CN117135641B - Resource allocation method and device of RIS-based power fusion communication network - Google Patents

Resource allocation method and device of RIS-based power fusion communication network Download PDF

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CN117135641B
CN117135641B CN202311397933.4A CN202311397933A CN117135641B CN 117135641 B CN117135641 B CN 117135641B CN 202311397933 A CN202311397933 A CN 202311397933A CN 117135641 B CN117135641 B CN 117135641B
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model
ris
base station
power
determining
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CN117135641A (en
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金燊
邢宁哲
申昉
韩旭东
赵阳
冯禹清
纪雨彤
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State Grid Corp of China SGCC
State Grid Jibei Electric Power Co Ltd
Information and Telecommunication Branch of State Grid Jibei Electric Power Co Ltd
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State Grid Corp of China SGCC
State Grid Jibei Electric Power Co Ltd
Information and Telecommunication Branch of State Grid Jibei Electric Power Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/22—Traffic simulation tools or models
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/06—Testing, supervising or monitoring using simulated traffic
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/53—Allocation or scheduling criteria for wireless resources based on regulatory allocation policies
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention relates to the field of power communication, and provides a resource allocation method and a resource allocation device of an RIS-based power fusion communication network, wherein the method comprises the steps of constructing a multi-user MISO system model of the RIS-based power fusion communication network; determining an original optimization problem model based on the data transmission rate model and the power consumption model, wherein the objective of the original optimization problem model comprises maximizing the energy efficiency of the multi-user MISO system model; determining a first sub-problem model and a second sub-problem model based on the original optimization problem model; alternately iterating the first sub-problem model and the second sub-problem model to determine an optimal solution; and performing resource allocation on the RIS-based power fusion communication network based on the multi-user MISO system model for obtaining the optimal solution. Therefore, the problems of low access speed and unreasonable resource allocation of the mass terminals of the power fusion communication network in the related technology are solved, and the rapid access and the reduction of the energy consumption of the mass terminals of the power fusion communication network are realized.

Description

Resource allocation method and device of RIS-based power fusion communication network
Technical Field
The invention relates to the technical field of power communication, in particular to a resource allocation method and device of a power fusion communication network based on RIS.
Background
During the last decades, traditional power grid construction has achieved excellent results, but the development pace is still not informative and the communication technology is fast. With the continued penetration of the electricity market, consumers put higher demands on the electricity supply, while the increasingly aged power grids have not kept pace with technological changes. How to thoroughly reform the power grid by utilizing the daily and monthly information technology so as to build a power system with higher efficiency, low investment, safety, reliability and flexible strain becomes a trend.
The information technology is fused in the power system, so that the power system is more stable and reliable, the power system is more flexible and adaptive, the intelligent power grid fused with the information technology is a development trend of a future power grid, and a fusion network fused with the information technology to the intelligent power grid is a foundation of development of the intelligent power grid.
Under the age background of the 5G/6G communication technology, if the smart grid needs to be better developed, the two are organically combined, so that the application of the 5G/6G communication technology in the smart grid is promoted. In this context, reconfigurable smart reflective surfaces (Reconfigurable Intelligent Surface, RIS) are a favored new technology that enables the configuration of wireless propagation environments. It can be used to adjust the reflection coefficient of the reflecting element by a programmable controller, so that the reflected signal can be transmitted to the expected receiver in a desired manner, and the wireless environment can be controlled and programmable. RIS is a more energy efficient and economical technology.
At present, RIS technology is widely researched and focused in the communication field, research literature on RIS is also endless, and a great deal of research on RIS is performed in the fields of theoretical research, application scenes and the like. However, in the related art, there is little research on a method for accessing and allocating resources to a mass terminal of a power convergence communication network, and research on optimizing RIS power is not involved, and under the condition of an RIS auxiliary system, energy consumption in an RIS control circuit is not negligible.
There is a need in the art for a resource allocation method for access conditions of a large number of terminals in a power convergence communication network.
Disclosure of Invention
The invention provides a resource allocation method and a resource allocation device of an electric power fusion communication network based on RIS, which are used for solving the problems of low access speed and unreasonable resource allocation of mass terminals of the electric power fusion communication network in the prior art and realizing rapid intervention and energy consumption reduction of the mass terminals of the electric power fusion communication network.
The invention provides a resource allocation method of an electric power fusion communication network based on RIS, which comprises the following steps:
constructing a multi-user MISO system model oriented to an RIS-based power fusion communication network; the multi-user MISO system model comprises a base station, and power distribution terminals and RISs which are in one-to-one correspondence with multiple users;
determining a data transmission rate model and a power consumption model of the multi-user MISO system model;
determining an original optimization problem model based on the data transmission rate model and the power consumption model, wherein the objective of the original optimization problem model comprises maximizing the energy efficiency of the multi-user MISO system model; variables of the original optimization problem model include beamforming of the base station and a phase angle of the RIS;
determining a first sub-problem model and a second sub-problem model based on the original optimization problem model; the first sub-problem model includes fixing a phase angle of the RIS, optimizing beamforming of the base station; the second sub-problem model includes: fixing the beam forming of the base station and optimizing the phase angle of the RIS;
alternately iterating the first sub-problem model and the second sub-problem model to determine an optimal solution;
and performing resource allocation on the RIS-based power fusion communication network based on the multi-user MISO system model for obtaining the optimal solution.
According to the resource allocation method of the RIS-based power fusion communication network provided by the invention, the determining the data transmission rate model of the multi-user MISO system model comprises the following steps:
determining the data transmission rate of the base station for each user based on the signal-to-interference-and-noise ratio of each power distribution terminal;
the sum rate of the base station for all users is determined based on the data transmission rate of the base station for each user and the number of users.
According to the resource allocation method of the RIS-based power fusion communication network provided by the invention, the determining of the power consumption model of the multi-user MISO system model comprises the following steps:
and determining the total power consumption of the multi-user MISO system model based on the sum of the transmission power consumption of the base station, the self-fixed power consumption of the base station, the circuit power consumption of the power distribution terminal and the circuit power consumption of the RIS.
According to the resource allocation method of the RIS-based power fusion communication network provided by the invention, the original optimization problem model is determined based on the data transmission rate model and the power consumption model, and the method comprises the following steps:
determining the limiting conditions of the original optimization problem model;
and obtaining the energy efficiency of the multi-user MISO system model based on the ratio of the sum rate to the total power consumption and maximizing the energy efficiency.
According to the resource allocation method of the RIS-based power fusion communication network provided by the invention, the determining of the limiting condition of the original optimization problem model comprises the following steps:
determining that the data transmission rate of the base station for the current user is greater than or equal to the minimum data transmission rate required by the current user;
determining that the transmitting power of the base station is less than or equal to the maximum transmitting power;
determining that the phase angle of the RIS is between 0 and 2Between them.
According to the resource allocation method of the RIS-based power fusion communication network provided by the invention, the optimization of the beamforming of the base station comprises the following steps:
on the premise of setting the base station to adopt maximum ratio transmission precoding, determining target beam forming of the base station for the current user; the target beam forming is related to target transmitting power of the base station aiming at the signal of the current user;
and optimizing the target transmitting power based on the target beam forming.
According to the resource allocation method of the power fusion communication network based on the RIS, which is provided by the invention, the phase angle of the RIS is optimized, and the method comprises the following steps:
determining the channel gain corresponding to the multi-user MISO system model as an fitness function;
and solving the phase angle of the RIS by using a modified sine and cosine algorithm based on the fitness function.
According to the resource allocation method of the RIS-based power fusion communication network provided by the invention, the improved sine and cosine algorithm is utilized to solve the phase angle of the RIS, and the method comprises the following steps:
randomly generating I particles, wherein each particle meets unit mode constraint;
calculating the fitness function of each particle;
selecting the first I/2 particles for iterative updating;
performing iterative updating on the I/2 particles after selection;
and when the fitness function converges, obtaining a local optimal solution of the phase angle.
According to the resource allocation method of the RIS-based power fusion communication network provided by the invention, the calculating of the fitness function of each particle comprises the following steps:
obtaining a fitness function of each particle according to a first formula, wherein the first formula is as follows:
；
the iterative updating of the I/2 particles before selection comprises the following steps:
the first I/2 particles are iteratively updated according to a second formula; the second formula is:
；
the I/2 particles after selection are subjected to iterative updating, which comprises the following steps:
the latter I/2 particles are iteratively updated according to a third formula; the third formula is:
，
wherein,to the first of the RISkChannel vectors for individual users;
to the base stationkChannel vectors for individual users;
，/>is the phase angle;
a channel matrix for the base station to the RIS;
position information at the t iteration for the j-th dimension of the i-th particle;
the global optimal value of the j-th dimensional position information of the ions after t iterations;
is a step size factor, obeys the Lewy distribution, and is initialized according to a fourth formula, wherein the fourth formula is as follows:
；
for controlling parameters, the parameters are adaptively adjusted through a fifth formula, wherein the fifth formula is as follows:
；
，/>，/>for 3 random numbers obeying a uniform distribution, +.>；
T is the maximum iteration number;
a is a constant, and is generally 2;
is->The weight of the variable at the t-th iteration;
is a scaling factor;
is a standard gamma function.
The invention also provides a resource allocation device of the RIS-based power fusion communication network, which comprises:
the construction module is used for constructing a multi-user MISO system model oriented to the RIS-based power fusion communication network; the multi-user MISO system model comprises a base station, a power distribution terminal corresponding to multiple users one by one and a reconfigurable intelligent reflection surface RIS;
a first determining module, configured to determine a data transmission rate model and a power consumption model of the multi-user MISO system model;
a second determining module configured to determine an original optimization problem model based on the data transmission rate model and the power consumption model, the objective of the original optimization problem model including maximizing energy efficiency of the multi-user MISO system model; variables of the original optimization problem model include beamforming of the base station and a phase angle of the RIS;
a third determining module, configured to determine a first sub-problem model and a second sub-problem model based on the original optimization problem model; the first sub-problem model includes fixing a phase angle of the RIS, optimizing beamforming of the base station; the second sub-problem model includes: fixing the beam forming of the base station and optimizing the phase angle of the RIS;
the iteration module is used for alternately iterating the first sub-problem model and the second sub-problem model to determine an optimal solution;
and the allocation module is used for allocating resources to the RIS-based power fusion communication network based on the multi-user MISO system model for obtaining the optimal solution.
The invention provides a resource allocation method and a resource allocation device for an RIS-based power fusion communication network, which are characterized in that a multi-user MISO system model for the RIS-based power fusion communication network is constructed, a data transmission rate model and a power consumption model are determined in the multi-user MISO system model, an original optimization problem model is determined based on the data transmission rate model and the power consumption model, in order to better solve an original optimization problem, the original optimization problem is divided into a first sub-problem model and a second sub-problem model, then the first sub-problem and the second sub-problem are alternately and iteratively solved, an optimal solution is determined, and finally, resource allocation is carried out on the RIS-based power fusion communication network based on the multi-user MISO system model with the optimal solution. Therefore, by designing the resource allocation method with energy efficiency priority, effective information transmission between the control center and the power distribution terminal is realized, the quick control instruction issuing of time delay constraint can be met, the mass terminal access capability of the power fusion communication network is further improved, and the energy consumption is reduced.
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In order to more clearly illustrate the invention or the technical solutions of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for resource allocation for RIS-based power fusion communication networks according to one embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a multi-user MISO system model for RIS-based power fusion communication network according to one embodiment of the present invention;
FIG. 3 is a schematic diagram showing the variation trend of normalized channel gain values with iteration number according to different schemes provided by an embodiment of the present invention;
FIG. 4 is a graph showing the trend of average energy efficiency with maximum transmit power for different schemes provided by an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a resource allocation device of a RIS-based power fusion communication network according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, 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 resource allocation method and apparatus of the RIS-based power fusion communication network of the present invention are described below with reference to fig. 1 to 5.
As shown in fig. 1, an embodiment of the present invention provides a resource allocation method for an RIS-based power fusion communication network, which may include:
step 110: constructing a multi-user MISO system model oriented to an RIS-based power fusion communication network; as shown in fig. 2, the multi-user MISO system model may include base stations, power distribution terminals in one-to-one correspondence with multiple users, RIS, and RIS circuit controllers.
The traditional power grid is structured as a centralized system, intelligent equipment is only centralized in a central control area and part of transformer substations, and remote terminal equipment can hardly realize the intellectualization, so that the traditional power grid has the problems of low power transmission efficiency and the like. In order to solve the problem, the prior art combines a power system and an information communication technology to form a novel power grid, namely a smart power grid, and the smart power grid can simultaneously provide power flow and bidirectional information flow.
Specifically, the power fusion communication network in the embodiment of the invention is a communication network of a smart grid in a popular sense.
Specifically, the smart grid may include components such as a power plant, a transformer substation, a control center, an electric power optical transmission network, a gateway, a base station, and a smart grid terminal, and the smart grid terminal may include two types, one being a smart meter and the other being a phasor measurement unit. The intelligent ammeter is generally arranged at the network terminal and is used for collecting power consumption information and sending the power consumption information to the control center; phasor measurement units are installed in power plants and substations for monitoring voltage and current. The power fusion communication network can connect a large number of intelligent terminals in the power grid together. The power distribution terminal in the embodiment of the invention refers to a smart grid terminal. The distribution terminals and the users are in one-to-one correspondence.
In implementations, the smart grid may include a large number of consumers of power consumption.
Specifically, embodiments of the present invention primarily consider the downlink of a RIS-assisted multi-user MISO system.
In some embodiments, the multi-user MISO system may include a base station with N antennas, a RIS with M reflecting elements, and K single-antenna users.
All user sets can be expressed as:all the reflection unit sets can be expressed as: />。
The signal received by the kth user can be expressed as:
(1)
wherein,to the base stationkChannel vectors for individual users;
to RIS to the firstkChannel vectors for individual users;
a channel matrix for base station to RIS;
for the base station to aim atiPersonal useBeamforming for user->；
Indicating that the base station transmits to the firstiSignals of individual users;
，/>is the phase angle;
for the amplitude reflection coefficient, generally set +.>；
Represent the firstkThe power at individual users is +.>Additive white gaussian noise of (c).
Step 120: a data transmission rate model and a power consumption model of the multi-user MISO system model are determined.
Specifically, according to the multi-user MISO system model of the RIS-based power fusion communication network and the downlink transmission link thereof, the signal-to-interference-and-noise ratio at the kth user can be obtainedThe method comprises the following steps:
(2)
wherein,characterization from the firstkInterference caused by other users than the individual user.
Step 130: determining an original optimization problem model based on the data transmission rate model and the power consumption model, wherein the objective of the original optimization problem model comprises maximizing the energy efficiency of the multi-user MISO system model; variables of the original optimization problem model include beamforming of the base station and phase angle of the RIS.
Step 140: determining a first sub-problem model and a second sub-problem model based on the original optimization problem model; the first sub-problem model comprises fixing the phase angle of RIS, and optimizing the beam forming of the base station; the second sub-problem model includes: beamforming of the fixed base station, and optimizing a phase angle of the RIS;
specifically, the original optimization problem is decomposed, and the modeled multitasking coupled original optimization problem is decomposed into 2 independent sub-problems: the first sub-problem model is to fix the phase angle of RIS and optimize the active beam shaping vector of the base station; the second sub-problem is to fix the beamforming vector of the base station, optimizing the phase angle of the RIS; and 2 optimization problems before and after alternate iteration, and finally obtaining an optimal solution of the original optimization problem.
And 150, alternately solving each sub-problem model, and further alternately iterating the first sub-problem model and the second sub-problem model to determine an optimal solution.
Step 160: and performing resource allocation on the RIS-based power fusion communication network based on the multi-user MISO system model for determining the optimal solution.
In this embodiment, for mass data transmission of a power fusion communication network, for example, data transmission of a high-definition map of a power distribution station, unified instructions of a control center, etc., firstly, a multi-user MISO system model assisted by RIS is constructed, a base station provides information service for all users, and because the transmission process may be affected by an obstacle, the embodiment proposes to enhance the gain of an information transmission channel by using the RIS technology, and improve the data transmission rate of the system; secondly, the original optimization problem with the aim of maximizing the energy efficiency is established, so that the system power consumption is reduced; and thirdly, because the original optimization problem relates to high-dimensional discrete variables and complex variable coupling relations, the original optimization problem is non-convex discontinuous and difficult to directly solve, the original optimization problem is split into 2 sub-problems for effectively solving the original optimization problem, and the optimal solution is further obtained by respectively optimizing the active beam forming of the base station and the phase angle of the RIS. Therefore, by designing the resource allocation method with energy efficiency priority, effective information transmission between the control center and the power distribution terminal is realized, and the quick control instruction issuing of time delay constraint can be met, so that the mass terminal access capability of the power fusion communication network is further improved, and the energy consumption of the power fusion communication network is reduced.
To illustrate the solution process of the inventive solution, in some embodiments, it is assumed that the multi-user MISO system includes a base station equipped with N antennas, a RIS with M reflecting elements, and K single-antenna users.
In a multi-user MISO system, all power loss mainly includes transmission power consumption of a base station, fixed power consumption of the base station itself, circuit power consumption of a power distribution terminal, and circuit power consumption of an RIS. Thus, determining a power consumption model of the multi-user MISO system model may include: the total power consumption of the multi-user MISO system model is determined based on the sum of the transmission power consumption of the base station, the fixed power consumption of the base station itself, the circuit power consumption of the distribution terminal, and the circuit power consumption of the RIS. It should be noted that, in the embodiment of the present invention, the transmission power consumption of the base station refers to the total transmission power consumption of the base station.
Further, the total power consumption of the multi-user MISO system model can be characterized using equation (3):
(3)
wherein,the total power consumption of the multi-user MISO system model; />The efficiency of the power amplifier at the base station end is;fixing power consumption for the base station itself; />Circuit power consumption for the kth user; />Is the total reflective element power consumption of the RIS.
In a multi-user MISO system, determining a data transmission rate model of the multi-user MISO system model may comprise: determining a data transmission rate of the base station for each user based on the signal-to-interference-and-noise ratio of each power distribution terminal; the sum rate of the base station for all users is determined based on the data transmission rate of the base station for each user and the number of users.
Further, the sum rate of base stations for all users in the multiuser MISO system model can be characterized by equation (4):
（4）
wherein,characterization and rate, B characterizes the transmission bandwidth of the signal.
Specifically, determining an original optimization problem model based on the data transmission rate model and the power consumption model includes: determining the limiting conditions of an original optimization problem model; the energy efficiency of the multi-user MISO system model is derived based on the ratio of sum rate to total power consumption and is maximized.
Further, determining the limiting condition of the original optimization problem model specifically includes:
determining that the data transmission rate of the base station for the current user is greater than or equal to the minimum data transmission rate required by the current user;
determining that the transmitting power of the base station is less than or equal to the maximum transmitting power;
determining the phase angle of RIS to be between 0 and 2Between them.
In some embodiments, modeling is performed by using the established data transmission model and the system power consumption model, and the energy efficiency of the multi-user MISO system is maximized, the beamforming of the base station and the phase angle of the RIS are used as variables, and meanwhile, the optimization problem of the limiting conditions such as the minimum rate requirement of the user, the maximum transmitting power of the base station and the phase angle constraint are met, and the original optimization problem can be modeled as formula (5):
(5)
(6)
(7)
(8)
wherein equation (6) characterizes the minimum rate requirement for each user;
equation (7) is the transmit power constraint of the base station end;
equation (8) is the phase angle constraint of RIS;
is the firstkA minimum rate threshold required by the individual user;
the process of solving equation (5) requires that three constraints of equation (6), equation (7) and equation (8) be satisfied simultaneously.
The following example illustrates the detailed steps of 2 sub-problem solving:
in an exemplary embodiment, optimizing the beamforming of the base station may include: on the premise of setting the base station to adopt maximum ratio transmission precoding, determining target beam forming of the base station for the current user; target beamforming is related to target transmitting power of a base station for a signal of a current user;
the target transmit power is optimized based on target beamforming.
Specifically, for the beamforming optimization problem, in order to reduce the computational complexity, it is assumed that the base station adopts maximum ratio transmission (Maximum Ratio Transmission, MRT) precoding, i.e. under MRT precoding, the user can obtain the maximum SINR. At this time, the beamforming can be expressed by the formula (9):
（9）
wherein,characterizing a beam forming direction, the beam forming direction being determined; />Target transmit power for each user signal for the base station, and target transmit power + ->Is varied in real time, so that the optimization of the beam forming can be further set to +.>Optimization of (i) by determining +.>To obtain the optimal solution for beamforming. In this embodiment, the target transmit power is +.>The updated formula of (2) is represented by (10):
(10)
wherein,as a lambertian (Lambert W Function) W function;
；
，/>a minimum rate threshold value required by each user, B is the transmission bandwidth of the signal;
，/>characterizing normalized channel gains;
；
is the maximum transmit power at the base station;
is the minimum transmit power at the base station.
In an exemplary embodiment, optimizing the phase angle of the RIS may include: determining channel gain corresponding to the multi-user MISO system model as an fitness function; and solving the phase angle of the RIS by using an improved sine and cosine algorithm based on the fitness function.
Specifically, the phase angle of RISOptimizing question of (2)The question may be further arranged to obtain the phase angle +.>Equation (11) is:
(11)
wherein, 。
equation (11) represents that the variable phase angle is obtained by maximizing the channel gain corresponding to the multi-user MISO system modelIs a solution to the optimization of (3).
Since the optimization problem (11) is non-convex, it is difficult to directly get a closed-form solution. To address this problem, the present embodiment utilizes a modified sine and cosine algorithm (Sine Cosine Algorithm, SCA) to solve the phase angleIs a solution to the optimization of (3). Specifically, solving the phase angle of the RIS using the modified SCA algorithm may include:
randomly generating I particles, wherein each particle meets unit mode constraint;
calculating the fitness function of each particle, specifically calculating the fitness function of each particle according to a first formula, namely formula (11), and finding out the maximum value of the fitness function in randomly generated particles to obtain a locally optimal particle;
selecting the previous I/2 particles to carry out iterative updating according to a second formula, comparing fitness functions of each time in the iterative updating process, and updating the locally optimal particles; the second formula is:
（12）
the I/2 particles are iteratively updated according to a third formula after selection, the local optimal solution at the moment is selected, the global optimal solution is developed and explored, the fitness function of each time is still compared in the process, and the optimal particles are updated;
the third formula is:
（13）
wherein,position information at the t iteration for the j-th dimension of the i-th particle;
the global optimal value of the j-th dimensional position information after t iterations;
and when the fitness function converges, obtaining a local optimal solution of the phase angle.
Specifically, the iteration is completed, the fitness function converges, the phase optimal solution is obtained, and the iteration times are increased if the fitness function does not converge.
Wherein,ωis a step size factor, obeys the lewye distribution, is initialized according to a fourth formula, i.e. formula (14),
(14)
as control parameters, the amplitudes of the sine and cosine functions are mainly controlled, and are adaptively adjusted by a fifth formula, i.e. formula (15),
(15)
，/>，/>for 3 random numbers obeying a uniform distribution, +.>；
t is the current iteration number;
t is the maximum iteration number;
a is a constant, and is generally 2;
is->The weight of the variable at the t-th iteration;
is a scaling factor; />
Is a standard gamma function.
In a specific embodiment, the simulation of the solution using the modified SCA algorithm is as follows:
the main parameters of the SCA algorithm are shown in Table 1:
table 1: SCA algorithm simulation parameters
Step 01: initializing randomly generating I particles Xi, i=1, 2, … …, I; wherein I is an even number;
step 02, firstly finding an optimal solution in initialization, and assigning the position of the optimal solution to Xi;
step 03, while T is less than or equal to T, do
Step 04, calculating according to a formula (15), and calculating w according to a formula (14);
step 05, for i=1: i do
Step 06 if I > I/2+1
Step 07, updating Xi based on formula (12)
Step 08 else
Step 09 updating Xi based on equation (13)
Step 10 end
Step 11, comparing the fitness function of the positions Xi and Xi-1
Step 12, updating the optimal solution of the optimal position Xi
Step 13 end
Step 14, end
Fig. 3 illustrates a schematic diagram of a variation trend of normalized channel gain values with iteration numbers under different schemes, and it is obvious that the normalized channel gain values obtained by the optimization of the improved SCA algorithm under multiple users in the present embodiment are higher than the normalized channel gain values obtained by the optimization of the classical SCA algorithm under multiple users in the related art, and the normalized channel gain values obtained by the optimization of the improved SCA algorithm under multiple users in the present embodiment are also higher than the normalized channel gain values obtained by the optimization of the principal and subordinate convex approximation algorithm under multiple users in the related art, which reflects the effectiveness of the improved SCA algorithm in the present embodiment.
Fig. 4 shows the average energy efficiency as a function of maximum transmit power for different schemes. As can be seen from fig. 4, when the maximum transmission power of the base station exceeds 17dBm, the average energy efficiency obtained by optimizing the improved SCA algorithm under multiple users in the present embodiment is higher than the average energy efficiency obtained by the conventional relay amplification scheme in the related art, which reflects the effectiveness of the scheme in the present embodiment.
The resource allocation device of the RIS-based power fusion communication network provided by the invention is described below, and the resource allocation device of the RIS-based power fusion communication network described below and the resource allocation method of the RIS-based power fusion communication network described above can be referred to correspondingly. The parts not mentioned in the embodiments of the resource allocation device of the RIS-based power fusion communication network described below may also all specifically refer to all the embodiments of the resource allocation method of the RIS-based power fusion communication network described above, and will not be described in detail.
As shown in fig. 5, an embodiment of the present invention further provides a resource allocation device of a RIS-based power fusion communication network, which may include:
a building module 510, configured to build a multi-user MISO system model for a RIS-based power fusion communication network; the multi-user MISO system model comprises a base station, power distribution terminals corresponding to multiple users one by one and a reconfigurable intelligent reflection surface RIS;
a first determining module 520, configured to determine a data transmission rate model and a power consumption model of the multi-user MISO system model;
a second determining module 530, configured to determine an original optimization problem model based on the data transmission rate model and the power consumption model, where a goal of the original optimization problem model includes maximizing energy efficiency of the multi-user MISO system model; variables of the original optimization problem model include beamforming of the base station and phase angle of the RIS;
a third determining module 540, configured to determine a first sub-problem model and a second sub-problem model based on the original optimization problem model; the first sub-problem model comprises fixing the phase angle of RIS, and optimizing the beam forming of the base station; the second sub-problem model includes: beamforming of the fixed base station, and optimizing a phase angle of the RIS;
an iteration module 550, configured to alternately iterate the first sub-problem model and the second sub-problem model to determine an optimal solution;
the allocation module 560 is configured to allocate resources to the RIS-based power fusion communication network based on the multiuser MISO system model that obtains the optimal solution.
In an exemplary embodiment, the first determining module 520 is specifically configured to determine a data transmission rate of the base station for each user based on a signal-to-interference-and-noise ratio of each power distribution terminal; the sum rate of the base station for all users is determined based on the data transmission rate of the base station for each user and the number of users.
In an exemplary embodiment, the first determining module 520 is specifically configured to determine the total power consumption of the multi-user MISO system model based on a sum of the transmission power consumption of the base station, the fixed power consumption of the base station itself, the circuit power consumption of the distribution terminal, and the circuit power consumption of the RIS.
In an exemplary embodiment, the second determining module 530 is specifically configured to determine an original optimization problem model based on the data transmission rate model and the power consumption model, including: determining the limiting conditions of an original optimization problem model; the energy efficiency of the multi-user MISO system model is derived based on the ratio of sum rate to total power consumption and is maximized.
In an exemplary embodiment, further, the second determining module 530 is specifically configured to determine that the data transmission rate of the base station for the current user is greater than or equal to the minimum data transmission rate required by the current user; determining that the transmitting power of the base station is less than or equal to the maximum transmitting power; determining the phase angle of RIS to be between 0 and 2Between them.
In an exemplary embodiment, the third determining module 540 is specifically configured to determine target beamforming of the base station for the current user on the premise that the base station is set to use maximum ratio transmission precoding; target beamforming is related to target transmitting power of a base station for a signal of a current user; the target transmit power is optimized based on target beamforming.
In an exemplary embodiment, the third determining module 540 is specifically configured to determine that a channel gain corresponding to the multi-user MISO system model is an fitness function; and solving the phase angle of the RIS by using an improved sine and cosine algorithm based on the fitness function.
In an exemplary embodiment, the iteration module 550 is specifically configured to solve the phase angle of the RIS using a modified sine and cosine algorithm, including: randomly generating I particles, wherein each particle meets unit mode constraint; calculating the fitness function of each particle; selecting the first I/2 particles for iterative updating; performing iterative updating on the I/2 particles after selection; and when the fitness function converges, obtaining a local optimal solution of the phase angle.
In an exemplary embodiment, further, the iteration module 550 is specifically configured to:
obtaining a fitness function of each particle according to a first formula, wherein the first formula is as follows:
；
selecting the first I/2 particles for iterative updating, comprising:
the first I/2 particles are iteratively updated according to a second formula; the second formula is:
；
the I/2 particles after selection are subjected to iterative updating, which comprises the following steps:
the latter I/2 particles are iteratively updated according to a third formula; the third formula is:
，
wherein,to RIS to the firstkChannel vectors for individual users;
to the base stationkChannel vectors for individual users;
，/>is the phase angle;
a channel matrix for base station to RIS;
position information at the t iteration for the j-th dimension of the i-th particle;
the global optimal value of the j-th dimensional position information after t iterations;
is a step factor, obeys the Lewy distribution, is initialized according to a fourth formula, and the fourth formula is as follows:
；
for controlling parameters, the parameters are adaptively adjusted through a fifth formula, wherein the fifth formula is as follows:
；
，/>，/>for 3 random parameters subject to a uniform distribution,；/>
t is the current iteration number;
t is the maximum iteration number;
a is a constant, and is generally 2;
is->The weight of the variable at the t-th iteration;
is a scaling factor;
is a standard gamma function.
In the resource allocation device of the RIS-based power fusion communication network in the embodiment, a multi-user MISO system model for the RIS-based power fusion communication network is constructed, a data transmission rate model and a power consumption model are determined in the multi-user MISO system model, an original optimization problem model is determined based on the data transmission rate model and the power consumption model, in order to better solve the original optimization problem, the original optimization problem is divided into a first sub-problem model and a second sub-problem model, then the first sub-problem and the second sub-problem are alternately and iteratively solved, an optimal solution is determined, and finally, resource allocation is performed on the RIS-based power fusion communication network based on the multi-user MISO system model with the optimal solution. Therefore, by designing the resource allocation method with energy efficiency priority, effective information transmission between the control center and the power distribution terminal is realized, the quick control instruction issuing of time delay constraint can be met, the mass terminal access capability of the power fusion communication network is further improved, and the energy consumption is reduced.
In one aspect, the present invention also provides a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, are capable of performing the method for resource allocation of a RIS-based power fusion communication network provided by any of the embodiments above, the method comprising: constructing a multi-user MISO system model oriented to an RIS-based power fusion communication network; the multi-user MISO system model comprises a base station, power distribution terminals corresponding to multiple users one by one and RIS; determining a data transmission rate model and a power consumption model of a multi-user MISO system model; determining an original optimization problem model based on the data transmission rate model and the power consumption model, wherein the objective of the original optimization problem comprises maximizing the energy efficiency of the multi-user MISO system model; variables of the original optimization problem include beamforming of the base station and phase angle of the RIS; determining a first sub-problem model and a second sub-problem model based on the original optimization problem model; the first sub-problem model comprises fixing the phase angle of RIS, and optimizing the beam forming of the base station; the second sub-problem model includes: beamforming of the fixed base station, and optimizing a phase angle of the RIS; alternately iterating the first sub-problem and the second sub-problem to determine an optimal solution; and performing resource allocation on the RIS-based power fusion communication network based on the multi-user MISO system model for obtaining the optimal solution.
In yet another aspect, the present invention further provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, is implemented to perform the method for resource allocation of a RIS-based power fusion communication network provided in any of the above embodiments, the method comprising: constructing a multi-user MISO system model oriented to an RIS-based power fusion communication network; the multi-user MISO system model comprises a base station, power distribution terminals corresponding to multiple users one by one and RIS; determining a data transmission rate model and a power consumption model of a multi-user MISO system model; determining an original optimization problem model based on the data transmission rate model and the power consumption model, wherein the objective of the original optimization problem comprises maximizing the energy efficiency of the multi-user MISO system model; variables of the original optimization problem include beamforming of the base station and phase angle of the RIS; determining a first sub-problem model and a second sub-problem model based on the original optimization problem model; the first sub-problem model comprises fixing the phase angle of RIS, and optimizing the beam forming of the base station; the second sub-problem model includes: beamforming of the fixed base station, and optimizing a phase angle of the RIS; alternately iterating the first sub-problem and the second sub-problem to determine an optimal solution; and performing resource allocation on the RIS-based power fusion communication network based on the multi-user MISO system model for obtaining the optimal solution.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (6)

1. A method for allocating resources of a RIS-based power fusion communication network, comprising:
constructing a multi-user MISO system model oriented to an RIS-based power fusion communication network; the multi-user MISO system model comprises a base station, and power distribution terminals and RISs which are in one-to-one correspondence with multiple users;
determining a data transmission rate model and a power consumption model of the multi-user MISO system model;
determining an original optimization problem model based on the data transmission rate model and the power consumption model, wherein the objective of the original optimization problem model comprises maximizing the energy efficiency of the multi-user MISO system model; variables of the original optimization problem model include beamforming of the base station and a phase angle of the RIS;
determining a first sub-problem model and a second sub-problem model based on the original optimization problem model; the first sub-problem model includes fixing a phase angle of the RIS, optimizing beamforming of the base station; the second sub-problem model includes: fixing the beam forming of the base station and optimizing the phase angle of the RIS;
alternately iterating the first sub-problem model and the second sub-problem model to determine an optimal solution;
performing resource allocation on the RIS-based power fusion communication network based on a multi-user MISO system model for obtaining the optimal solution;
the optimizing the beamforming of the base station includes:
on the premise of setting the base station to adopt maximum ratio transmission precoding, determining target beam forming of the base station for the current user; the target beam forming is related to target transmitting power of the base station aiming at the signal of the current user;
optimizing the target transmit power based on the target beamforming;
said optimizing the phase angle of said RIS comprising:
determining the channel gain corresponding to the multi-user MISO system model as an fitness function;
based on the fitness function, solving the phase angle of the RIS by utilizing an improved sine and cosine algorithm;
the solving the phase angle of the RIS by using the improved sine and cosine algorithm comprises the following steps:
randomly generating I particles, wherein each particle meets unit mode constraint;
calculating the fitness function of each particle;
selecting the first I/2 particles for iterative updating;
performing iterative updating on the I/2 particles after selection;
and when the fitness function converges, obtaining a local optimal solution of the phase angle.
2. The method for resource allocation of an RIS-based power fusion communication network of claim 1, wherein said determining a data transmission rate model of the multi-user MISO system model comprises:
determining the data transmission rate of the base station for each user based on the signal-to-interference-and-noise ratio of each power distribution terminal;
the sum rate of the base station for all users is determined based on the data transmission rate of the base station for each user and the number of users.
3. The method for resource allocation of an RIS-based power fusion communication network of claim 2, wherein said determining a power consumption model of the multi-user MISO system model comprises:
and determining the total power consumption of the multi-user MISO system model based on the sum of the transmission power consumption of the base station, the self-fixed power consumption of the base station, the circuit power consumption of the power distribution terminal and the circuit power consumption of the RIS.
4. A method for resource allocation of a RIS-based power fusion communications network according to claim 3, wherein said determining an original optimization problem model based on said data transfer rate model and said power consumption model comprises:
determining the limiting conditions of the original optimization problem model;
and obtaining the energy efficiency of the multi-user MISO system model based on the ratio of the sum rate to the total power consumption and maximizing the energy efficiency.
5. The method for resource allocation of an RIS-based power fusion communication network of claim 4, wherein said determining constraints of the raw optimization problem model includes:
determining that the data transmission rate of the base station for the current user is greater than or equal to the minimum data transmission rate required by the current user;
determining that the transmitting power of the base station is less than or equal to the maximum transmitting power;
determining that the phase angle of the RIS is between 0 and 2Between them.
6. A resource allocation apparatus of an RIS-based power fusion communication network for performing the method for allocating resources of an RIS-based power fusion communication network according to any one of claims 1 to 5, comprising:
the construction module is used for constructing a multi-user MISO system model oriented to the RIS-based power fusion communication network; the multi-user MISO system model comprises a base station, a power distribution terminal corresponding to multiple users one by one and a reconfigurable intelligent reflection surface RIS;
a first determining module, configured to determine a data transmission rate model and a power consumption model of the multi-user MISO system model;
a second determining module configured to determine an original optimization problem model based on the data transmission rate model and the power consumption model, the objective of the original optimization problem model including maximizing energy efficiency of the multi-user MISO system model; variables of the original optimization problem model include beamforming of the base station and a phase angle of the RIS;
a third determining module, configured to determine a first sub-problem model and a second sub-problem model based on the original optimization problem model; the first sub-problem model includes fixing a phase angle of the RIS, optimizing beamforming of the base station; the second sub-problem model includes: fixing the beam forming of the base station and optimizing the phase angle of the RIS;
the iteration module is used for alternately iterating the first sub-problem model and the second sub-problem model to determine an optimal solution;
and the allocation module is used for allocating resources to the RIS-based power fusion communication network based on the multi-user MISO system model for obtaining the optimal solution.
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