets including one MBS and a plurality of SBSs. The top layer of the proposed HetNets is MBS, while the bottom layer includes SBSs. In the proposed HetNets, SBS is assumed to use the same spectrum resources as MBS. We assume that each User Equipment (UE) can access HetNets through MBS and SBS. Let N be {1, 2., N } denote a set of SBSs. The deployment of SBSs is considered sparse. Then, the co-layer interference between SBSs is negligible. However, since the SBSs and the MBS share the same spectrum resources, the cross interference cannot be eliminated. In order to capture cross-layer interference, the MBS controls the interference price and considers the interference payment between the SBSs and the MBS. And the SBSs pay the cross-layer interference cost to the MBS according to the interference price provided by the MBS. Interference prices have been broadcast from MBS to SBSs before SBSs make decisions on transmission power. The SBSs then pay the MBS the interference fee according to the broadcasted interference price.
It is assumed that HetNets are running in time. At time t, the transmission power of the i-th cell base station SBSi is pi(t) is represented by gim(t) is the channel gain of SBSi in MBS. u. ofm(t) represents the interference price offered by the MBS. U for unit interference pricem(t) is also a function of time. Known interference price um(t) and transmission power pi(t), the interference between SBSi and MBS pays:
Figure BDA0002778391480000061
wherein, IimAnd (t) paying for interference between the SBSi and the MBS.
In order to reduce the impact of cross-layer interference on the MBS, the SBSs need to control their transmit power according to the interference payment given in (1), where the unit interference price published by the MBS is an impact factor.
S102, constructing state models of MBS and SBS in the renewable energy heterogeneous network;
specifically, in the heterogeneous network system model constructed in this embodiment, the energy of the MBS is provided by a conventional power grid, and the energy source of the SBS is heterogeneous energy, including the conventional power grid and renewable energy, assuming that the energy supply of the MBS is stable. Driven by heterogeneous energy sources, SBS needs to harvest energy from renewable energy sources, the harvested energy should be stored in SBS, and traditional power grids are reserved guaranteed energy sources.
The charging power level of renewable energy sources, especially renewable energy sources, will have a significant impact on the energy storage capacity of SBSs. Meanwhile, the transmission of information may also reduce the energy storage capacity of the SBSs. Suppose the energy storage state of SBSi is recorded as xi(t), i ∈ {1,2, … N }. Charging power level q of SBSi in renewable energyi(t) represents. Based on charging,During the discharge process, the energy storage dynamics of SBSi can be characterized as:
Figure BDA0002778391480000071
wherein p isi(t) represents the transmission power of SBSi,. etaiRepresents the energy transfer coefficient of the system from renewable energy sources to SBSi, epsilon represents the system loss coefficient,
Figure BDA0002778391480000072
representing the initial energy storage state of the SBSi;
from the above formula, the energy storage state of SBSi is a function of time variable, and the energy storage state needs to change with time. The change of the energy storage state is influenced by a control variable of the information transmission power. In practical applications, the SBSi transmit power is limited by the available energy in the battery, limited by:
pi(t)≤xi(t) (3)
the state of the MBS is a dynamic variable, which indicates that the MBS shares the available spectrum with the SBS as follows:
Figure BDA0002778391480000073
wherein x ism(t) denotes MBS status, um(t) represents a control variable, ηmIs a state xm(t) and a control variable um(t) a constant spectral transfer coefficient; epsilonmThe system is programmed to determine the power consumption for the system, such as the consumption of control signaling,
Figure BDA0002778391480000074
representing the MBS initial state; control variable um(t) interference price constrained by lower limit value:
Figure BDA0002778391480000075
wherein the content of the first and second substances,
Figure BDA0002778391480000076
a lower limit of the interference price unit price set for the MBS to co-channel interference.
S103, constructing MBS and SBS cost objective functions; the MBS target is used for searching an optimal interference pricing strategy, and the SBS target is used for controlling the transmitting power of the SBS target according to the interference price provided by the MBS so as to minimize the cost;
it should be noted that, considering that power resources are limited, cost objective functions of the MBS and the SBS may be constructed separately in view of the system status to minimize the cost.
Specifically, as shown in fig. 2, constructing MBS and SBS cost objective functions includes:
s201, constructing an MBS cost objective function.
As a leader of the Stackelberg dynamic game, a cost objective function should be constructed for the MBS first. The goal of MBS is to find the optimal interference pricing strategy. In the target optimization process, an appropriate interference price is set, and the MBS can obtain interference payment from the SBSs due to cross-layer interference. The instantaneous targets of an MBS may be defined as:
Figure BDA0002778391480000081
wherein, γmTo possess a certain amount of spectrum xm(t) the cost per unit spectrum of MBS. The first part of the objective function is the spectral cost of having a certain amount of spectrum. Since the SBSs and the SBSs share the same spectrum resources for information transmission, in the proposed HetNets, there is interference payment between the SBSs and the MBS. Due to pi(t) is the transmission power of SBS i, um(t) interference price charged by MBS, we pay interference as the second part of the objective function. Since the interference price is assumed to be not less than a lower limit, we have a third part of the objective function given in (6). In (6), αm、βmAnd vmAre weight constant parameters that represent tradeoffs between energy states, interference costs, and unit price strategies.
The goal of MBS is to find an optimal interference pricing strategy to minimize its objective, as follows:
Figure BDA0002778391480000082
wherein e is-r(t-t0)To discount factor, xm(t) is the final spectrum state that the MBS wants to reach at the end of the time interval.
S202, constructing an SBS cost objective function.
For the follower SBSs, although higher transmission power can achieve better service quality, higher transmission power also results in higher interference cost for the MBS. The SBSs then need to control their transmit power according to the interference price provided by the MBS to minimize the cost. The instantaneous target of SBSi can be defined as follows:
Figure BDA0002778391480000083
wherein gamma (t) is the energy cost coefficient of SBSi, gammai(t)xi(t) represents the energy consumption of SBSi. The second part of the objective function is
Figure BDA0002778391480000084
Is the interference payment of SBSi to MBS. (3) The constraints given in (1) are also included in the objective function of each SBS. Wherein alpha isi，βiAnd viA weight constant parameter represents a trade-off between energy, interference cost and transmission power constraints.
The goal of SBSi is to find an optimal power control strategy that minimizes its cost function, as follows:
Figure BDA0002778391480000085
wherein e is-r(t-t0)Is a discount factor, xi(T) indicates that SBSi is expected to be reached at the end of the time intervalTo the terminal energy state.
And S104, constructing a dynamic game model by taking the MBS as a leader and the SBS as a follower, and solving a Nash equilibrium solution of the dynamic game model in an open loop and feedback mode to obtain optimal power distribution and optimal interference pricing.
Specifically, in this embodiment, the constructed dynamic game model is a Stackelberg dynamic game model; MBS acts as leader and uses interference price as control variable cost minimization problem (7). The SBSs dynamically control the transmission energy as a follower to minimize a given cost (9), and take the interference price provided by the MBS as a control variable. The main motivation for using differential gaming is that the state changes of MBS and SBSs can be described by differential equations, and MBS and SBSs need to make optimal decisions based on time-varying states. Under the framework of game, the cost of MBS and SBSs can be respectively and sequentially minimized: given the price of the interference, the SBSs can obtain their optimal transmit power. Through the strategy of the SBSs, the MBS can adjust the interference price so as to minimize the cost.
For MBS, the interference price should be controlled to minimize the target shown in (6), i.e. interference pricing, as follows:
Figure BDA0002778391480000091
wherein f ism(t,xm)＝ηmum(t)+εmxm(t)。
For SBS, it should control the transmission power to minimize the target given in (8), i.e. power control, as follows:
Figure BDA0002778391480000092
wherein f isi(t,xi)＝ηiqi(t)-pi(t)+εxi(t)。
(10) The constraints in (11) and (9) are dynamic changes in the status of MBS and SBSs. Based on the Stackelberg differential game framework, the MBS shall inform the SBSs of the interference price at the beginning of the game, and then control the transmission power by using the interference price as an input variable, so as to minimize the transmission cost. And finally, the MBS readjusts the interference price according to the power control strategy of the SBSs. Strategy control is carried out in an open loop mode and a feedback mode respectively, an attacker and a defender also have an open loop mode Nash equilibrium solution and a feedback mode Nash equilibrium solution respectively, namely, the attacker and the defender have optimal resource allocation values in the open loop mode and the feedback mode respectively.
Based on the above, the present embodiment defines the unchanged state of the established policies of the MBS and SBS gatekeeper at the beginning of the game through the differential equation; the follower adjusts the transmission power according to the interference price, so that a target cost function is minimized, and an open-loop Nash equilibrium solution of the follower is obtained through solving; the leader makes an interference price according to the strategy of the follower, so that a target cost function is minimized, and an open-loop Nash equilibrium solution of the leader is obtained through solving.
Defining dynamic change states of the leader and the follower after the game starts through a differential equation; the follower selects an optimal resource strategy within a limited time and solves to obtain a feedback Nash equilibrium solution of the follower; and the leader selects an optimal pricing strategy based on the strategy of the follower, and solves to obtain a feedback Nash equilibrium solution of the leader.
Specifically, as shown in fig. 3, the process of solving the open-loop nash equilibrium solution in this embodiment includes:
s301, defining an expression meeting the optimal resource strategy.
Power control strategy for follower SBSi (i ∈ N)
Figure BDA0002778391480000093
If the following inequality is satisfied,
Figure BDA0002778391480000094
then the corresponding optimum state is set for the most appropriate state,
Figure BDA0002778391480000101
for leader MBS, if interference price strategy
Figure BDA0002778391480000102
The following inequality is satisfied,
Figure BDA0002778391480000103
then the corresponding optimum state is set for the most appropriate state,
Figure BDA0002778391480000104
s302, defining characteristics describing the open-loop Nash equilibrium solution.
For follower SBS i, if power control strategy
Figure BDA0002778391480000105
Is a Nash equilibrium solution for cost minimized gaming in open loop mode if the upper state function Lambda existsi(t) for i ∈ N, there are
Figure BDA0002778391480000106
Figure BDA0002778391480000107
Figure BDA0002778391480000108
Wherein, the final value of the upper state function is constrained by the final value of the cost function, and can be expressed as
Figure BDA0002778391480000109
For leader, if interference pricing strategy
Figure BDA00027783914800001010
Is in open loop modeNash equilibrium solution for minimizing game if upper state function lambda existsm(t) and lambdai(t) then
Figure BDA00027783914800001011
Figure BDA00027783914800001012
Figure BDA00027783914800001013
Figure BDA00027783914800001014
S303, obtaining the open-loop Nash equilibrium solution.
For the follower, there is a unique open-loop nash equalization for each SBS for the power control problem, as follows:
Figure BDA00027783914800001015
wherein, Λi(t) is given by the following equation:
Figure BDA00027783914800001016
Figure BDA00027783914800001017
partial derivatives are taken from (14) to obtain nash equilibrium solutions in open loop mode for each SBS given in (21) - (23).
Will optimize the transmission power
Figure BDA00027783914800001018
Substituting (22) we can get a solution to the upper state function in x form, the boundary conditions are given by (23). Meanwhile, the dynamic change of the energy storage state xi (t) can also be expressed by a co-modal function. Solved differential equations (21) - (23), we can obtain solution xi(t)、Λi(t) and Nash equalization solutions applicable to all SBSs power control problems.
Performing a specified minimization to (17) yield for the leader,
Figure BDA0002778391480000111
wherein the content of the first and second substances,
Figure BDA0002778391480000112
is the SBSs strategy obtained. It can be seen that in open loop mode, the optimal interference price is mainly influenced by the transmit power strategy of the spread spectrum system. (24) Middle, upper state function lambdam(t) is given by the following formula:
Figure BDA0002778391480000113
Figure BDA0002778391480000114
as shown in fig. 4, the process of solving the nash equilibrium solution in the feedback mode in this embodiment includes:
s401, solving the non-cooperative power control solution of the SBSs, namely the followers, in the feedback mode.
In particular, for SBSi, if the power control strategy is
Figure BDA0002778391480000115
Is a non-cooperative Nash equilibrium solution of an infinite-level cost minimization game in a feedback mode if a continuous differential value function V existsi(x) For i ∈ N, the following Isaacs-Bellman equation is satisfied:
Figure BDA0002778391480000116
since the proposed game is played over an infinitely long time range using a time relaxation mechanism, the optimal transmission power strategy needs to be a function of the state. The optimal transmit power strategy is as follows:
for the power control problem, there is a unique non-cooperative feedback equalization for each SBS, as follows:
Figure BDA0002778391480000117
to obtain the non-cooperative feedback nash equalization of each SBS, the partial derivative of the function in (27) is first calculated. The partial derivatives of the relationship function are as follows:
Figure BDA0002778391480000118
wherein the content of the first and second substances,
Figure BDA0002778391480000119
according to (29), we have:
Figure BDA00027783914800001110
bringing (30) back to (29) to obtain a non-cooperative nash equalization solution of the power control problem SBSi in feedback mode:
Figure BDA00027783914800001111
based on the above function, we can obtain the expression of the non-cooperative Nash equilibrium solution of the SBSs in the feedback mode.
And S402, solving the cooperative power control solution of the SBSs, namely the followers, in the feedback mode.
In particular, to achieve cooperative nash equilibrium, the total cost can be minimized as follows:
Figure BDA0002778391480000121
(32) the size of the total cost minimization problem to be performed is N, i.e. all SBSs participate in the proposed cooperative countermeasure. The total cost minimization problem exists with the Bellman value function W (N, t, x) satisfying the following equation:
Figure BDA0002778391480000122
by minimizing the one shown in (33), we can get a cooperative nash equalization of the power control problem in feedback mode:
Figure BDA0002778391480000123
by substituting the above equation into (33), we can get:
Figure BDA0002778391480000124
the cooperative nash equalization of the power control problem in feedback mode can be expressed as:
Figure BDA0002778391480000125
and S403, solving a feedback Nash equilibrium solution of the MBS, namely the leader.
For the leader, if the pricing strategy is disturbed
Figure BDA0002778391480000126
Is a Nash equilibrium solution of infinite level cost minimization game in a feedback mode if a continuous differential value function V existsm(t) satisfies the following for i (i ∈ N)The Isaacs-Bellman equation,
Figure BDA0002778391480000127
based on the Bellman dynamic programming technology, a feedback Nash equilibrium solution can be obtained. Through the minimization in (37), we can obtain the optimal interference price solution of the MBS in the feedback mode:
Figure BDA0002778391480000128
through the calculation, Nash equilibrium solutions of an open loop mode and a feedback mode in a Stackelberg dynamic game model can be obtained, so that the optimal distribution of the renewable energy heterogeneous network resource under the limitation is realized.
Second embodiment
The embodiment provides a power control and interference pricing device of a renewable energy heterogeneous network, which comprises the following modules:
the system model building module is used for building a renewable energy heterogeneous network system model; the renewable energy heterogeneous network system model comprises a macro base station MBS and a plurality of small cell base stations SBS, wherein the MBS and the SBS use the same frequency spectrum resource, the MBS controls the interference price, and the SBS pays the interference cost to the MBS according to the interference price provided by the MBS and controls the transmitting power of the SBS according to the interference price provided by the MBS;
the state model building module is used for building state models of MBS and SBS in the renewable energy heterogeneous network;
the target function constructing module is used for constructing MBS and SBS cost target functions; the MBS target is used for searching an optimal interference pricing strategy, and the SBS target is used for controlling the transmitting power of the SBS target according to the interference price provided by the MBS so as to minimize the cost;
and the dynamic game model building and solving module is used for building a dynamic game model by taking the MBS as a leader and the SBS as a follower, and solving a Nash equilibrium solution of the dynamic game model in an open-loop and feedback mode to obtain an optimal power distribution and an optimal interference pricing strategy.
The power control and interference pricing device of the renewable energy heterogeneous network of the present embodiment corresponds to the power control and interference pricing method of the renewable energy heterogeneous network of the first embodiment described above; the functions realized by the functional modules in the power control and interference pricing device of the renewable energy heterogeneous network of the embodiment correspond to the flow steps in the method of the first embodiment one to one; therefore, it is not described herein.
Third embodiment
The present embodiment provides an electronic device, which includes a processor and a memory; wherein the memory has stored therein at least one instruction that is loaded and executed by the processor to implement the method of the first embodiment.
The electronic device may have a relatively large difference due to different configurations or performances, and may include one or more processors (CPUs) and one or more memories, where at least one instruction is stored in the memory, and the instruction is loaded by the processor and executes the method.
Fourth embodiment
The present embodiments provide a computer-readable storage medium having stored therein at least one instruction, which is loaded and executed by a processor, to implement the method of the first embodiment. The computer readable storage medium may be, among others, ROM, random access memory, CD-ROM, magnetic tape, floppy disk, optical data storage device, and the like. The instructions stored therein may be loaded by a processor in the terminal and perform the above-described method.
Furthermore, it should be noted that the present invention may be provided as a method, apparatus or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media having computer-usable program code embodied in the medium.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should also be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
Finally, it should be noted that while the above describes a preferred embodiment of the invention, it will be appreciated by those skilled in the art that, once the basic inventive concepts have been learned, numerous changes and modifications may be made without departing from the principles of the invention, which shall be deemed to be within the scope of the invention. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.

Claims (10)

1. A power control and interference pricing method for a renewable energy heterogeneous network is characterized by comprising the following steps:
constructing a renewable energy heterogeneous network system model; the renewable energy heterogeneous network system model comprises a macro base station MBS and a plurality of small cell base stations SBS, wherein the MBS and the SBS use the same frequency spectrum resource, the MBS controls the interference price, and the SBS pays the interference cost to the MBS according to the interference price provided by the MBS and controls the transmitting power of the SBS according to the interference price provided by the MBS;
constructing state models of MBS and SBS in the renewable energy heterogeneous network;
constructing MBS and SBS cost objective functions; the MBS target is used for searching an optimal interference pricing strategy, and the SBS target is used for controlling the transmitting power of the SBS target according to the interference price provided by the MBS so as to minimize the cost;
and constructing a dynamic game model by taking the MBS as a leader and the SBS as a follower, and solving Nash equilibrium solutions of the dynamic game model in open-loop and feedback modes to obtain an optimal power distribution and optimal interference pricing strategy.
2. The method for power control and interference pricing of renewable energy heterogeneous network according to claim 1, wherein the renewable energy heterogeneous network system model is a two-layer heterogeneous network system, the top layer is MBS, the bottom layer comprises SBS, and co-layer interference between SBS is negligible.
3. The method for power control and interference pricing for renewable energy heterogeneous networks of claim 1, wherein the interference between SBS and MBS pays for:
Figure FDA0002778391470000011
wherein, Iim(t) payment for interference between the ith SBS and MBS um(t) interference price for MBS, gim(t) channel gain in MBS of i-th SBS, pi(t) is the transmission power of the ith SBS.
4. The method for power control and interference pricing of renewable energy heterogeneous network according to claim 1, wherein in the renewable energy heterogeneous network system model, the energy of MBS is provided by a conventional power grid, the energy source of SBS is heterogeneous energy, including the conventional power grid and the renewable energy, SBS collects energy from the renewable energy under the driving of heterogeneous energy, and the collected energy is stored in SBS.
5. The method for power control and interference pricing for renewable energy heterogeneous networks of claim 4, wherein in the state model, the energy storage dynamics of SBS is characterized by:
Figure FDA0002778391470000012
wherein x isi(t) represents the energy storage form of the i-th SBSState qi(t) represents the charging power level of the ith SBS in the renewable energy source, pi(t) represents the transmission power of the ith SBS, ηiRepresents the energy transfer coefficient of the system from renewable energy sources to the i-th SBS, epsilon represents the system loss coefficient,
Figure FDA0002778391470000021
represents the initial energy storage state of the ith SBS;
the launch power of SBS is limited by the energy available in the energy source, limited by:
pi(t)≤xi(t)
the state of the MBS is a dynamic variable, which indicates that the MBS shares the available spectrum with the SBS as follows:
Figure FDA0002778391470000022
wherein x ism(t) denotes MBS status, um(t) represents a control variable, ηmDenotes xm(t) and um(t) spectral transfer coefficient between (t), epsilonmWhich represents the consumption coefficient of the system,
Figure FDA0002778391470000023
representing the MBS initial state;
control variable um(t) interference price constrained by lower limit value:
Figure FDA0002778391470000024
wherein the content of the first and second substances,
Figure FDA0002778391470000025
a lower limit value of the interference price unit price set for the MBS to the co-channel interference.
6. The method for power control and interference pricing for renewable energy heterogeneous networks of claim 5, wherein the objective function of MBS is as follows:
Figure FDA0002778391470000026
Figure FDA0002778391470000027
wherein, γmIndicating possession of a particular number of spectra xm(t) cost per unit of spectrum of MBS, pi(t) represents the transmission power of the i-th SBS, um(t) interference price, α, charged by MBSm、βmAnd vmIs a weight constant, gimIndicating the channel gain of the ith SBS in the MBS,
Figure FDA0002778391470000028
lower limit value of interference price unit price representing MBS to co-channel interference setting-r(t-t0)Denotes the discount factor, xm(T) indicates the final spectrum state that the MBS wants to reach at the end of the time interval.
7. The method for power control and interference pricing for renewable energy heterogeneous networks of claim 6, wherein the objective function of SBS is as follows:
Figure FDA0002778391470000029
Figure FDA00027783914700000210
wherein p isi(t) represents the transmission power of the ith SBS, γ (t) represents the energy cost coefficient of the first SBS, xi(t) represents the energy consumption of the ith SBS,
Figure FDA00027783914700000211
represents the interference payment of the ith SBS on the MBS, alphai，βiAnd viRepresents a weight constant, e-r(t-t0)Denotes the discount factor, xi(T) represents the terminal energy state that the i-th SBS wishes to reach at the end of the time interval.
8. The power control and interference pricing method for the renewable energy heterogeneous network of claim 1, wherein the dynamic game model is a Stackelberg dynamic game model;
solving Nash equilibrium solution under the open loop mode of the dynamic game model, comprising the following steps:
defining the unchanged state of the MBS and SBS credit standing strategy at the beginning of the game through a differential equation; the follower adjusts the transmission power according to the interference price, so that a target cost function is minimized, and an open-loop Nash equilibrium solution of the follower is obtained through solving; the leader makes an interference price according to the strategy of the follower, so that a target cost function is minimized, and an open-loop Nash equilibrium solution of the leader is obtained through solving.
9. The method for power control and interference pricing of the renewable energy heterogeneous network as claimed in claim 8, wherein solving nash equilibrium solution in a dynamic game model feedback mode comprises:
defining dynamic change states of the leader and the follower after the game starts through a differential equation; the follower selects an optimal resource strategy within a limited time and solves to obtain a feedback Nash equilibrium solution of the follower; and the leader selects an optimal pricing strategy based on the strategy of the follower, and solves to obtain a feedback Nash equilibrium solution of the leader.
10. A power control and interference pricing apparatus for a renewable energy heterogeneous network, the power control and interference pricing apparatus comprising:
the system model building module is used for building a renewable energy heterogeneous network system model; the renewable energy heterogeneous network system model comprises a macro base station MBS and a plurality of small cell base stations SBS, wherein the MBS and the SBS use the same frequency spectrum resource, the MBS controls the interference price, and the SBS pays the interference cost to the MBS according to the interference price provided by the MBS and controls the transmitting power of the SBS according to the interference price provided by the MBS;
the state model building module is used for building state models of MBS and SBS in the renewable energy heterogeneous network;
the target function constructing module is used for constructing MBS and SBS cost target functions; the MBS target is used for searching an optimal interference pricing strategy, and the SBS target is used for controlling the transmitting power of the SBS target according to the interference price provided by the MBS so as to minimize the cost;
and the dynamic game model building and solving module is used for building a dynamic game model by taking the MBS as a leader and the SBS as a follower, and solving a Nash equilibrium solution of the dynamic game model in an open-loop and feedback mode to obtain an optimal power distribution and an optimal interference pricing strategy.
CN202011273448.2A 2020-11-13 2020-11-13 Power control and interference pricing method and device for renewable energy heterogeneous network Active CN112533275B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011273448.2A CN112533275B (en) 2020-11-13 2020-11-13 Power control and interference pricing method and device for renewable energy heterogeneous network

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011273448.2A CN112533275B (en) 2020-11-13 2020-11-13 Power control and interference pricing method and device for renewable energy heterogeneous network

Publications (2)

Publication Number Publication Date
CN112533275A true CN112533275A (en) 2021-03-19
CN112533275B CN112533275B (en) 2022-01-25

Family

ID=74980767

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011273448.2A Active CN112533275B (en) 2020-11-13 2020-11-13 Power control and interference pricing method and device for renewable energy heterogeneous network

Country Status (1)

Country Link
CN (1) CN112533275B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114362792A (en) * 2022-01-23 2022-04-15 燕山大学 Relay power distribution and pricing method considering communication safety in smart grid

Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050058151A1 (en) * 2003-06-30 2005-03-17 Chihsiang Yeh Method of interference management for interference/collision avoidance and spatial reuse enhancement
CN103856947A (en) * 2014-02-07 2014-06-11 西安电子科技大学 Channel selection-power control combined interference coordination method
CN104105193A (en) * 2014-07-01 2014-10-15 西安交通大学 Power distribution method in heterogeneous network based on Starckelberg game
US20160021621A1 (en) * 2012-03-13 2016-01-21 Zte (Usa) Inc. Interference management in the heterogeneous network
CN105848274A (en) * 2016-03-25 2016-08-10 山东大学 Non-uniform pricing power control method based on Steinberg game theory in bi-layer heterogeneous network
CN106937295A (en) * 2017-02-22 2017-07-07 沈阳航空航天大学 Heterogeneous network high energy efficiency power distribution method based on game theory
CN107135508A (en) * 2017-02-28 2017-09-05 南京邮电大学 A kind of small base station interference management method of energy acquisition based on Game with Coalitions
CN107864512A (en) * 2017-11-01 2018-03-30 南京邮电大学 A kind of honeycomb heterogeneous network resource allocation methods based on game theory
CN107911860A (en) * 2017-11-01 2018-04-13 重庆邮电大学 Power distribution method in NOMA systems based on price and with rate fairness ratio
CN108990067A (en) * 2018-07-09 2018-12-11 常熟理工学院 A kind of energy efficiency controlling method applied to super-intensive heterogeneous network
CN109618351A (en) * 2019-01-09 2019-04-12 南京邮电大学 Resource allocation methods in heterogeneous network based on stackelberg game
CN109639729A (en) * 2019-01-16 2019-04-16 北京科技大学 A kind of dynamic game method and device of internet of things oriented intimidation defense resource allocation

Patent Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050058151A1 (en) * 2003-06-30 2005-03-17 Chihsiang Yeh Method of interference management for interference/collision avoidance and spatial reuse enhancement
US20160021621A1 (en) * 2012-03-13 2016-01-21 Zte (Usa) Inc. Interference management in the heterogeneous network
CN103856947A (en) * 2014-02-07 2014-06-11 西安电子科技大学 Channel selection-power control combined interference coordination method
CN104105193A (en) * 2014-07-01 2014-10-15 西安交通大学 Power distribution method in heterogeneous network based on Starckelberg game
CN105848274A (en) * 2016-03-25 2016-08-10 山东大学 Non-uniform pricing power control method based on Steinberg game theory in bi-layer heterogeneous network
CN106937295A (en) * 2017-02-22 2017-07-07 沈阳航空航天大学 Heterogeneous network high energy efficiency power distribution method based on game theory
CN107135508A (en) * 2017-02-28 2017-09-05 南京邮电大学 A kind of small base station interference management method of energy acquisition based on Game with Coalitions
CN107864512A (en) * 2017-11-01 2018-03-30 南京邮电大学 A kind of honeycomb heterogeneous network resource allocation methods based on game theory
CN107911860A (en) * 2017-11-01 2018-04-13 重庆邮电大学 Power distribution method in NOMA systems based on price and with rate fairness ratio
CN108990067A (en) * 2018-07-09 2018-12-11 常熟理工学院 A kind of energy efficiency controlling method applied to super-intensive heterogeneous network
CN109618351A (en) * 2019-01-09 2019-04-12 南京邮电大学 Resource allocation methods in heterogeneous network based on stackelberg game
CN109639729A (en) * 2019-01-16 2019-04-16 北京科技大学 A kind of dynamic game method and device of internet of things oriented intimidation defense resource allocation

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
HAITAO XU; LIJING ZHANG; XIANWEI ZHOU; ZHU HAN: "Stackelberg Differential Game based Power Control in Small Cell Networks Powered by Renewable Energy", 《IEEE》 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114362792A (en) * 2022-01-23 2022-04-15 燕山大学 Relay power distribution and pricing method considering communication safety in smart grid

Also Published As

Publication number Publication date
CN112533275B (en) 2022-01-25

Similar Documents

Publication Publication Date Title
Piovesan et al. Energy sustainable paradigms and methods for future mobile networks: A survey
Bu et al. Green cognitive mobile networks with small cells for multimedia communications in the smart grid environment
CN114004476B (en) Multi-time scale optimal scheduling method for comprehensive energy system
Wei et al. On optimally reducing power loss in micro-grids with power storage devices
CN108322938B (en) Power distribution method based on double-layer non-cooperative game theory under ultra-dense networking and modeling method thereof
Yang et al. Distributed deep reinforcement learning-based spectrum and power allocation for heterogeneous networks
Lim et al. Optimal allocation of energy storage and solar photovoltaic systems with residential demand scheduling
Liu et al. Joint computation offloading and demand response management in mobile edge network with renewable energy sources
CN109982434B (en) Wireless resource scheduling integrated intelligent control system and method and wireless communication system
Zhong et al. Stable user association and resource allocation based on stackelberg game in backhaul-constrained HetNets
CN112533275B (en) Power control and interference pricing method and device for renewable energy heterogeneous network
CN111682536B (en) Random-robust optimization operation method for virtual power plant participating in dual market before day
CN112512065A (en) Mobility aware offloading and migration algorithms in small cell networks supporting MEC
CN107249190A (en) Cell micro-base station spectrum auction method based on neutral net and temporal evolution
Li et al. Decentralized renewable energy pricing and allocation for millimeter wave cellular backhaul
Diamantoulakis et al. Game theoretic approach to demand side management in smart grid with user-dependent acceptance prices
CN113747450A (en) Service deployment method and device in mobile network and electronic equipment
Vamvakas et al. A user-centric economic-driven paradigm for rate allocation in non-orthogonal multiple access wireless systems
Pourkabirian et al. Dynamic resource allocation for OFDMA femtocell networks: a game-theoretic approach
CN116579455A (en) Optimized bidding method of virtual power plant in multi-element power market
CN110049436A (en) Distributed channel distribution and shared method and system based on isomery frequency spectrum
CN114006399B (en) Optimized scheduling method for participating in power distribution network demand response of large-scale 5G base station
Reyhanian et al. A matching-game-based energy trading for small cell networks with energy harvesting
Xu et al. Stackelberg differential game based power control in small cell networks powered by renewable energy
Reyhanian et al. Double-auction-based energy trading for small cell networks with energy harvesting

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant