CN115802380A - Resource allocation method and device for cognitive industry Internet of things in dynamic uncertain scene - Google Patents
Resource allocation method and device for cognitive industry Internet of things in dynamic uncertain scene Download PDFInfo
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Abstract
The application relates to a resource allocation method and device for a cognitive industry internet of things in a dynamic uncertain scene, which belong to the field of industrial internet of things communication, aiming at the problem of time delay guarantee of a cognitive industry internet of things service in the dynamic uncertain scene, a time delay model of the cognitive industry internet of things service is established based on a queuing theory, a rate analytic solution for guaranteeing time delay requirements is deduced, and a cognitive industry internet of things resource allocation model considering randomness of a dynamic uncertain environment is established based on the model; due to the introduction of random environment parameters, the model is difficult to obtain a global optimal solution by adopting a traditional optimization method; aiming at the problem, the method adopts the idea of robust optimization to convert the constraint of uncertain parameters into convex constraint, and provides a resource allocation strategy of the cognitive industry Internet of things.
Description
Technical Field
The application relates to the field of industrial Internet of things communication, in particular to a resource allocation method and device for a cognitive industrial Internet of things in a dynamic uncertain scene.
Background
With the expansion of the Internet of Things (IoT), industrial Internet of Things (IIoT) devices are growing exponentially, and the existing spectrum resources cannot meet the spectrum demand of the IIoT devices in the future. Cognitive Radio (CR) theory can share spectrum resources, and effectively improve resource utilization rate, so that a Cognitive industry Internet of Things (CIIoT) arises.
In the CIIoT network, how to improve the resource allocation efficiency is a key to alleviate the spectrum scarcity problem of Industrial Internet of Things devices (iiotds). The existing research rarely considers the randomness of the dynamic uncertain environment of the CIIoT and the problem of how to guarantee the delay requirement of the service. However, the actual communication environment between devices is fraught with dynamic uncertainties, including uncertainties in channel gain, uncertainties in background noise, and uncertainties in interference, among others. In an actual communication environment, on one hand, due to frequent changes of network user behaviors, a network electromagnetic environment has dynamic uncertainty. The dynamic uncertain environment causes service transmission delay to have certain randomness. On the other hand, in an industrial control network, the service has a high requirement on the sensitivity to the delay, and the delay of the service needs to be effectively controlled. Therefore, how to guarantee the delay requirement of the CIIoT service in a dynamic uncertain scene is a key problem to be solved urgently for realizing the cognitive industry Internet of things.
Disclosure of Invention
The application provides a resource allocation method and device for a cognitive industry internet of things in a dynamic uncertain scene, and the technical scheme of the application is as follows:
according to a first aspect of the embodiments of the present application, a resource allocation method for a cognitive industry internet of things in a dynamic uncertain scene is provided, including:
establishing a cognitive industrial Internet of things model under a dynamic uncertain scene, and establishing a cognitive industrial Internet of things time delay model based on a queuing model; the cognitive industry Internet of things model under the dynamic uncertain scene is built based on an authorized large-range macro cell area, each macro cell area comprises a plurality of mobile users and a macro base station, a plurality of micro cell area industry Internet of things are built in the macro cell area, and each micro cell area industry Internet of things comprises a plurality of Internet of things devices and a micro base station;
determining a rate analytic solution based on guaranteed delay requirements so that the correlation multiple of the allocation factors on each subcarrier and the channel gain of the mobile user does not exceed an interference threshold on the subcarrier;
establishing a cognitive industry internet of things resource allocation model based on the randomness of a dynamic uncertain environment, combining a base station transmitting power constraint, an interference constraint between equipment and a service transmission delay guarantee constraint, and establishing an optimization model aiming at maximizing the throughput of the internet of things equipment;
and solving the time delay model based on the cognitive industry Internet of things through Lagrange's theorem, and obtaining the throughput of the time delay model by adopting an iterative algorithm.
Optionally, the assumption condition for establishing the time delay model of the cognitive industry internet of things based on the queuing theory is as follows:
the data packet arrival rate of the Internet of things equipment is assumed to follow Poisson distribution, and the data packet service time follows exponential distribution.
Optionally, before establishing the cognitive industry internet of things model in the dynamic uncertain scene, the method includes:
assuming that the dynamic uncertain scene has N subcarriers, wherein each mobile user occupies one subcarrier for data transmission, and each Internet of things device shares the subcarrier through cognitive radio;
defining a set of micro base stations as
The set of the Internet of things equipment is
The set of subcarriers is
Wherein, I represents the total number of the micro base stations, I represents the ith micro base station, J represents the total number of the internet of things equipment, J represents the jth internet of things equipment, N represents the total number of the subcarriers, and N represents the nth subcarrier.
Optionally, the establishing of the time delay model of the internet of things of the cognitive industry based on the queuing model specifically includes:
setting a queuing process of the data packet arriving at a sending end as an M/M/1 queuing model, and keeping the average waiting time less than the longest stay time allowed in the queue, so that the following requirements are met:
wherein,
lambda is the rate of data arrival of the cognitive industry Internet of things, R j,n In order to recognize the transmission rate which can be reached by the data transmission of the industrial Internet of things,
for the maximum dwell time allowed in the queue, T j,n Is the average latency;
the formula (1) is simplified as:
the delay model is then:
wherein
Optionally, the determining a rate resolution solution based on a requirement for guaranteeing a delay so that an association multiple of a distribution factor on each subcarrier and a channel gain of a mobile user does not exceed an interference threshold on the subcarrier specifically includes:
setting p i,j,n Is the power h of the micro base station i from the subcarrier n to the Internet of things equipment j i,j,n Representing the channel gain from the subcarrier n to the cognitive industry internet of things j of the micro base station i, and representing the signal to interference plus noise ratio received by the channel as follows:
where m denotes a different one of the i micro base stations from i, σ 2 Representing the sum of the noise power and the mobile user interference;
calculating to obtain the data rate R which can be realized by the Internet of things equipment on the subcarrier n i,j,n ＝R j,n ＝Wlog(1+γ i,j,n ) Wherein W is a first adjusting parameter;
the interference is constrained as follows:
wherein s is i,j,n Denotes the subcarrier allocation factor, g i,j,n Representing the channel gain from the cognitive industry internet of things j of the micro base station i to the mobile user on the subcarrier n,
is the interference threshold of the mobile user on subcarrier n.
Optionally, when s i,j,n And when =1, it indicates that the micro base station i is from the subcarrier n to the internet of things device j.
Optionally, the cognitive industry internet of things resource allocation model based on the randomness of the dynamic uncertain environment is as follows:
wherein the constraint conditions are as follows:
wherein ξ n Uncertainty for different channels, also known as interference probability threshold, R i,j,n Representing the achievable data rate of the internet of things device on subcarrier n.
Optionally, the solving is performed on the delay model based on the internet of things of the cognitive industry by lagrangian theorem, and an iterative algorithm is adopted to obtain the throughput of the delay model, which specifically includes:
dualizing the random model by adopting Lagrangian duality theorem;
and solving the random model by utilizing a KKT condition of Lagrangian to obtain an analytic solution of throughput.
According to a second aspect of the embodiments of the present application, there is provided a resource allocation device for a cognitive industry internet of things in a dynamic uncertain scene, including:
the time delay model building module is used for building a cognitive industrial Internet of things model under a dynamic uncertain scene and building a time delay model of the cognitive industrial Internet of things based on the queuing model; the cognitive industrial Internet of things model under the dynamic uncertain scene is established based on an authorized large-range macro cell area, each macro cell area comprises a plurality of mobile users and a macro base station, a plurality of industrial Internet of things in micro cell areas are also established in the macro cell area, and each industrial Internet of things network comprises a plurality of Internet of things devices and a micro base station;
a rate resolution determination module, configured to determine a rate resolution solution based on a requirement for guaranteeing delay, so that an association multiple of an allocation factor on each subcarrier and a channel gain of a mobile user does not exceed an interference threshold on the subcarrier;
the random cognitive industry internet of things resource allocation model building module is used for building a cognitive industry internet of things resource allocation model based on dynamic uncertain environment randomness, combining base station transmitting power constraint, interference constraint between equipment and service transmission delay guarantee constraint, and building an optimization model aiming at maximizing the throughput of the internet of things equipment;
and the convex constraint throughput solving module is used for solving the time delay model based on the cognitive industry Internet of things through Lagrange's theorem and obtaining the throughput of the time delay model by adopting an iterative algorithm.
According to a third aspect of embodiments herein, there is provided a nonvolatile storage device including: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored by the memory to implement the method provided by the first aspect.
Has the advantages that:
according to the resource allocation method and device for the cognitive industrial internet of things in the dynamic uncertain scene, aiming at the problem of time delay guarantee of the service of the cognitive industrial internet of things in the dynamic uncertain scene, a time delay model of the cognitive industrial internet of things is established based on a queuing theory, a rate analytic solution for guaranteeing time delay requirements is deduced, and a resource allocation model of the cognitive industrial internet of things considering randomness of a dynamic uncertain environment is established based on the model; due to the introduction of random environment parameters, the model is difficult to obtain a global optimal solution by adopting a traditional optimization method; aiming at the problem, the method adopts the idea of robust optimization to convert the constraint of uncertain parameters into convex constraint, and provides a resource allocation strategy of the cognitive industry Internet of things.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the application and are not to be construed as limiting the application.
Fig. 1 is a diagram illustrating a cognitive industry internet of things communication architecture in a dynamic uncertain scenario provided by the present application according to an exemplary embodiment;
fig. 2 is a schematic flowchart illustrating a resource allocation method for the internet of things of cognitive industry in a dynamic uncertain scene according to an exemplary embodiment;
fig. 3 is a diagram illustrating a representation of throughput versus different channel estimation errors for an internet of things device, according to an example embodiment;
fig. 4 illustrates an internet of things device overall Interference Efficiency (IE) and an Interference threshold under different algorithms according to an example embodiment
The relationship of (a) represents a schematic diagram;
fig. 5 shows an overall Energy Efficiency (EE) and an interference threshold of an internet of things device according to another exemplary embodiment under different algorithms
The relationship of (a) represents a schematic diagram;
fig. 6 is a schematic structural diagram illustrating a resource allocation apparatus of a cognitive industry internet of things in a dynamic uncertain scene according to an exemplary embodiment.
Detailed Description
In order to make the technical solutions of the present application better understood by those of ordinary skill in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
As shown in fig. 1, the scenario involved in the present application is CIIoT in a dynamic uncertain scenario. The overall scenario is based on an authorized large-scale Macro cell Area (MA), where the MA includes M Mobile Users 100 (MU) and a Macro Base Station 120 (MBS), assuming that IIoT of I micro cell areas (FA) is established in the MA, and the IIoT network includes J Internet of Things devices 130 (Cognitive industry of Internet devices, CIIoTD) and a micro Base Station 140 (FBS).
Due to the scarcity of spectrum resources, IIoT networks may share spectrum resources with MAs in an underlay mode. Assuming that a scene has N total subcarriers, each MU occupies one subcarrier for data transmission, and each CIIoTD shares the subcarriers through cognitive radio. Define the set of FBS as
Set of CIIoTD is
The set of subcarriers is
Wherein I represents the total number of the micro base stations, I represents the ith micro base station,j represents the total number of industrial internet of things, J represents the jth industrial internet of things, N represents the total number of subcarriers, and N represents the nth subcarrier.
Due to the fact that dynamic uncertainty exists in a communication environment due to the fact that mobility and behaviors of the MU are complex, the time delay requirement for guaranteeing the spectrum sharing CIIoTD service is difficult.
As shown in fig. 2, to solve the above technical problem, the present application provides a resource allocation method for a cognitive industry internet of things in a dynamic uncertain scene. Specifically, the method comprises the following steps:
s1, establishing a CIIoT (common Internet of things) delay model based on a queuing theory.
In some embodiments, before the delay model of CIIoT is built based on queuing theory, packet arrival rate of CIIoTD is assumed to follow poisson distribution, and packet service time follows exponential distribution.
In some embodiments, the queuing process of the data packets arriving at the sending end is modeled as an M/1 queuing model, that is, there is only one data processor, the arrival time of the data packets arriving at the receiving end is a poisson process, and the residence time follows exponential distribution. λ is the rate of CIIoTD data arrival, R j,n For the transmission rate reachable for CIIoTD data transmission,
for the maximum dwell time allowed in the queue, T j,n For the average waiting time, in order to reduce the probability of packet loss, the average waiting time needs to be smaller than the longest allowed residence time in the queue, and the expression is as follows:
wherein,
after simplification, the following can be obtained:
namely, the rate expression under the requirement of guaranteeing the time delay is as follows:
wherein
Definition of p i,j,n Is the power, h, of the FBSi from subcarrier n to CIIoTDj i,j,n The channel gain of FBSi from subcarrier n to CIIoTDj is shown, so the received Signal to Interference plus Noise Ratio (SINR) of the channel is shown as:
where m denotes a different one of the i micro base stations from i, σ 2 Representing the sum of the noise power and MU interference, so the achievable data rate for CIIoTD on subcarrier n is R i,j,n ＝R j,n ＝Wlog(1+γ i,j,n ) Wherein W is a first adjusting parameter.
And S2, determining a rate analytic solution based on the requirement of guaranteeing the time delay so that the correlation multiple of the distribution factor on each subcarrier and the channel gain of the mobile user does not exceed the interference threshold on the subcarrier.
In some embodiments, considering the interference of CIIoTD to MU, in order not to affect the delay of MU transmission and the packet loss rate of data, the interference needs to be constrained, as shown in equation (5).
Wherein s is i,j,n Denotes the subcarrier allocation factor, g i,j,n Representing the channel gain from CIIoTDj of FBSi to MU on subcarrier n,
is the interference threshold of the MU on subcarrier n. When s is i,j,n When =1, FBSi is expressed from subcarrier n to CIIoTDj.
And S3, establishing a cognitive industry Internet of things resource allocation model based on the randomness of the dynamic uncertain environment, combining base station transmitting power constraint, interference constraint between devices and service transmission delay guarantee constraint, and establishing an optimization model aiming at maximizing the throughput of the CIIoTD.
In the CIIoT network according to some embodiments of the present application, in order to ensure randomness and service delay requirements of a dynamic uncertain scene, an environment random variable is introduced, and a random model for introducing the environment random variable is formed as follows:
wherein the constraint conditions are as follows:
wherein s is i,j,n Denotes the subcarrier allocation factor, g i,j,n Representing the channel gain from CIIoTDj of FBSi to MU on subcarrier n,
is the interference threshold, ξ, of the MU on subcarrier n n Uncertainty for different channels, also known as interference probability threshold, R i,j,n Representing the achievable data rate of CIIoTD on subcarrier n.
And S4, solving the CIIoT-based delay model through a Lagrange theory, and obtaining the throughput of the CIIoT-based delay model by adopting an iterative algorithm.
In some embodiments, to obtain an analytical expression of throughput, the objective function may be through a quadratic transformation-based approach, as shown in equation (7).
In formula (7) y m,d,n Is an auxiliary variable introduced by a quadratic transformation, wherein
W is a first regulating parameter, p i,j,n Is the power, h, of the FBSi from subcarrier n to CIIoTD j i,j,n Denotes the channel gain, p, of the FBSi from subcarrier n to CIIoTDj m,j,n Is the power, h, of the FBSm from subcarrier n up to CIIoTD j m,j,n Denotes the channel gain, σ, of the FBSm from subcarrier n up to CIIoTD j 2 Representing the sum of the noise power and MU interference.
Constraint h of rate and interference power of equation (6) i,j,n And g i,j,n The channel uncertainty of (c) can be modeled as:
wherein,
and
is the estimated channel gain, Δ h i,j,n And Δ g i,j,n Subject to a gaussian distribution,
and
indicating the corresponding channel estimation error.
In some embodiments, define
Wherein,
according to Δ g i,j,n Gaussian distribution model of (1), constraint C 1 The following steps are changed:
the following can be obtained by the theorem of majority:
then, according to the nature of the Gaussian Q (-) function:
wherein
The following can be obtained:
in some embodiments, according to the inequality
Simplifying formula (12), thus obtaining:
wherein
Constraint C 3 The method is a non-convex constraint under the delay sensitivity service and meets the requirement of the lowest rate of CIIoTD transmission data, and the expression is as follows:
from the opportunity constraint and the gaussian Q (-) function:
wherein
And
based on the above series of transformations, the problem constraint of equation (6) becomes a convex constrained problem model:
the constraint function is:
wherein
And
and S41, dualization is carried out on the random model by adopting Lagrangian duality theorem.
In some embodiments, it is assumed that
Then
Wherein alpha is n ，χ i,n And
non-negative lagrange multipliers. After simplification, the following can be obtained:
wherein,
to this end, the stochastic model is converted into a pair of dual models:
the constraint conditions are as follows:
in some embodiments, the objective function of the dual model is:
and S42, solving the random model by utilizing the KKT condition of Lagrange to obtain an analytic solution of the throughput.
In some embodiments, s may be derived from the KKT condition of Lagrangian i,j,n ：
Wherein
Thus, the transmit power p can be derived from the KKT condition of Lagrangian i,j,n Comprises the following steps:
therefore, equation (26) is equal to:
wherein
After simplification, the following can be obtained:
in some embodiments, the associated pseudo-code is as shown in Algorithm 1:
in some embodiments, an exemplary simulation scenario of the present application is CIIoT in a dynamic uncertain scenario. The coverage area of the MBS is within a circle with a radius of 500m, the coverage area of the FBS is within a circle with a radius of 50m, the bandwidth of the base station is 10MHz, the number of the FBS is assumed to be 1, the number of the ciiotds is assumed to be 20, and the simulation parameters are shown in table 2.
Table 2 simulation parameter settings
Fig. 3 shows throughput of CIIoTD versus different channel estimation errors. Wherein delta 1 And delta 2 The uncertainty of different channels of two specific CIIoTD devices respectively, the channel gain of CIIoTD1 is lower than that of CIIoTD2, i.e. h 1 =1 and h 2 And (5). As can be seen in fig. 3, with channel uncertainty δ 1 And delta 2 Decrease in CIIoTD, increase in CIIoTD. It is explained that when the channel estimation error increases, the transmission power of CIIoTD is increased, thereby decreasing the IE of the system. Also, it can be seen that the IE of CIIoTD1 with poor channel conditions is lower than the IE of CIIoTD2 with good channel conditions.
In other examples, FIG. 4 shows CIIoTD total IE and interference threshold under different algorithms
The relationship (2) of (c). It can be seen from the figure that the total Interference Efficiency (IE) of CIIoTD follows the Interference threshold
Increasing and becoming smaller. Illustrating the interference threshold
When increased, the system requires more energy to achieve data transmission, thereby reducing the data transmission rate. IE of the proposed stochastic model allocation algorithm over IE of the non-stochastic model allocation algorithmThe average reduction is 30.70%. And, when the channel uncertainty is δ =0.1, the non-stochastic model allocation algorithm has a similar IE as the proposed stochastic model allocation algorithm.
In other embodiments, FIG. 5 shows the total Energy Efficiency (EE) and interference threshold of CIIoTD under different algorithms
The relationship (2) of (c). As can be seen from the figure, the total EE of CIIoTD follows the interference threshold
Is increased and becomes smaller. The EE of the random model distribution algorithm is averagely improved by 134.25% compared with the EE of the non-random model distribution algorithm, the random model distribution algorithm has the best energy efficiency performance, and the non-random model distribution algorithm is inferior. The proposed stochastic model allocation algorithm has a higher EE than the non-stochastic model allocation algorithm when in the same channel.
The result shows that in the resource allocation method for the internet of things in the cognitive industry in the dynamic uncertain scene, the problem of time delay guarantee of the service of the internet of things in the cognitive industry in the dynamic uncertain scene is solved. According to the method, on one hand, a time delay model of the CIIoT service is established based on a queuing theory, a rate analytic solution for guaranteeing time delay requirements is deduced, and a cognitive industry Internet of things resource allocation model considering dynamic uncertain environment randomness is established based on the model. On the other hand, due to the introduction of random environment parameters, the model is difficult to obtain a global optimal solution by adopting a traditional optimization method. Aiming at the problem, the method adopts the idea of robust optimization to convert the constraint of uncertain parameters into convex constraint, and provides a resource allocation strategy of the cognitive industry Internet of things.
Fig. 6 is a schematic structural diagram of a resource allocation apparatus for cognitive internet in a dynamic uncertain scene according to an exemplary embodiment of the present application. The resource allocation device for the cognitive internet in the dynamic uncertain scene, provided by the embodiment of the application, can execute the processing flow provided by the resource allocation method for the cognitive internet in the dynamic uncertain scene. As shown in fig. 6, the resource allocation apparatus 20 for the internet of things in cognitive industry in a dynamic uncertain scene provided by the present application includes:
the time delay model building module 201 is used for building a cognitive industry internet of things model in a dynamic uncertain scene and building a cognitive industry internet of things time delay model based on a queuing model; the cognitive industry Internet of things model under the dynamic uncertain scene is built based on an authorized large-range macro cell area, each macro cell area comprises a plurality of mobile users and a macro base station, a plurality of micro cell area industry Internet of things are built in the macro cell area, and each micro cell area industry Internet of things comprises a plurality of Internet of things devices and a micro base station;
a rate resolution determination module 202, configured to determine a rate resolution solution based on a requirement for guaranteeing delay, so that a correlation multiple between an allocation factor on each subcarrier and a channel gain of a mobile user does not exceed an interference threshold on the subcarrier;
the random cognitive industry internet of things resource allocation model building module 203 is used for building a cognitive industry internet of things resource allocation model based on the randomness of a dynamic uncertain environment, and building an optimization model aiming at maximizing the throughput of the internet of things equipment by combining base station transmitting power constraint, interference constraint between equipment and service transmission delay guarantee constraint;
and the convex constraint throughput solving module 204 is used for solving the time delay model based on the cognitive industry internet of things through Lagrange's theorem and obtaining the throughput of the time delay model by adopting an iterative algorithm.
The apparatus provided in the embodiment of the present application may be specifically configured to execute the scheme provided in the embodiment of the method corresponding to fig. 2, and specific functions and technical effects that can be achieved are not described herein again.
An embodiment of the present application further provides a nonvolatile memory device, including: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes the computer execution instructions stored in the memory to implement the solutions provided by any of the above method embodiments, and the specific functions and technical effects that can be achieved are not described herein again. The electronic device may be the above-mentioned server.
The embodiment of the present application further provides a computer-readable storage medium, where computer-executable instructions are stored in the computer-readable storage medium, and when the computer-executable instructions are executed by a processor, the computer-executable instructions are used to implement the solutions provided in any of the above method embodiments, and specific functions and technical effects that can be achieved are not described herein again.
An embodiment of the present application further provides a computer program product, where the program product includes: the computer program is stored in a readable storage medium, at least one processor of the electronic device can read the computer program from the readable storage medium, and the at least one processor executes the computer program to enable the electronic device to execute the scheme provided by any one of the above method embodiments, and specific functions and achievable technical effects are not described herein again.
The application scenario described in the embodiment of the present application is for more clearly illustrating the technical solution of the embodiment of the present application, and does not form a limitation on the technical solution provided in the embodiment of the present application, and it can be known by a person skilled in the art that with the occurrence of a new application scenario, the technical solution provided in the embodiment of the present application is also applicable to similar technical problems.
As will be appreciated by one skilled in the art, aspects of the present application may be embodied as a system, method or program product. Accordingly, various aspects of the present application may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
In some possible implementations, an electronic device according to the present application may include at least one processor, and at least one memory. The memory stores program code, and when the program code is executed by the processor, the program code causes the processor to execute the operation data management method according to various exemplary embodiments of the present application described above in the present specification. For example, the processor may perform steps as in an operational data management method.
It should be noted that although several units or sub-units of the apparatus are mentioned in the above detailed description, such division is merely exemplary and not mandatory. Indeed, the features and functions of two or more units described above may be embodied in one unit, according to embodiments of the application. Conversely, the features and functions of one unit described above may be further divided into embodiments by a plurality of units.
Further, while the operations of the methods of the present application are depicted in the drawings in a particular order, this does not require or imply that these operations must be performed in this particular order, or that all of the illustrated operations must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable image scaling apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable image scaling apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable image scaling apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable image scaling device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer implemented process such that the instructions which execute on the computer or other programmable device provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (10)
1. A resource allocation method of a cognitive industry Internet of things in a dynamic uncertain scene is characterized by comprising the following steps:
establishing a cognitive industrial Internet of things model under a dynamic uncertain scene, and establishing a cognitive industrial Internet of things time delay model based on a queuing model; the cognitive industry Internet of things model under the dynamic uncertain scene is built based on an authorized large-range macro cell area, each macro cell area comprises a plurality of mobile users and a macro base station, a plurality of micro cell area industry Internet of things are built in the macro cell area, and each micro cell area industry Internet of things comprises a plurality of Internet of things devices and a micro base station;
determining a rate analytic solution based on guaranteed delay requirements so that the correlation multiple of the allocation factors on each subcarrier and the channel gain of the mobile user does not exceed an interference threshold on the subcarrier;
establishing a cognitive industry internet of things resource allocation model based on the randomness of a dynamic uncertain environment, combining a base station transmitting power constraint, an interference constraint between equipment and a service transmission delay guarantee constraint, and establishing an optimization model aiming at maximizing the throughput of the internet of things equipment;
and solving the time delay model based on the cognitive industry Internet of things through Lagrange's theorem, and obtaining the throughput of the time delay model by adopting an iterative algorithm.
2. The method according to claim 1, wherein the assumed conditions for establishing the time delay model of the internet of things in the cognitive industry based on the queuing theory are as follows:
the data packet arrival rate of the Internet of things equipment is assumed to follow Poisson distribution, and the data packet service time follows exponential distribution.
3. The method of claim 2, wherein prior to establishing the cognitive industrial internet of things model in a dynamic uncertain scenario, the method comprises:
assuming that the dynamic uncertain scene has N subcarriers, wherein each mobile user occupies one subcarrier for data transmission, and each Internet of things device shares the subcarrier through cognitive radio;
defining a set of micro base stations as
The set of the equipment of the Internet of things is
The set of subcarriers is
Wherein, I represents the total number of the micro base stations, I represents the ith micro base station, J represents the total number of the internet of things equipment, J represents the jth internet of things equipment, N represents the total number of the subcarriers, and N represents the nth subcarrier.
4. The method according to claim 3, wherein the establishing of the cognitive industry Internet of things delay model based on the queuing model specifically comprises:
setting a queuing process of the data packet arriving at a sending end as an M/M/1 queuing model, and keeping the average waiting time less than the longest stay time allowed in the queue, so that the following requirements are met:
wherein,
lambda is the rate of data arrival of the cognitive industry Internet of things, R j,n In order to recognize the transmission rate which can be reached by the data transmission of the industrial Internet of things,
for the maximum dwell time allowed in the queue, T j,n Is the average latency;
the formula (1) is simplified as:
the delay model is then:
wherein
5. The method of claim 2, wherein the determining a rate resolution solution based on guaranteed delay requirements such that an association multiple of an allocation factor on each subcarrier and a channel gain of a mobile user does not exceed an interference threshold on the subcarrier comprises:
setting p i,j,n Is the power h of the micro base station i from the subcarrier n to the Internet of things equipment j i,j,n Representing the channel gain from the subcarrier n to the cognitive industry internet of things j of the micro base station i, and representing the signal to interference plus noise ratio received by the channel as follows:
where m denotes a different one of the i micro base stations from i, σ 2 Representing the sum of the noise power and the mobile user interference;
the data rate which can be realized by the Internet of things equipment on the subcarrier n is obtained through calculation
Wherein W is a first adjusting parameter;
the interference is constrained as follows:
wherein s is i,j,n Denotes the subcarrier allocation factor, g i,j,n Representing the channel gain from the cognitive industry internet of things j of the micro base station i to the mobile user on the subcarrier n,
is the interference threshold of the mobile user on subcarrier n.
6. The method of claim 5, wherein s is measured as i,j,n And when =1, it indicates that the micro base station i is from the subcarrier n to the internet of things device j.
7. The method according to claim 6, wherein the cognitive industry internet of things resource allocation model based on the randomness of the dynamic uncertain environment is as follows:
wherein the constraint conditions are as follows:
C 1 :
C 2 :
C 3 :
wherein ξ n Uncertainty for different channels, also known as interference probability threshold, R i,j,n Representing the achievable data rate of the internet of things device on subcarrier n.
8. The method according to claim 7, wherein the time delay model based on the internet of things of the cognitive industry is solved through the Lagrange's theorem, and the throughput is obtained through an iterative algorithm, specifically comprising:
dualizing the random model by adopting Lagrangian duality theorem;
and solving the random model by utilizing a KKT condition of Lagrangian to obtain an analytic solution of throughput.
9. A resource distribution device of a cognitive industry Internet of things in a dynamic uncertain scene is characterized by comprising:
the time delay model building module is used for building a cognitive industrial Internet of things model under a dynamic uncertain scene and building a time delay model of the cognitive industrial Internet of things based on the queuing model; the cognitive industrial Internet of things model under the dynamic uncertain scene is established based on an authorized large-range macro cell area, each macro cell area comprises a plurality of mobile users and a macro base station, a plurality of industrial Internet of things in micro cell areas are also established in the macro cell area, and each industrial Internet of things network comprises a plurality of Internet of things devices and a micro base station;
a rate resolution determination module, configured to determine a rate resolution solution based on a requirement for guaranteeing delay, so that an association multiple of an allocation factor on each subcarrier and a channel gain of a mobile user does not exceed an interference threshold on the subcarrier;
the random cognitive industry internet of things resource allocation model building module is used for building a cognitive industry internet of things resource allocation model based on dynamic uncertain environment randomness, combining base station transmitting power constraint, interference constraint between equipment and service transmission delay guarantee constraint, and building an optimization model aiming at maximizing the throughput of the internet of things equipment;
and the convex constraint throughput solving module is used for solving the time delay model based on the cognitive industry Internet of things through Lagrange's theorem and obtaining the throughput of the time delay model by adopting an iterative algorithm.
10. A non-volatile storage device, comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored by the memory to implement the method of any of claims 1-8.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116938835A (en) * | 2023-09-18 | 2023-10-24 | 北京交通大学 | Bandwidth resource allocation method in industrial Internet of things scene and electronic equipment |
| CN117641598A (en) * | 2023-12-06 | 2024-03-01 | 重庆理工大学 | Consumer electronic resource allocation method and system based on split robust learning |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116938835A (en) * | 2023-09-18 | 2023-10-24 | 北京交通大学 | Bandwidth resource allocation method in industrial Internet of things scene and electronic equipment |
| CN116938835B (en) * | 2023-09-18 | 2023-11-17 | 北京交通大学 | Bandwidth resource allocation method in industrial Internet of things scene and electronic equipment |
| CN117641598A (en) * | 2023-12-06 | 2024-03-01 | 重庆理工大学 | Consumer electronic resource allocation method and system based on split robust learning |
| CN117641598B (en) * | 2023-12-06 | 2024-07-09 | 重庆理工大学 | Consumer electronic resource allocation method and system based on split robust learning |
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