CN109743713B - Resource allocation method and device for electric power Internet of things system - Google Patents
Resource allocation method and device for electric power Internet of things system Download PDFInfo
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Abstract
The invention discloses a resource allocation method and a device of an electric power Internet of things system, wherein the method comprises the following steps: determining energy and power constraint conditions of the electric power Internet of things system according to receiving and transmitting signal-to-noise ratios of terminals in the electric power Internet of things system; calculating the optimal transmitting power according to a Dinkelbach method model and energy and power constraint conditions; determining all terminal-channel-relay combinations in the electric power Internet of things system according to the optimal transmitting power; and determining the optimal terminal-channel-relay combination from all the terminal-channel-relay combinations according to the genetic algorithm model and the optimal transmitting power. By implementing the method and the device, the total energy efficiency of the system is minimized, the resources of the power internet of things system are effectively divided according to the Dinkelbach method model and the genetic algorithm model, the time of computer calculation and the resources required to be consumed are saved, the energy efficiency of the internet of things terminal is improved, and the access capability of the system is improved.
Description
Technical Field
The invention relates to the technical field of power Internet of things, in particular to a resource allocation method and device for a power Internet of things system.
Background
Large-scale Machine Type Communication (mtc) is expected to play an important role in future 5G wireless networks as one of three directions of 5G application scenarios. It will be able to support hundreds of billions of low complexity and energy limited machine class terminals. In particular, mtc requires over 100 million connections in the range of 1 square kilometer, whereas today's 4G mobile networks support up to thousands of connections, which generally limits its use in mobile phones, computers and similar smart devices.
The main challenge of mtc is to provide reliable, efficient connections for a large number of devices, the solution of which requires wide area coverage and deep indoor penetration, while being low cost and energy efficient. Meanwhile, most mtc devices are generally equipped with a low-capacity battery, and are expected to operate for a long time without replacing the battery. Therefore, it is desirable to reduce the energy efficiency of mtc devices. At present, a fixed spectrum allocation mode is generally adopted in the mMTC during equipment communication, channels in the Internet of things cannot be fully utilized by the allocation mode, and the spectrum utilization rate is low.
Disclosure of Invention
In view of this, embodiments of the present invention provide a resource allocation method and an apparatus for an electric power internet of things system, so as to solve the technical problems that in the prior art, when fixed spectrum allocation is adopted by an mtc device, a channel in the internet of things cannot be fully utilized, and the spectrum utilization rate is low.
The technical scheme provided by the invention is as follows:
the first aspect of the embodiment of the invention provides a resource allocation method for an electric power Internet of things system, which comprises the following steps: determining energy and power constraint conditions of the electric power Internet of things system according to receiving and transmitting signal-to-noise ratios of terminals in the electric power Internet of things system; calculating the optimal transmitting power according to a Dinkelbach method model and the energy and power constraint conditions; determining all terminal-channel-relay combinations in the electric power Internet of things system according to the optimal transmitting power; and determining the optimal terminal-channel-relay combination from all the terminal-channel-relay combinations according to a genetic algorithm model and the optimal transmitting power.
Further, the determining the energy and power constraint conditions of the electric power internet of things system according to the receiving and transmitting signal-to-noise ratios of the terminals in the electric power internet of things system comprises the following steps: determining the signal-to-noise ratio received by a relay and a base station in the electric power Internet of things system according to the receiving and transmitting signal-to-noise ratio of a terminal in the electric power Internet of things system, wherein the signal-to-noise ratio received by the relay and the base station is greater than a signal-to-noise ratio threshold; and determining the constraint condition of the terminal transmitting power in the power Internet of things system according to the signal-to-noise ratios received by the relay and the base station.
Further, the calculating the optimal transmitting power according to the Dinkelbach method model and the energy and power constraint condition comprises: determining a target function of the transmitting power according to the constraint condition of the transmitting power of the terminal; and solving the objective function of the transmitting power according to a Dinkelbach method model to obtain the optimal transmitting power.
Further, the determining an optimal terminal-channel-relay combination from all the terminal-channel-relay combinations according to a genetic algorithm model and the optimal transmit power includes: determining constraint conditions of the terminal-channel-relay combination according to connection limitations of the terminal and the relay in all the terminal-channel-relay combinations and limitations of a channel used by the terminal; and determining the optimal terminal-channel-relay combination according to the constraint conditions of the terminal-channel-relay combination and all the terminal-channel-relay combinations.
Further, the constraint conditions of the terminal-channel-relay combination include: each terminal is connected with a channel and each terminal is connected with a relay.
A second aspect of the embodiments of the present invention provides a resource allocation device for an electric power internet of things system, where the resource allocation device includes: the constraint condition determining module is used for determining energy and power constraint conditions of the electric power Internet of things system according to the receiving and transmitting signal-to-noise ratios of the terminals in the electric power Internet of things system; the optimal transmitting power calculation module is used for calculating the optimal transmitting power according to the Dinkelbach method model and the energy and power constraint conditions; the combination determining module is used for determining all terminal-channel-relay combinations in the electric power Internet of things system according to the optimal transmitting power; and the optimal combination determining module is used for determining the optimal terminal-channel-relay combination from all the terminal-channel-relay combinations according to a genetic algorithm model and the optimal transmitting power.
Further, the constraint condition determination module comprises: the signal-to-noise ratio determining module is used for determining the signal-to-noise ratio received by a relay and a base station in the electric power Internet of things system according to the receiving and transmitting signal-to-noise ratio of the terminal in the electric power Internet of things system, wherein the signal-to-noise ratio received by the relay and the base station is greater than a signal-to-noise ratio threshold; and the transmission power determining module is used for determining the constraint condition of the terminal transmission power in the power Internet of things system according to the signal-to-noise ratio received by the relay and the base station.
Further, the optimal transmit power calculation module comprises: the target function determining module is used for determining a target function of the transmitting power according to the constraint condition of the transmitting power of the terminal; and the optimal transmitting power calculation submodule is used for solving a target function of the transmitting power according to a Dinkelbach method model to obtain the optimal transmitting power.
Further, the optimal combination determination module includes: a combination constraint condition determining module, configured to determine constraint conditions of the terminal-channel-relay combination according to connection limitations of the terminal and the relay in all the terminal-channel-relay combinations and limitations of a channel used by the terminal; and the optimal combination determining submodule is used for determining the optimal terminal-channel-relay combination according to the constraint condition of the terminal-channel-relay combination.
A third aspect of the embodiments of the present invention provides a computer-readable storage medium, which stores computer instructions for causing a computer to execute a resource allocation method for an electric power internet of things system according to any one of the first aspect of the embodiments of the present invention.
A fourth aspect of the present invention provides a resource allocation device for an electric power internet of things system, including: the power internet of things system resource allocation method comprises a memory and a processor, wherein the memory and the processor are connected in communication with each other, the memory stores computer instructions, and the processor executes the computer instructions so as to execute the resource allocation method of the power internet of things system according to any one of the first aspect of the embodiment of the invention.
The technical scheme provided by the embodiment of the invention has the following advantages:
the embodiment of the invention provides a resource allocation method and a resource allocation device for an electric power Internet of things system, which relate to a joint optimization problem of relay selection, channel allocation and power control. To make it easy to handle, the present invention equivalently decomposes the problem into a power control subproblem and a relay channel matching subproblem. The power control sub-problem is described as a Nonlinear Fractional Programming (NFP) problem, and a Dinkelbach method model for solving the problem is proposed, which can complete the solution of the problem within a few seconds, and the result of the solution is an optimal solution. The relay channel matching subproblem is quickly and efficiently solved through an intelligent genetic algorithm, the change range of variables can be limited by adopting the intelligent genetic algorithm, the time of computer calculation and resources required to be consumed are saved, and the same effect can be achieved.
The resource allocation method and device for the power internet of things system provided by the embodiment of the invention adopt a scheme aiming at minimizing the total energy efficiency of the system, can improve the energy efficiency of the internet of things terminal and improve the access capability of the system on the premise of ensuring the access quality of the original internet of things terminal. Meanwhile, the invention ensures that the energy efficiency obtained by the resource allocation method reaches the maximum energy efficiency according to strict mathematical derivation. In addition, the resource allocation method can enable the edge physical network terminal to access the base station under the condition of realizing high energy efficiency, thereby improving the coverage area of the base station and saving the deployment cost of the system.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart illustrating a resource allocation method of an electric power Internet of things system according to an embodiment of the invention;
FIG. 2 is a flow chart illustrating a resource allocation method of the power Internet of things system according to another embodiment of the invention;
fig. 3 is a block diagram showing a configuration of a resource allocation apparatus of an electric power internet of things system according to another embodiment of the present invention;
fig. 4 is a block diagram showing a configuration of a resource allocation apparatus of an electric power internet of things system according to another embodiment of the present invention;
fig. 5 is a block diagram showing a configuration of a resource allocation apparatus of an electric power internet of things system according to another embodiment of the present invention;
fig. 6 is a block diagram showing a configuration of a resource allocation apparatus of an electric power internet of things system according to another embodiment of the present invention;
fig. 7 is a schematic diagram illustrating a hardware structure of a resource allocation terminal of an electric power internet of things system according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides a resource allocation method of an electric power Internet of things system, which comprises the following steps as shown in figure 1:
step S101: and determining energy and power constraint conditions of the electric power Internet of things system according to the receiving and transmitting signal-to-noise ratios of the terminals in the electric power Internet of things system. In order to ensure stable transmission of signals in the power internet of things system, the signal-to-noise ratios of relays and base stations in the power internet of things system, which are obtained according to the signal-to-noise ratios of the signals received and transmitted by the terminals in the power internet of things system, need to be greater than or equal to a signal-to-noise ratio threshold, which can be specifically expressed by formula (1):
wherein, γthWhich represents a signal-to-noise ratio threshold,
representing the signal-to-noise ratio transmitted by the terminal to the relay over the channel,
signal to noise ratio, gamma, of terminal transmitted to base station through channel and relayr,bRepresenting the signal-to-noise ratio of the relay transmission to the base station. Equation (1) indicates that the above three signal-to-noise ratios need to be greater than or equal to the signal-to-noise ratio threshold.
Determining a constraint condition of terminal transmitting power in the power Internet of things system according to signal-to-noise ratios received by the relay and the base station, wherein the constraint condition of the terminal transmitting power can be expressed by a formula (2) to a formula (5):
wherein, PdDenotes the transmit power of the terminal, d, r, k denote the numbers of the terminal, relay and channel, respectively, hd,BFor the channel coefficients of the terminal to the base station,
for the channel coefficients of the terminal to the relay,
for the channel coefficient from ordinary user to relay, σ2Representing white noise power. PLRepresenting a lower power limit, and expressed by formula (3), wherein the specific meaning of formula (3) is that both a signal sent by a terminal to a relay and a signal sent by the relay to a base station need to satisfy a signal-to-noise ratio condition; pURepresenting the upper power limit, expressed by formula (4), the concrete meaning of formula (4) is the upper power limit P of the terminal transmitting powerUNot exceeding the transmission power P of the ordinary usersnRepresents; equation (2) indicates that the transmission power of the terminal is limited within the range of the upper power limit and the lower power limit for any d, r, k.
In addition, the resource allocation method of the power internet of things system aims at maximizing energy efficiency, so that energy constraint conditions can be obtained, and the constraint conditions are expressed by the following formula (6):
wherein EE represents a variable representing energy efficiency, and the formula represents an energy constraint condition under which energy efficiency is maximized.
Step S102: and calculating the optimal transmitting power according to the Dinkelbach method model and the energy and power constraint conditions. The formula (2) and the formula (6) respectively give a power constraint condition and an energy constraint condition, an objective function of the terminal transmitting power can be obtained by the power constraint condition, the formula (3) to the formula (5) is used for representing, the formula (3) to the formula (5) needs to be solved when the optimal transmitting power is calculated, a Dinkelbach method model can be used for solving, and as shown in FIG. 2, the concrete solving process comprises the following three steps:
step S201: initializing, selecting any initial solution p0Setting t equal to 1, q1＝φ(p0)/ψ(p0). Wherein phi (p)0)＝log2(1+γd,r,b)，ψ(p0)＝(Pd+Pn+2P0)/Bk。p0Denotes the initial power, PdIs the terminal transmit power, PnIndicating the transmission power, P, of the ordinary user0The power is muted. B iskIs the bandwidth.
Step S202: solving for g (p) ═ max { phi (p) -qkPsi (p) } to obtain an optimal solution pt。
ptAnd expressing the optimal solution of the power, namely the optimal transmitting power.
Step S203: if h (q)t)-h(qt-1) <e, stop. Otherwise, return to step S202.
E represents a minimum value.
According to the steps S201 to S203, the optimal transmission power of the resource allocation method of the power internet of things system can be obtained.
Step S103: and determining all terminal-channel-relay combinations in the electric power Internet of things system according to the optimal transmitting power. The optimal transmitting power of the resource allocation method of the power Internet of things system is aimed at maximizing energy efficiency, and all terminal-channel-relay combinations under the condition of maximizing the total energy efficiency of the system can be obtained after the optimal transmitting power is determined.
Step S104: and determining the optimal terminal-channel-relay combination from all the terminal-channel-relay combinations according to the genetic algorithm model and the optimal transmitting power. Wherein, the selection of the terminal-channel-relay combination aims at maximizing the total energy efficiency of the system, and the constraint condition of the terminal-channel-relay combination can be established according to the connection limit of the terminal and the relay and the limit of the terminal using the channel. Specifically, it can be expressed by formula (7) and formula (8):
wherein D, K, R also represents the terminal, channel and relay, specifically the total amount of the terminal, channel and relay, and equation (7) represents that all terminal-channel-relay combinations need to satisfy the requirement of maximizing the total energy efficiency of the system. Equation (8) shows that the connection limit of the terminals and the relays and the limit condition of the terminals using the channels on the premise that all the combinations of terminals-channel-relays satisfy the maximum total energy efficiency of the system, can be interpreted as that each terminal is connected with one channel and each terminal is connected with one relay.
Specifically, the formula (8) can be obtained from the following formula (9) and formula (10),
wherein the binary variable thetad,kThe method can be defined that the channel of the terminal in the electric power Internet of things system can be reused by at most one terminal in the electric power Internet of things system, and the binary variable xid,rThe terminal representing each power internet of things system can select one relay node. A binary variable X can be definedD×R×K∈RD×R×KIf and only if θd,k＝ξd,rWhen 1, xd,r,k1, otherwise xd,r,kAnd 0, that is, each channel cannot be simultaneously multiplexed by two or more terminals of the power internet of things system, and each relay node can only be selected as a relay node by at most one terminal of the power internet of things system. From this, equation (8) can be obtained.
According to the formula (7) and the formula (8), the optimal terminal-channel-relay combination can be determined from all the terminal-channel-relay combinations, the process is an intelligent genetic algorithm solving process, firstly, a certain number of candidate solutions are randomly generated and abstractly expressed as chromosomes, so that the population is evolved towards a better solution, and the solutions are expressed by binary systems (namely strings of 0 and 1). Evolution starts with a population of completely random individuals, followed by one generation. The fitness of the entire population is evaluated in each generation, a number of individuals are randomly selected from the current population (based on their fitness), a new life population is created by natural selection and mutation, which becomes the current population in the next iteration of the algorithm.
Through the steps S101 to S104, the resource allocation method for the power internet of things system provided by the embodiment of the present invention relates to a joint optimization problem of relay selection, channel allocation and power control, and during actual processing, the joint optimization problem is converted into a Mixed Integer Programming (MIP) problem. To make it easy to handle, the present invention equivalently decomposes the problem into a power control subproblem and a relay channel matching subproblem. The power control sub-problem is described as a Nonlinear Fractional Programming (NFP) problem, and a Dinkelbach method model for solving the problem is proposed, which can complete the solution of the problem within a few seconds, and the result of the solution is an optimal solution. The relay channel matching subproblem is quickly and efficiently solved through an intelligent genetic algorithm, the variable range can be limited by adopting the intelligent genetic algorithm, the time of computer calculation and the resource required to be consumed are saved, and the same effect can be achieved.
The resource allocation method of the electric power internet of things system provided by the embodiment of the invention adopts a scheme aiming at minimizing the total energy efficiency of the system, can improve the energy efficiency of the internet of things terminal and improve the access capability of the system on the premise of ensuring the access quality of the original internet of things terminal. Meanwhile, the invention ensures that the energy efficiency obtained by the resource allocation method reaches the maximum energy efficiency according to strict mathematical derivation. In addition, the resource allocation method can enable the edge physical network terminal to access the base station under the condition of realizing high energy efficiency, thereby improving the coverage area of the base station and saving the deployment cost of the system.
An embodiment of the present invention further provides a resource allocation device for an electric power internet of things system, as shown in fig. 3, the resource allocation device includes:
the constraint condition determining module 1 is used for determining energy and power constraint conditions of the electric power Internet of things system according to receiving and transmitting signal-to-noise ratios of terminals in the electric power Internet of things system; for details, refer to the related description of step S101 in the above method embodiment.
The optimal transmitting power calculation module 2 is used for calculating the optimal transmitting power according to the Dinkelbach method model and the energy and power constraint conditions; for details, refer to the related description of step S102 in the above method embodiment.
The combination determining module 3 is used for determining all terminal-channel-relay combinations in the electric power Internet of things system according to the optimal transmitting power; for details, refer to the related description of step S103 in the above method embodiment.
And the optimal combination determining module 4 is used for determining the optimal terminal-channel-relay combination from all the terminal-channel-relay combinations according to the genetic algorithm model and the optimal transmitting power. For details, refer to the related description of step S104 in the above method embodiment.
Through the modules 1 to 4, the resource allocation device of the power internet of things system provided by the embodiment of the invention relates to a joint optimization problem of relay selection, channel allocation and power control, and the joint optimization problem is converted into a Mixed Integer Programming (MIP) problem during actual processing. To make it easy to handle, the present invention equivalently decomposes the problem into a power control subproblem and a relay channel matching subproblem. The power control sub-problem is described as a Nonlinear Fractional Programming (NFP) problem, and a Dinkelbach method model for solving the problem is proposed, which can complete the solution of the problem within a few seconds, and the result of the solution is an optimal solution. The relay channel matching subproblem is quickly and efficiently solved through an intelligent genetic algorithm, the variable range can be limited by adopting the intelligent genetic algorithm, the time of computer calculation and the resource required to be consumed are saved, and the same effect can be achieved.
The resource allocation device of the electric power internet of things system provided by the embodiment of the invention adopts a scheme aiming at minimizing the total energy efficiency of the system, and can improve the energy efficiency of the internet of things terminal and the access capability of the system on the premise of ensuring the access quality of the original internet of things terminal. Meanwhile, the invention ensures that the energy efficiency obtained by the resource allocation device reaches the maximum energy efficiency according to strict mathematical derivation. In addition, the resource allocation device can enable the edge physical network terminal to be accessed to the base station under the condition of realizing high energy efficiency, thereby improving the coverage area of the base station and saving the deployment cost of the system.
In a preferred embodiment, as shown in fig. 4, the constraint determining module 1 includes:
the signal-to-noise ratio determining module 11 is used for determining signal-to-noise ratios received by a relay and a base station in the electric power internet of things system according to the receiving and transmitting signal-to-noise ratios of the terminals in the electric power internet of things system, wherein the signal-to-noise ratios received by the relay and the base station are greater than a signal-to-noise ratio threshold; for details, refer to the related description of step S101 in the above method embodiment.
And the transmission power determining module 12 is configured to determine a constraint condition of terminal transmission power in the power internet of things system according to the signal-to-noise ratios received by the relay and the base station. For details, refer to the related description of step S101 in the above method embodiment.
In a preferred embodiment, as shown in fig. 5, the optimal transmit power calculation module 2 includes:
an objective function determining module 21, configured to determine an objective function of the transmit power according to a constraint condition of the transmit power of the terminal; for details, refer to the related description of step S102 in the above method embodiment.
And the optimal transmitting power calculation submodule 22 is used for solving an objective function of the transmitting power according to the Dinkelbach method model to obtain the optimal transmitting power. For details, refer to the related description of step S102 in the above method embodiment.
In a preferred embodiment, as shown in fig. 6, the optimal combination determination module 4 includes:
a combination constraint condition determining module 41, configured to determine constraint conditions of the terminal-channel-relay combination according to connection limitations of the terminal and the relay in all the terminal-channel-relay combinations and limitations of a channel used by the terminal; for details, refer to the related description of step S104 in the above method embodiment.
And an optimal combination determining submodule 42, configured to determine an optimal terminal-channel-relay combination according to the constraint condition of the terminal-channel-relay combination. For details, refer to the related description of step S104 in the above method embodiment.
The functional description of the resource allocation device of the power internet of things system provided by the embodiment of the invention refers to the description of the resource allocation method of the power internet of things system in the above embodiment in detail.
An embodiment of the present invention further provides a resource allocation terminal of an electric power internet of things system, as shown in fig. 7, the resource allocation terminal of the electric power internet of things system may include a processor 51 and a memory 52, where the processor 51 and the memory 52 may be connected by a bus or in another manner, and fig. 7 takes the connection by the bus as an example.
The processor 51 may be a Central Processing Unit (CPU). The Processor 51 may also be other general purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or combinations thereof.
The memory 52, which is a non-transitory computer-readable storage medium, may be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules (for example, the constraint condition determination module 1, the optimal transmission power calculation module 2, the combination determination module 3, and the optimal combination determination module 4 shown in fig. 3) corresponding to the resource allocation device of the power internet of things system in the embodiment of the present invention. The processor 51 executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions and modules stored in the memory 52, that is, implements the resource allocation method of the power internet of things system in the above method embodiment.
The memory 52 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor 51, and the like. Further, the memory 52 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 52 may optionally include memory located remotely from the processor 51, and these remote memories may be connected to the processor 51 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The one or more modules are stored in the memory 52, and when executed by the processor 51, perform a resource allocation method of the power internet of things system as in the embodiment shown in fig. 1.
The specific details of the resource allocation terminal of the power internet of things system may be understood with reference to the corresponding related description and effects in the embodiment shown in fig. 1, and are not described herein again.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.
Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.
Claims (11)
1. A resource allocation method of an electric power Internet of things system is characterized by comprising the following steps:
determining energy and power constraint conditions of the electric power Internet of things system according to receiving and transmitting signal-to-noise ratios of terminals in the electric power Internet of things system;
the signal-to-noise ratio of the relay and the base station in the electric power internet of things system, which is obtained according to the signal-to-noise ratio of the terminal receiving and transmitting signals in the electric power internet of things system, is required to be greater than or equal to a signal-to-noise ratio threshold, and is expressed by the following formula (1):
wherein, γthWhich represents a signal-to-noise ratio threshold,
representing the signal-to-noise ratio transmitted by the terminal to the relay over the channel,
representing the signal-to-noise ratio, gamma, of the terminal transmitted to the base station via the channel and the relayr，bRepresents the signal-to-noise ratio of the relay transmission to the base station;
the constraint condition of the terminal transmitting power can be expressed by formula (2) to formula (5):
wherein, PdDenotes the transmit power of the terminal, d, r, k denote the numbers of the terminal, relay and channel, respectively, hd，BFor the channel coefficients of the terminal to the base station,
for the channel coefficients of the terminal to the relay,
for the channel coefficient from ordinary user to relay, σ2Representing white noise power; pLRepresents the lower limit of power, and is expressed by formula (3), wherein the specific meaning of formula (3) isThe signal sent by the terminal to the relay and the signal sent by the relay to the base station both need to meet the signal-to-noise ratio condition; pURepresenting the upper power limit, expressed by formula (4), the concrete meaning of formula (4) is the upper power limit P of the terminal transmitting powerUNot exceeding the transmission power P of the ordinary usersnRepresents; formula (2) shows that for any d, r and k, the transmitting power of the terminal is limited within the range of the upper power limit and the lower power limit;
the constraint of energy is expressed by equation (6):
wherein EE represents a variable representing energy efficiency, and the formula represents an energy constraint condition under the condition of maximizing the energy efficiency;
calculating the optimal transmitting power according to a Dinkelbach method model and the energy and power constraint conditions;
solving equations (3) to (5) according to the Dinkelbach method model, and calculating the optimal transmitting power comprises:
s1: initializing, selecting any initial solution p0Setting t equal to 1, q1＝φ(p0)/ψ(p0) (ii) a Wherein phi (p)0)＝log2(1+γd，r，b)，ψ(p0)＝(Pd+Pn+2P0)/Bk；p0Denotes the initial power, PdIs the terminal transmit power, PnIndicating the transmission power, P, of the ordinary user0A mute power; b iskIs the bandwidth;
s2: solving for g (p) ═ max { phi (p) -qkPsi (p) } to obtain an optimal solution pt；
ptExpressing the optimal solution of the power, namely the optimal transmitting power;
s3: if h (q)t)-h(qt-1) E, stopping; otherwise, return to step S2;
e represents a minimum value;
determining all terminal-channel-relay combinations in the electric power Internet of things system according to the optimal transmitting power; all terminal-channel-relay combinations are obtained with the goal of maximizing system energy efficiency;
determining an optimal terminal-channel-relay combination from all the terminal-channel-relay combinations according to a genetic algorithm model and the optimal transmitting power;
the constraint condition of the terminal-channel-relay combination is formulated according to the connection limit of the terminal and the relay and the limit of the terminal using the channel, and is expressed by formula (7) and formula (8):
wherein D, K, R also represents terminals, channels and relays, specifically the total amount of terminals, channels and relays, and formula (7) represents that all terminal-channel-relay combinations need to satisfy the maximum system total energy efficiency; formula (8) shows that the connection limit of the terminal and the relay and the limit condition of the terminal using the channel are interpreted as that each terminal is connected with one channel and each terminal is connected with one relay respectively on the premise that all the combinations of the terminal, the channel and the relay meet the maximum total energy efficiency of the system;
the formula (8) is obtained from the following formula (9) and formula (10),
wherein the binary variable thetad，kCan be defined as a channel of a terminal in the electric power Internet of things systemThe binary variable xi can be reused by at most one terminal of the electric power Internet of things systemd，rA terminal representing each electric power Internet of things system can select one relay node; a binary variable X can be definedD×R×K∈RD×R×KIf and only if θd，k＝ξd，rWhen 1, xd，r，k1, otherwise xd，r，k0, that is, each channel cannot be simultaneously multiplexed by two or more terminals of the electric power internet of things system, and each relay node can only be selected as a relay node by at most one terminal of the electric power internet of things system; thereby obtaining formula (8);
determining an optimal terminal-channel-relay combination from all terminal-channel-relay combinations according to the formula (7) and the formula (8), and performing a solving process of an intelligent genetic algorithm, wherein firstly, a certain number of candidate solutions are generated randomly and abstractly expressed as chromosomes, so that the population is evolved towards a better solution, and the solution is expressed by binary; evolution starts with a population of completely random individuals, followed by one generation; the fitness of the whole population is evaluated in each generation, a number of individuals are randomly selected from the current population, a new life population is generated by natural selection and mutation, and the population becomes the current population in the next iteration of the algorithm.
2. The resource allocation method of the power internet of things system according to claim 1, wherein the determining the energy and power constraints of the power internet of things system according to the receiving and transmitting signal-to-noise ratios of the terminals in the power internet of things system comprises:
determining the signal-to-noise ratio received by a relay and a base station in the electric power Internet of things system according to the receiving and transmitting signal-to-noise ratio of a terminal in the electric power Internet of things system, wherein the signal-to-noise ratio received by the relay and the base station is greater than a signal-to-noise ratio threshold;
and determining the constraint condition of the terminal transmitting power in the power Internet of things system according to the signal-to-noise ratios received by the relay and the base station.
3. The resource allocation method of the power internet of things system according to claim 2, wherein the calculating the optimal transmission power according to the Dinkelbach method model and the energy and power constraint condition comprises:
determining a target function of the transmitting power according to the constraint condition of the transmitting power of the terminal;
and solving the objective function of the transmitting power according to a Dinkelbach method model to obtain the optimal transmitting power.
4. The method for allocating resources in the power internet of things system according to claim 1, wherein the determining an optimal terminal-channel-relay combination from all the terminal-channel-relay combinations according to a genetic algorithm model and the optimal transmit power comprises:
determining constraint conditions of the terminal-channel-relay combination according to connection limitations of the terminal and the relay in all the terminal-channel-relay combinations and limitations of a channel used by the terminal;
and determining the optimal terminal-channel-relay combination according to the constraint conditions of the terminal-channel-relay combination and all the terminal-channel-relay combinations.
5. The resource allocation method of the power internet of things system according to claim 4, wherein the constraint conditions of the terminal-channel-relay combination include: each terminal is connected with a channel and each terminal is connected with a relay.
6. A resource allocation device of an electric power Internet of things system is characterized by comprising:
the constraint condition determining module is used for determining energy and power constraint conditions of the electric power Internet of things system according to the receiving and transmitting signal-to-noise ratios of the terminals in the electric power Internet of things system; the signal-to-noise ratio of the relay and the base station in the electric power internet of things system, which is obtained according to the signal-to-noise ratio of the terminal receiving and transmitting signals in the electric power internet of things system, is required to be greater than or equal to a signal-to-noise ratio threshold, and is expressed by the following formula (1):
wherein, γthWhich represents a signal-to-noise ratio threshold,
representing the signal-to-noise ratio transmitted by the terminal to the relay over the channel,
representing the signal-to-noise ratio, gamma, of the terminal transmitted to the base station via the channel and the relayr，bRepresents the signal-to-noise ratio of the relay transmission to the base station;
the constraint condition of the terminal transmitting power can be expressed by formula (2) to formula (5):
wherein, PdDenotes the transmit power of the terminal, d, r, k denote the numbers of the terminal, relay and channel, respectively, hd，BFor the channel coefficients of the terminal to the base station,
for the channel coefficients of the terminal to the relay,
for the channel coefficient from ordinary user to relay, σ2Representing white noise power; pLRepresenting a lower power limit, and expressed by formula (3), wherein the specific meaning of formula (3) is that both a signal sent by a terminal to a relay and a signal sent by the relay to a base station need to satisfy a signal-to-noise ratio condition; pURepresenting the upper power limit, expressed by formula (4), the concrete meaning of formula (4) is the upper power limit P of the terminal transmitting powerUNot exceeding the transmission power P of the ordinary usersnRepresents; formula (2) shows that for any d, r and k, the transmitting power of the terminal is limited within the range of the upper power limit and the lower power limit;
the constraint of energy is expressed by equation (6):
wherein EE represents a variable representing energy efficiency, and the formula represents an energy constraint condition under the condition of maximizing the energy efficiency;
the optimal transmitting power calculation module is used for calculating the optimal transmitting power according to the Dinkelbach method model and the energy and power constraint conditions; solving equations (3) to (5) according to the Dinkelbach method model, and calculating the optimal transmitting power comprises:
s1: initializing, selecting any initial solution p0Setting t equal to 1, q1＝φ(p0)/ψ(p0) (ii) a Wherein phi (p)0)＝log2(1+γd，r，b)，ψ(p0)＝(Pd+Pn+2P0)/Bk；p0Denotes the initial power, PdIs the terminal transmit power, PnIndicating the transmission power, P, of the ordinary user0A mute power; b iskIs the bandwidth;
s2: solving for g (p) ═ max { phi (p) -qkPsi (p) } to obtain an optimal solution pt；
ptExpressing the optimal solution of the power, namely the optimal transmitting power;
s3: if h (q)t)-h(qt-1) E, stopping; otherwise, return to step S2;
e represents a minimum value;
the combination determining module is used for determining all terminal-channel-relay combinations in the electric power Internet of things system according to the optimal transmitting power; all terminal-channel-relay combinations are obtained with the goal of maximizing system energy efficiency;
an optimal combination determining module, configured to determine an optimal terminal-channel-relay combination from all the terminal-channel-relay combinations according to a genetic algorithm model and the optimal transmit power; the constraint condition of the terminal-channel-relay combination is formulated according to the connection limit of the terminal and the relay and the limit of the terminal using the channel, and is expressed by formula (7) and formula (8):
wherein D, K, R also represents terminals, channels and relays, specifically the total amount of terminals, channels and relays, and formula (7) represents that all terminal-channel-relay combinations need to satisfy the maximum system total energy efficiency; formula (8) shows that the connection limit of the terminal and the relay and the limit condition of the terminal using the channel are interpreted as that each terminal is connected with one channel and each terminal is connected with one relay respectively on the premise that all the combinations of the terminal, the channel and the relay meet the maximum total energy efficiency of the system;
the formula (8) is obtained from the following formula (9) and formula (10),
wherein the binary variable thetad，kThe method can be defined that the channel of the terminal in the electric power Internet of things system can be reused by at most one terminal in the electric power Internet of things system, and the binary variable xid，rA terminal representing each electric power Internet of things system can select one relay node; a binary variable X can be definedD×R×K∈RD×R×KIf and only if θd，k＝ξd，rWhen 1, xd，r，k1, otherwise xd，r，k0, that is, each channel cannot be simultaneously multiplexed by two or more terminals of the electric power internet of things system, and each relay node can only be selected as a relay node by at most one terminal of the electric power internet of things system; thereby obtaining formula (8);
determining an optimal terminal-channel-relay combination from all terminal-channel-relay combinations according to the formula (7) and the formula (8), and performing a solving process of an intelligent genetic algorithm, wherein firstly, a certain number of candidate solutions are generated randomly and abstractly expressed as chromosomes, so that the population is evolved towards a better solution, and the solution is expressed by binary; evolution starts with a population of completely random individuals, followed by one generation; the fitness of the whole population is evaluated in each generation, a number of individuals are randomly selected from the current population, a new life population is generated by natural selection and mutation, and the population becomes the current population in the next iteration of the algorithm.
7. The resource allocation device of the power internet of things system according to claim 6, wherein the constraint condition determination module comprises:
the signal-to-noise ratio determining module is used for determining the signal-to-noise ratio received by a relay and a base station in the electric power Internet of things system according to the receiving and transmitting signal-to-noise ratio of the terminal in the electric power Internet of things system, wherein the signal-to-noise ratio received by the relay and the base station is greater than a signal-to-noise ratio threshold;
and the transmission power determining module is used for determining the constraint condition of the terminal transmission power in the power Internet of things system according to the signal-to-noise ratio received by the relay and the base station.
8. The resource allocation device of the power internet of things system according to claim 7, wherein the optimal transmission power calculation module comprises:
the target function determining module is used for determining a target function of the transmitting power according to the constraint condition of the transmitting power of the terminal;
and the optimal transmitting power calculation submodule is used for solving a target function of the transmitting power according to a Dinkelbach method model to obtain the optimal transmitting power.
9. The resource allocation device of the power internet of things system according to claim 6, wherein the optimal combination determination module comprises:
a combination constraint condition determining module, configured to determine constraint conditions of the terminal-channel-relay combination according to connection limitations of the terminal and the relay in all the terminal-channel-relay combinations and limitations of a channel used by the terminal;
and the optimal combination determining submodule is used for determining the optimal terminal-channel-relay combination according to the constraint condition of the terminal-channel-relay combination.
10. A computer-readable storage medium storing computer instructions for causing a computer to execute the resource allocation method of the power internet of things system according to any one of claims 1 to 5.
11. A resource allocation device of an electric power Internet of things system is characterized by comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the resource allocation method of the power internet of things system according to any one of claims 1 to 5.
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