CN109195222B - Power distribution method based on statistical characteristic reference - Google Patents
Power distribution method based on statistical characteristic reference Download PDFInfo
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Abstract
The embodiment of the invention provides a power distribution method based on statistical characteristic reference, which comprises the following steps: constructing a power distribution optimization model according to the average signal-to-interference-and-noise ratio and the average transmission rate of each user in the ultra-dense network and the power level ratio corresponding to the transmission power used by the user; the power distribution optimization model is composed of a sum maximization function of average transmission rates and a constraint condition; and solving the power distribution optimization model to obtain a power distribution optimization scheme, and performing power distribution according to the power distribution optimization scheme. The method provided by the embodiment of the invention constructs the power distribution optimization model based on the random geometric theory, solves the problem of overlarge resource distribution calculation amount caused by the sharp increase of the number of network nodes, reduces the system measurement and feedback overhead, realizes high-efficiency power distribution, and improves the system performance and the resource utilization efficiency.
Description
Technical Field
The embodiment of the invention relates to the technical field of communication, in particular to a power distribution method based on statistical characteristic reference.
Background
In recent years, the number of internet users has increased, and various new services such as high-definition videos, cloud computing, edge computing, online games, touch communication, machine communication and the like have been emerging, so that higher requirements are made on the capacity, speed and reliability of a network. The fifth generation mobile communication technology (5G) utilizes a novel physical layer communication technology, a network virtualization technology and a multi-network convergence mode, can provide a flexible customized access service for a user at a higher access rate (up to 10Gbps) and with lower network energy consumption, and becomes a hotspot of current research.
The fifth generation mobile communication network introduces a millimeter wave communication technology without registration in the small cell base station, thereby realizing high-speed data transmission and avoiding co-channel signal interference with the macro base station. Meanwhile, the communication service of the macro base station is unloaded to the small cell base station, so that the original macro base station is responsible for expanding the network coverage and controlling the small cell base station, the signal transmission rate can be greatly improved, and the network energy consumption can be reduced. However, the transmission distance of millimeter wave communication is limited due to oxygen adsorption, and a large number of cell base stations need to be deployed to ensure complete network coverage, i.e., Ultra Dense network Deployment (UDN).
In the current world of everything interconnection, the ultra-dense network deployment greatly solves the requirement of users on network data traffic. However, with the increase of cell density, the number of network nodes increases sharply due to ultra-dense network deployment of cells, which brings great challenges to calculation and control in resource allocation and reduces network performance and user experience. Most of the traditional resource allocation methods are deterministic, real-time feedback and centralized, so that the feedback quantity, the calculation dimension and the calculation complexity in the control calculation process are high. How to reduce the calculation amount of power allocation is an urgent issue to be solved in the industry at present.
Disclosure of Invention
The embodiment of the invention provides a power distribution method based on statistical characteristic reference, which is used for solving the problem of overlarge resource distribution calculation amount caused by the sharp increase of the number of network nodes in the prior art and realizing simple and efficient power distribution.
In one aspect, an embodiment of the present invention provides a power allocation method, including: constructing a power distribution optimization model according to the average signal-to-interference-and-noise ratio of each user in the ultra-dense network, the average transmission rate and the user ratio of the power grade corresponding to the transmission power; the power distribution optimization model is composed of a sum maximization function of average transmission rates and a constraint condition; and solving the power distribution optimization model to obtain a power distribution optimization scheme, and performing power distribution according to the power distribution optimization scheme.
In another aspect, an embodiment of the present invention provides a power distribution apparatus, which includes a processor, a communication interface, a memory, and a bus, where the processor and the communication interface communicate with each other through the bus, and the processor may call a logic instruction in the memory to execute the power distribution method described above.
The power distribution method based on the statistical characteristic reference provided by the embodiment of the invention constructs the power distribution optimization model based on the random geometric theory, solves the problem of overlarge resource distribution calculation amount caused by the sharp increase of the number of network nodes, reduces the system measurement and feedback overhead, realizes efficient power distribution, and improves the system performance and the resource utilization efficiency.
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 those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a flow chart of a power allocation method according to an embodiment of the present invention;
FIG. 2 is a flow chart of a genetic algorithm according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating a communication system scenario in an ultra-dense network according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a power distribution apparatus according to an embodiment of the present 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.
Fig. 1 is a flowchart of a power allocation method according to an embodiment of the present invention, and as shown in fig. 1, a power allocation method includes:
s1, constructing a power distribution optimization model according to the average signal-to-interference-and-noise ratio of each user in the ultra-dense network, the average transmission rate and the user ratio of the power level corresponding to the transmission power; the power allocation optimization model is composed of a sum of average transmission rates maximization function and a constraint condition.
Specifically, to implement the power allocation method of the ultra-dense network, a power allocation optimization model is first established. In the embodiment of the invention, the power distribution optimization model aims at maximizing the sum of the average transmission rates of all users in the ultra-dense network.
Further, a power class is preset for the ultra-dense network. In the ultra-dense network, the transmission power of each user corresponds to a unique preset power level. And acquiring the user ratio corresponding to each power grade in the ultra-dense network according to the power grades of different user transmission powers.
And constructing a maximization function of the sum of the average transmission rates of each user according to the average transmission rate of each user in the ultra-dense network, namely constructing a maximization function of the sum of the average transmission rates, and simultaneously constructing a corresponding constraint condition by applying the average signal-to-interference-and-noise ratio of each user and the user ratio of the power level corresponding to the transmission power. And combining the sum of the average transmission rates with a maximization function and a constraint condition to construct a power distribution optimization model.
And S2, solving the power distribution optimization model, obtaining a power distribution optimization scheme, and performing power distribution according to the power distribution optimization scheme.
Specifically, the power allocation optimization model is solved to obtain a maximum power allocation scheme, i.e., a power allocation optimization scheme, of the sum of the average transmission rates of each user in the ultra-dense network. And finally, performing power distribution on the ultra-dense network according to the power distribution optimization scheme.
In the embodiment of the invention, the power distribution optimization model is constructed based on the random geometric theory, so that the problem of overlarge resource distribution calculation amount caused by the sharp increase of the number of network nodes is solved, the system measurement and feedback overhead is reduced, the efficient power distribution is realized, and the system performance and the resource utilization efficiency are improved.
Based on the above embodiment, before constructing a power allocation optimization model according to an average signal-to-interference-and-noise ratio of each user in an ultra-dense network, an average transmission rate, and a user ratio of a power class corresponding to a transmission power, a power allocation method further includes: calculating the signal-to-interference-and-noise ratio of each user according to the transmission power of each user in the ultra-dense network, a preset power level and a user ratio corresponding to each power level; and based on a random geometric theory, acquiring the average signal-to-interference-and-noise ratio and the average transmission rate of each user according to the signal-to-interference-and-noise ratio of each user.
Specifically, first, a signal to interference plus noise ratio of each user in the ultra-dense network is obtained according to a transmission power of each user in the ultra-dense network, a preset power level and a user ratio corresponding to each power level. The Signal to Interference plus Noise Ratio (SINR) is a Ratio of the received strength of a useful Signal to the received strength of an Interference Signal (Noise and Interference), and is commonly used for multi-user detection.
And then, based on a random geometric theory, carrying out statistical average on the signal to interference plus noise ratio of each user to obtain the average signal to interference plus noise ratio of each user.
Meanwhile, based on the random geometry theory, the average transmission rate of each user is obtained according to the signal-to-interference-and-noise ratio of each user.
Wherein the stochastic geometry theory is a mathematical technique that analyzes spatial attributes of the study object using a probabilistic approach. The application trend of the stochastic geometry theory is the research on the average performance index of a large-scale wireless network with probability distribution of spatial positions.
The embodiment of the invention provides a method for acquiring the average signal-to-interference-and-noise ratio and the average transmission rate, and provides data support for establishing a power distribution optimization model.
Based on any of the above embodiments, a power allocation method for calculating a signal-to-interference-and-noise ratio of each user according to a transmission power of each user in an ultra-dense network, a preset power level, and a user ratio corresponding to each power level, further includes: the signal to interference plus noise ratio of each user is calculated according to the following formula:
in the formula, SINRiFor user UiSignal to interference and noise ratio of (P)iFor user UiTransmission power of hiFor user UiThe rayleigh small-scale fading parameters of (1),
for user UiLarge scale fading parameter of riFor user UiDistance from its serving base station, α being a channel loss parameter, σ2Is Gaussian white noise, PkFor a power corresponding to a predetermined power level k, phiukIs the set of users of power class k,
for all user information.
Specifically, large scale fading and small scale fading are considered when calculating the signal to interference plus noise ratio of the user. Where large-scale fading is caused by shadows of fixed obstacles (buildings, hills, forests, etc.) on the path of a mobile communication channel, the attenuation characteristics generally follow the d "law, and average signal fading and variations with respect to average fading are characterized by a lognormal distribution. Small-scale fading refers to fading in the short term, and in particular, to rapid fluctuation of the received signal in the short term when the mobile station moves a small distance. Small scale fading is determined by many factors such as multipath propagation, mobile station velocity, velocity of surrounding objects, and signal transmission bandwidth.
The embodiment of the invention provides a method for calculating the signal-to-interference-and-noise ratio (SINR) containing loss information such as large-scale fading and small-scale fading, which is beneficial to realizing high-precision SINR calculation.
Based on any of the above embodiments, a power allocation method, where the method obtains an average signal-to-interference-and-noise ratio and an average transmission rate of each user according to the signal-to-interference-and-noise ratio of each user based on a random geometry theory, further includes: based on a random geometric theory, obtaining the average signal-to-interference-and-noise ratio of each user according to the following formula:
in the formula, SSINRiFor user UiAverage signal to interference plus noise ratio, σ2Is Gaussian white noise, PiFor user UiTransmission power of riFor user UiDistance from its serving base station, α is the channel loss parameter, K is the number of power classes, ηkFor the user ratio, λ, corresponding to the power level kuIs the poisson distribution parameter of the base station.
Based on the random geometry theory, the average transmission rate of each user is obtained according to the following formula:
in the formula, SRiFor user UiN is the number of users, PiFor user UiTransmission power of riFor user UiThe distance from its serving base station, alpha is the channel loss parameter,
for user UiLarge scale fading parameter, σ2Is Gaussian white noise, K is the number of power levels, ηkFor the user ratio, λ, corresponding to the power level kuIs a Poisson distribution parameter, P, of a base stationkIs the transmission power corresponding to the preset power level k.
Specifically, in the ultra-dense network, the distribution of users and base stations is independent, and the user distribution obeying parameter is λaIn the poisson point process, the base station distribution obedience parameter is lambdauAnd assuming that each user accesses the closest base station.
Among them, Poisson Point Processes (PPPs) are theoretical analysis models mainly used for analyzing a problem concerning the geometric distribution of randomly occurring points in a multidimensional space. Preferably, the Poisson process is a homogeneous Poisson process (HPPP), which may be a common update process with the same exponential distribution of event intervals, or a pure process with a constant rate of generation.
Based on any of the above embodiments, a power allocation method, where a power allocation optimization model is constructed according to an average signal-to-interference-and-noise ratio of each user in an ultra-dense network, an average transmission rate, and a user ratio of a power class corresponding to transmission power, further includes: from the average transmission rate of each user, a sum of average transmission rates maximization function is constructed as follows:
in the formula, PiFor user UiTransmission power of etakFor the user ratio, SR, corresponding to the power level kiFor user UiN is the number of users.
In particular, the amount of the solvent to be used,
the sum of the average transmission rates of all users in the ultra-high density network, arg max is used to obtain
The transmission power of each user having the maximum value and the user ratio corresponding to the power level of the transmission power, i.e., (P)i,ηk). The above make
Taking the maximum value (P)i,ηk) I.e. a power allocation optimization scheme.
According to the average signal-to-interference-and-noise ratio of the received signal of each user, a first constraint condition is constructed as follows:
in the formula, SSINRiFor user UiAverage signal to interference plus noise ratio, tau, of the received signaliFor user UiIs detected by the first predetermined threshold.
Specifically, the first constraint condition is a constraint aiming at the average signal-to-interference-and-noise ratio of each user in the ultra-dense network, and guarantees the quality of information received by the users.
According to the user ratio corresponding to the power level, a second constraint condition is constructed as follows:
η1+η2+…+ηK＝1
where K is the power level.
Specifically, the second constraint condition is used to indicate that the sum of the user ratios of the K power classes is 1, that is, all users can select only one transmission power of the K power classes.
According to the relationship between the power of the user received signal and the user ratio corresponding to the power level, a third constraint condition is constructed as follows:
wherein, f (P)i)＝||Pi-Pk||0；
In the formula, | | | non-conducting phosphor0Is L0 norm, PkIs the power corresponding to power level k.
In particular, the L0 norm is used to represent the number of all non-zero elements in a vector, which is typically used for sparse coding and feature selection in machine learning.
For example, when the transmission power PiAt a power level of k, i.e. Pi-PkWhen equal to 0, f (P)i) 0; when transmission power PiWhen the power level of (1) is not k, i.e. Pi-PkNot equal to 0, f (P)i)＝1。
Assuming that the power level corresponding to the transmission power of m users in the N users is k, ηk＝m/N。
Based on any of the above embodiments, a power allocation method, where solving the power allocation optimization model to obtain a power allocation optimization scheme, further includes: and solving the power distribution optimization model based on a genetic algorithm, taking a power distribution scheme with the maximum sum of the average transmission rates of each user as a power distribution optimization scheme, and carrying out power distribution according to the power distribution optimization scheme.
Specifically, after the power distribution optimization model is constructed, the genetic algorithm is applied to solve the power distribution optimization model constructed in the previous step. The Genetic Algorithm (Genetic Algorithm) is a calculation model of a biological evolution process simulating natural selection and Genetic mechanism of Darwinian biological evolution theory, and is a method for searching an optimal solution by simulating the natural evolution process.
The genetic algorithm is applied to the solution of the power distribution optimization model, and the maximum power distribution scheme of the sum of the average transmission rates of each user in the ultra-dense network, namely the power distribution optimization scheme, can be obtained. And finally, performing power distribution of the ultra-dense network according to the power distribution optimization scheme.
In the embodiment of the invention, the power distribution optimization model is solved based on the genetic algorithm, so that the problem that the traditional iterative algorithm is easy to fall into local minimum and the iteration cannot be carried out is avoided, the calculation is simple, and the expansibility is strong.
Based on any of the above embodiments, fig. 2 is a flowchart of a genetic algorithm according to an embodiment of the present invention, and as shown in fig. 2, a power allocation method, where the power allocation optimization model is solved based on the genetic algorithm, a power allocation scheme with a maximum sum of average transmission rates of each user is used as a power allocation optimization scheme, and power allocation is performed according to the power allocation optimization scheme, further includes: randomly generating a population based on the power distribution optimization model; said population consisting of a preset number of individuals, each of said individuals representing a power allocation scheme; calculating the fitness of each individual; the fitness of each individual is the sum of the average transmission rates of all users in each individual; based on the fitness of each individual, selecting, crossing and mutating the population, and selecting the individual with the highest fitness as a newborn individual; calculating a user ratio corresponding to the power level of the transmission power of each user in the new individual; generating a population consisting of the preset number of individuals according to a user ratio corresponding to the power level of the transmission power of each user in the newborn individuals, and recalculating the fitness of each individual in the population until the times of generating the population are greater than the preset times; and taking the newborn individual as a power distribution optimization scheme, and carrying out power distribution according to the power distribution optimization scheme.
Specifically, solving the power allocation optimization model based on a genetic algorithm further comprises:
firstly, based on the constraint conditions in the power distribution optimization model, a preset number of power distribution schemes are randomly generated, each power distribution scheme is an individual, and the preset number of individuals form an initial population.
Secondly, each individual in the population represents a user power allocation under the ultra-density network. And respectively calculating the sum of the average transmission rates SR of all the users in each individual, and taking the sum of the average transmission rates SR of all the users in each individual as the fitness of each individual.
Then, the population generated in the above steps and the fitness of each individual in the population are selected, crossed and mutated.
Wherein selecting selects a number of individuals from the population to generate the next generation. One commonly used selection strategy is proportional selection, i.e., the probability that an individual is selected is proportional to its fitness function value. And crossing, namely exchanging partial information among individuals, and constructing a new individual of the next generation. Mutation means that information in newly generated individuals can be mistaken with a certain probability in the propagation process.
And generating new individuals through the selection, crossing and mutation, and selecting the individuals with the highest fitness from the new individuals and the individuals in the population as new individuals.
And then, acquiring the user ratio of the new individuals, generating a next generation population according to the user ratio, recalculating the fitness of each individual in the next generation population, and repeating the steps for iteration.
And if the iteration times are more than the preset times, stopping the iteration, and taking the new individual as a power distribution optimization scheme. And finally, performing power distribution of the ultra-dense network according to the power distribution optimization scheme.
The embodiment of the invention specifically provides a step of solving the power distribution optimization model by a genetic algorithm, and is beneficial to realizing accurate and efficient power distribution.
Based on any one of the embodiments above, a power allocation method, before calculating the fitness of each individual, further includes: calculating the signal-to-interference-and-noise ratio of each user in any one individual; and if the signal-to-interference-and-noise ratio of any user in any individual is smaller than a second preset threshold value, regenerating the any individual.
Specifically, the first constraint in the power allocation optimization model guarantees the communication quality of each user from the perspective of each user. On this basis, in the solving process of the genetic algorithm, before the fitness calculation is carried out on each individual in the population, the signal to interference plus noise ratio (SINR) of each user in each individual is also calculated, and an SINR threshold value, namely a second preset threshold value is set for the whole user.
And if the SINR of any user in any individual is smaller than a second preset threshold, considering that the any individual does not meet the allocation condition, and needing to regenerate the any individual.
The embodiment of the invention further ensures the communication quality of the users by setting the threshold value for the signal-to-interference-and-noise ratio of the users in the individual.
Based on any one of the embodiments, a power allocation method selects, crosses, and mutates the population based on the fitness of each individual, and selects an individual with the highest fitness as a new individual, further comprising: based on the fitness of each individual, selecting the preset number of individuals as a first population according to a roulette algorithm; exchanging the first population based on a k-opt exchange algorithm to obtain a second population; mutating the second population to obtain a third population; and selecting the individual with the highest fitness from the population and the third population as a newborn individual.
Specifically, first, a roulette algorithm is applied to obtain a first population based on the fitness of each individual in the population. The roulette algorithm is also called a proportion selection operator, and the basic idea is that the probability of each individual being selected is proportional to the fitness of the individual. Further:
the expected probability of reproduction for each individual was calculated according to the following formula:
in the formula, P (x)i) Is an individual xiExpected probability of reproduction, F (x)i) Is an individual xiM is the preset number, xiIs the ith individual of the population.
And randomly selecting M individuals as a first population according to the expected reproduction probability of each individual.
Secondly, performing cross operation on the first population by using k-opt exchange: and randomly selecting a k point as a cross point, keeping the information of each individual before the k point unchanged, and after the k point, exchanging the information between adjacent individuals, thereby regenerating the second population.
And then, based on the second population, carrying out mutation operation to obtain a third population.
And finally, selecting the individual with the highest fitness from the population and the third population as a newborn individual.
In order to better understand and apply the power allocation method proposed by the present invention, the following examples are made, and the present invention is not limited to the following examples.
The present example is an uplink OFDMA-based ultra-dense network. FIG. 3 is a schematic view of a communication system scenario in an ultra-dense network according to an embodiment of the present invention, as shown in FIG. 3, the distribution of users and base stations are independent of each other and respectively obey a parameter λaAnd λuThe poisson point process of (a).
It is assumed that for each user access is made to the base station closest to it and its transmission power level is selected from the set
Wherein P isk＝(Pmax-Pmin) K/K, K ═ 1,2, …, K. Will select P1,P2,…,PKThe user ratio of the power levels is denoted as eta1,η2,...,ηKIt is clear that: eta1+η2+...+ηK1. The users are divided into independent K groups according to different power levels selected by the users, and the groups are respectively independent and obey the parameter eta according to the sparse theorem of HPPP1λu,η2λu,...,ηKλuThe poisson point process of (a). For any user UiLarge scale fading of
Wherein r isiFor user UiThe distance from its serving base station, α, is the channel loss parameter. At the same time, the user obedience parameter is hiRayleigh small-scale fading, assuming white Gaussian noise as σ2For user UiThe signal-to-interference-and-noise ratio SINR is as follows:
in the formula, SINRiFor user UiSignal to interference and noise ratio of (P)iFor user UiTransmission power of phiukIs the set of users of power class k,
for all user information.
Based on random geometric theory, for user UiThe signal to interference plus noise ratio is counted and averaged to obtain a user UiAverage signal to interference and noise ratio (SSINR)i：
Wherein, the user UiReceive a signal of
The received interference is expressed as
Based on random geometric theory, for user UiThe transmission rate of the user is counted and averaged to obtain the user UiAverage transmission rate SRi：
In the formula, N is the number of users.
And performing power distribution based on the transmission model:
step 101, establishing a power distribution optimization model:
the constraint conditions comprise:
the first constraint condition is:
the second constraint condition is as follows: eta1+η2+...+ηK＝1
The third constraint condition is as follows:
wherein, f (P)i)＝||Pi-Pk||0；
In the formula, τiFor user UiIs a first preset threshold, | | | | | non-calculation0Is the norm of L0.
Based on the power distribution optimization model, solving by applying a genetic algorithm, and taking the power distribution scheme with the maximum sum of the average transmission rates of each user as the power distribution optimization scheme:
step 102: the parameters required for generating the population are input.
First, parameters for initializing the population are input: the population size, i.e. the predetermined number M is 20, the number of times T, i.e. the predetermined number of generations of termination evolution of genetic operations, is 2000, and the crossover probability pcThe variation probability pm is 0.001, 0.6.
Defining a fitness function as the sum of the average transmission rates of all users in an individual, the input being used to generate a sum of usersParameter lambda of the geographical position of the base stationaAnd λuThe number of users is N, the power level K is 8, and the SINR threshold, i.e., the second preset threshold, is a.
Step 103: 20 individuals were randomly generated. One for each bank.
In this step, a 20 × N matrix is randomly generated according to a preset power level, and decimal numbers in the matrix are quantized to generate a binary 20 × 3N matrix corresponding to the decimal numbers.
And calculating the SINR of each user in the population, and if the SINR of any user is smaller than a second preset threshold value a, regenerating an individual corresponding to the user.
Step 104: the fitness of each individual is calculated. The sum of the average transmission rates SR of all users in each individual is the fitness of each individual.
Step 105: selection (selection)
From the binary matrix generated in step 103 and the fitness calculated in step 104, the probability that each individual is selected, i.e. the expected reproduction probability, is calculated using the roulette algorithm:
in the formula, P (x)i) Is an individual xiExpected probability of reproduction, F (x)i) Is an individual xiM is the preset number, xiIs the ith individual of the population.
Through the selection step, individuals with higher fitness are left, and the binary matrix of 20 x 3N is regenerated.
Step 106: cross (crossover)
The binary matrix generated in step 105 is interleaved with k-opt switches: randomly selecting a k point as a cross point, wherein the binary codes before the k point are unchanged, and after the k point, the binary codes between adjacent individuals are exchanged, and a 20-by-3N binary matrix is regenerated through the steps.
Step 107: mutation (mutation)
Performing a mutation operation according to the new 20 × 3N binary matrix generated in step 106: in a 20 x 3N binary matrix, each binary code has pmA probabilistic mutation of 0.001. Through this step, a binary matrix of 20 x 3N is regenerated. The operation of step 104 is repeated. And comparing the newly generated fitness matrix with the fitness generated in the step 104, and selecting the individual with the highest fitness as a new individual.
Step 108: calculating a user ratio η of the newborn subject1,η2,...,ηKJudging whether the preset times T are exceeded or not, and if not, judging according to the user ratio eta1,η2,...,ηKRegenerating 20 individuals, and repeating the step 104; and if the preset times T are exceeded, stopping the algorithm.
And taking the new individuals as power distribution optimization schemes, wherein the new individuals are the power distribution schemes which have the maximum sum of the average transmission rates of the users and meet the SINR indexes of the users in resource scheduling. And finally, performing power distribution of the ultra-dense network according to the power distribution optimization scheme.
In the example, the power distribution optimization model is constructed based on the random geometric theory, so that the problem of overlarge resource distribution calculation amount caused by the sharp increase of the number of network nodes is solved, the system measurement and feedback overhead is reduced, the efficient power distribution is realized, and the system performance and the resource utilization efficiency are improved.
Fig. 4 is a schematic structural diagram of a power distribution apparatus according to an embodiment of the present invention, and as shown in fig. 4, the power distribution apparatus includes: a processor (processor)401, a communication Interface (communication Interface)402, a memory (memory)403 and a bus 404, wherein the processor 401, the communication Interface 402 and the memory 403 complete communication with each other through the bus 404. Processor 401 may call logic instructions in memory 403 to perform the following method: constructing a power distribution optimization model according to the average signal-to-interference-and-noise ratio of each user in the ultra-dense network, the average transmission rate and the user ratio of the power grade corresponding to the transmission power; the power distribution optimization model is composed of a sum maximization function of average transmission rates and a constraint condition; and solving the power distribution optimization model to obtain a power distribution optimization scheme, and performing power distribution according to the power distribution optimization scheme.
An embodiment of the present invention discloses a computer program product, which includes a computer program stored on a non-transitory computer readable storage medium, the computer program including program instructions, when the program instructions are executed by a computer, the computer can execute the methods provided by the above method embodiments, for example, the method includes: constructing a power distribution optimization model according to the average signal-to-interference-and-noise ratio of each user in the ultra-dense network, the average transmission rate and the user ratio of the power grade corresponding to the transmission power; the power distribution optimization model is composed of a sum maximization function of average transmission rates and a constraint condition; and solving the power distribution optimization model to obtain a power distribution optimization scheme, and performing power distribution according to the power distribution optimization scheme.
The present embodiments provide a non-transitory computer-readable storage medium storing computer instructions that cause the computer to perform the methods provided by the above method embodiments, for example, including: constructing a power distribution optimization model according to the average signal-to-interference-and-noise ratio of each user in the ultra-dense network, the average transmission rate and the user ratio of the power grade corresponding to the transmission power; the power distribution optimization model is composed of a sum maximization function of average transmission rates and a constraint condition; and solving the power distribution optimization model to obtain a power distribution optimization scheme, and performing power distribution according to the power distribution optimization scheme.
Those of ordinary skill in the art will understand that: all or part of the steps for implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer readable storage medium, and when executed, the program performs the steps including the method embodiments; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
The above-described embodiments of the test equipment and the like of the display device are merely illustrative, wherein the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the embodiments of the present invention, and are not limited thereto; although embodiments of the present invention have been described in detail with reference to the foregoing embodiments, those skilled in the art will understand that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (9)
1. A method of power allocation, comprising:
constructing a power distribution optimization model according to the average signal-to-interference-and-noise ratio of each user in the ultra-dense network, the average transmission rate and the user ratio of the power grade corresponding to the transmission power; the power distribution optimization model is composed of a sum maximization function of average transmission rates and a constraint condition;
solving the power distribution optimization model to obtain a power distribution optimization scheme, and performing power distribution according to the power distribution optimization scheme;
the constructing a power distribution optimization model according to the average signal-to-interference-and-noise ratio of each user in the ultra-dense network, the average transmission rate and the user ratio of the power level corresponding to the transmission power further comprises:
from the average transmission rate of each user, a sum of average transmission rates maximization function is constructed as follows:
in the formula, PiFor user UiTransmission power of etakFor the user ratio, SR, corresponding to the power level kiFor user UiN is the number of users;
according to the average signal-to-interference-and-noise ratio of the received signal of each user, a first constraint condition is constructed as follows:
in the formula, SSINRiFor user UiAverage signal to interference plus noise ratio, tau, of the received signaliFor user UiA first preset threshold value of (a);
according to the user ratio corresponding to the power level, a second constraint condition is constructed as follows:
η1+η2+...+ηK＝1
where K is the number of power levels, ηkThe user ratio corresponding to the power level k;
according to the relationship between the power of the user received signal and the user ratio corresponding to the power level, a third constraint condition is constructed as follows:
wherein, f (P)i)＝||Pi-Pk||0；
In the formula, | | | non-conducting phosphor0Is L0 norm, PkIs the power corresponding to power level k.
2. The method of claim 1, wherein before constructing the power allocation optimization model according to the user ratios of the power levels corresponding to the average signal-to-interference-and-noise ratio, the average transmission rate, and the transmission power of each user in the ultra-dense network, the method further comprises:
calculating the signal-to-interference-and-noise ratio of each user according to the transmission power of each user in the ultra-dense network, a preset power level and a user ratio corresponding to each power level;
and based on a random geometric theory, acquiring the average signal-to-interference-and-noise ratio and the average transmission rate of each user according to the signal-to-interference-and-noise ratio of each user.
3. The method according to claim 2, wherein the calculating the signal-to-interference-and-noise ratio of each user according to the transmission power of each user in the ultra-dense network, a preset power level and a user ratio corresponding to each power level further comprises:
the signal to interference plus noise ratio of each user is calculated according to the following formula:
in the formula, SINRiFor user UiSignal to interference plus noise ratio of hiFor user UiRayleigh small-scale fading parameter ri -αFor user UiLarge scale fading parameter of riFor user UiDistance from its serving base station, α being a channel loss parameter, σ2Is Gaussian white noise, phikIs the set of users of power class k,
for all user information.
4. The method according to claim 2, wherein the obtaining an average signal-to-interference-and-noise ratio and an average transmission rate of each user according to the signal-to-interference-and-noise ratio of each user based on a stochastic geometry theory further comprises:
based on a random geometric theory, obtaining the average signal-to-interference-and-noise ratio of each user according to the following formula:
in the formula, σ2Is white Gaussian noise, riFor user UiDistance from its serving base station, α being a channel loss parameter, λuIs a poisson distribution parameter of the base station;
based on the random geometry theory, the average transmission rate of each user is obtained according to the following formula:
5. the method of claim 1, wherein solving the power distribution optimization model to obtain a power distribution optimization scheme, and performing power distribution according to the power distribution optimization scheme further comprises:
and solving the power distribution optimization model based on a genetic algorithm, taking a power distribution scheme with the maximum sum of the average transmission rates of each user as a power distribution optimization scheme, and carrying out power distribution according to the power distribution optimization scheme.
6. The method of claim 5, wherein the solving the power allocation optimization model based on the genetic algorithm, using the power allocation scheme with the maximum sum of the average transmission rates of each user as the power allocation optimization scheme according to which the power allocation is performed, further comprises:
randomly generating a population based on the power distribution optimization model; the randomly generated population is composed of a preset number of individuals, each individual representing a power allocation scheme;
calculating the fitness of each individual; the fitness of each individual is the sum of the average transmission rates of all users in each individual;
based on the fitness of each individual, selecting, crossing and mutating the randomly generated population, and selecting the individual with the highest fitness as a new individual;
calculating a user ratio corresponding to the power level of the transmission power of each user in the new individual;
generating a population consisting of the preset number of individuals according to a user ratio corresponding to the power level of the transmission power of each user in the newly-generated individuals, and recalculating the fitness of each individual in the newly-generated population until the number of times of generating the population is greater than the preset number of times;
and taking the newborn individual as a power distribution optimization scheme, and carrying out power distribution according to the power distribution optimization scheme.
7. The method of claim 6, wherein before calculating the fitness of each of the individuals, further comprising:
calculating the signal-to-interference-and-noise ratio of each user in any one individual;
and if the signal-to-interference-and-noise ratio of any user in any individual is smaller than a second preset threshold value, regenerating the any individual.
8. The method of claim 6, wherein the randomly generated population is selected, crossed and mutated based on the fitness of each individual, and the individual with the highest fitness is selected as the newborn individual, further comprising:
based on the fitness of each individual, selecting the preset number of individuals as a first population according to a roulette algorithm;
exchanging the first population based on a k-opt exchange algorithm to obtain a second population;
mutating the second population to obtain a third population;
and selecting the individual with the highest fitness from the randomly generated population and the third population as a newborn individual.
9. A power distribution apparatus comprising a processor, a communication interface, a memory and a bus, wherein the processor, the communication interface and the memory communicate with each other via the bus, and the processor can call logic instructions in the memory to execute the power distribution method according to any one of claims 1 to 8.
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