CN114189914B - WiFi6 optimization switching decision method and system based on service priority - Google Patents
WiFi6 optimization switching decision method and system based on service priority Download PDFInfo
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Abstract
The invention provides a WiFi6 optimized switching decision method and a WiFi6 optimized switching decision system based on service priorities, which are used for constructing a switching scheduling model based on service priorities by considering the influence of signal strength and transmission expense on service transmission according to a channel resource division mode in WiFi 6. Simulation experiments show that the method provided by the invention can realize accurate switching of service priority and ensure the overall quality of power service transmission.
Description
Technical Field
The invention relates to the technical field of Internet, in particular to a WiFi6 optimization switching decision method and system based on service priority.
Background
With the development of the internet, the WiFi standard has been developed as an easy-to-deploy and high-speed wireless local area network standard, and has been widely applied in the electric power internet of things. WiFi6 is a new generation of wireless local area network standard, and aims to solve the problem of efficient transmission in dense networks. The WiFi6 adopts the OFDMA technology, so that channel resources can be divided more finely and distributed to different electric power Internet of things terminals reasonably and efficiently according to requirements, and transmission delay is reduced. In the electric power internet of things, different services have different transmission requirements according to service attributes of the services. The requirements of low time delay such as relay protection service, scheduling automation service and the like; the patrol video backhaul service and the like have high bandwidth requirements. Service priorities may be set in the power system according to different transmission requirements. Because a single wireless Access Point (AP) has limited service carrying capability, it is highly likely that a power internet of things terminal needs to switch to another AP during transmission to meet transmission requirements. If the accurate switching cannot be completed on the premise of differentiating the service priorities, that is, the transmission requirements of different services cannot be met after the switching, the transmission quality of the terminal and even the whole network can be affected. How to realize accurate switching of terminals in the electric power Internet of things by differentiating service priorities based on the technical characteristics of WiFi6 so as to ensure the transmission quality of electric power service has become an important research direction.
The patent number CN107124744a provides a network switching method and a wireless access point, where a connecting AP can scan for other switchable APs in a range, and determine an access priority according to access information. However, in this scheme, global switching scheduling optimization is not performed for the terminal, and the situation of network congestion is easily caused due to lack of uniform allocation scheduling.
The document CN106961690a proposes a method for user service aware adaptive switching, which allows as many end users to connect as possible while achieving information interaction between two networks, while guaranteeing network load balancing. Although the scheme considers the service transmission quality and priority, the influence of the new characteristic of WiFi6 on the multi-service transmission is not considered pertinently, and a great optimization space is still reserved.
The document CN101932054a devised a method for switching wireless local area networks, in which a mobile terminal listens to a beacon frame sent by an access point during transmission, and stores this information, through which an AP can be selectively accessed during subsequent switching. However, in this scheme, the decision condition is only signal strength, and the signal strength cannot fully reflect the transmission quality of the current AP, which is likely to affect the service transmission quality.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention designs a WiFi6 optimized switching decision method based on service priority, which can realize switching scheduling decision based on service priority, optimize transmission energy consumption and ensure the transmission quality of the electric power Internet of things terminal.
Specifically, the invention provides a WiFi6 optimization switching decision method based on service priority, which comprises the following steps:
s1: constructing a WiFi6 switching optimization scheduling model based on service priority;
s2: and solving the optimal scheduling model based on a genetic algorithm to realize optimal switching scheduling based on service priority.
Preferably, step S1 comprises:
s11: SSTA and SAP are built as sets of terminals and APs respectively,
SSTA＝{sta a |1≤a≤N sta }，
SAP＝{AP b |1≤b≤N ap }。
wherein N is sta And N ap The number of terminals and APs in SSTA and SAP, respectively, and a and b are the terminal stas in the set, respectively a And AP (Access Point) b Corresponding subscript, sta a Representing a plurality of terminals, AP b Representing a plurality of wireless access points;
s12: let h a Representing sta a Connected AP sequence number, h a =b, denote sta a Connecting to an AP b On the upper part, N sta The terminals are connected to N ap The optimal switching scheme of each AP is expressed as:
s13: the WiFi6 switching optimization scheduling model based on the service priority is constructed as follows:
st RSSI a，b ≥RSSI threshold ，h a ＝b
QoS a ≤QoS b ，h a ＝b
wherein gamma, delta and epsilon are taken as weight factors, and the service priority, the average received signal strength and the influence of transmission cost on the switching scheduling are respectively and correspondingly adjusted, and the RSSI opt A factor representing the correlation of the received signal strength value, z all As a utility function, C average Average transmission overhead for global terminals; RSSI (received signal strength indicator) a，b Representing a terminal sta a Connecting to a wireless Access Point (AP) b Is a received signal strength value of (1); RSSI (received signal strength indicator) threshold Representing a signal strength threshold; qoS (quality of service) a For the terminal sta a Traffic transmission priority, qoS of (c) b Representing wireless Access Point (AP) b Traffic transmission priority of (c).
Preferably, a utility function z is defined all The multi-service transmission case for prioritizing services in the global case is expressed as:
l(a)＝O a ×QoS a
o in a Representing a terminal sta a Minimum number of resource units allocated after handover scheduling, qoS a For the terminal sta a Traffic transmission priority, qoS of (c) a The value of (2) may be a positive integer of 1-10, qoS a The larger the value of (2) is, the description sta a The higher the traffic priority of (c).
Preferably, the RSSI factor is used to represent the correlation of the received signal strength values opt The calculation method is as follows:
RSSI opt ＝20+RSSI average ÷10
wherein RSSI average Representing the average received signal strength of the terminal under the direction of the access vector G.
Preferably, wherein the average transmission overhead C of the global terminal average Expressed as:
wherein O is a Representing a terminal sta a The minimum number of resource units allocated after the handover scheduling, C b Is AP b Is expressed as:
QoS b representing AP b Traffic transmission priority of (a) and (β) are respectivelyEnergy consumption level and influence factor of service QoS level E b Expressed as energy consumption level according to the equipment energy consumption identification, E b The value of (2) may be set to a positive integer of 1-5.
Preferably, the step S2 includes:
s21: randomly generating N P Individual individuals and form a population;
s22: performing cross operation on individuals in the population every two, and expanding the obtained new individuals into the population;
s23: performing mutation operation on the expanded population to avoid sinking into a local optimal solution;
s24: and outputting the optimal individuals in the population after the iteration conditions are met.
Preferably, in the step S21,
each individual has a total of N sta Genes, each gene having N ap Seed traits, a group of vectors is assembled into SG= { P i |1≤i≤N P The collection formed is called a population, in which
The objective function of each individual is set as the optimal scheduling model and the corresponding constraints in the model need to be met.
Preferably, in the step S22, it is assumed that the individual P is present at this time i-1 With individual P i Cross generation P i+1 ，N random Is greater than 1 and less than N ap The formula of the crossover operation is expressed as follows:
preferably, the step S23 includes:
each two individuals in the crossover operation can generate a new individual, and the population quantity is increased after the crossover operation on the assumption that only one individual is generated between the two individualsEliminating the +.f. with the highest objective function value>Individual, in order to ensure the population quantity unchanged.
Preferably, the step S24 includes:
if the operation times do not meet the algorithm iteration times n, continuing to perform crossover, mutation and selection operation to complete iteration; if n iterations have been performed, the optimal individuals in the population are output.
The invention also provides a WiFi6 optimized switching decision system based on the service priority, which comprises the following steps:
model construction module: constructing a WiFi6 switching optimization scheduling model based on service priority;
and a calculation scheduling module: and solving the optimal scheduling model based on a genetic algorithm to realize optimal switching scheduling based on service priority.
The invention also provides a terminal, which comprises a processor and a storage medium; the storage medium is used for storing instructions; the processor is operative to perform the steps of the method of the present invention in accordance with the instructions.
The invention also proposes a computer-readable storage medium on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the method according to the invention.
The invention provides a switching scheduling model of service priority, which is constructed to optimize signal strength and transmission cost under the condition of guaranteeing service transmission priority and optimize network multi-service transmission by considering the influence of signal strength and transmission cost on service transmission according to the channel resource allocation characteristic of WiFi6 and combining the service priority.
The invention provides a switching scheduling method based on a genetic algorithm, which aims at a switching scheduling model based on service priority, solves by using the genetic algorithm, realizes optimal switching scheduling based on service priority, ensures multi-service transmission quality of a terminal, and optimizes transmission quality of an electric power communication network.
Drawings
Fig. 1 is a block diagram of a handover scheduling method based on a genetic algorithm according to the present invention.
Fig. 2 is a schematic diagram of the comparison of the method of the present invention and the optimized function value of the switching method for making a switching decision based on RSSI.
Fig. 3 is a schematic diagram showing average rate comparison between the method of the present invention and a handover method for making a handover decision based on RSSI.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. The embodiments described herein are merely some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art without making any inventive effort, are within the scope of the present invention.
WiFi6, an evolved version of WiFi, enables channel resources to be divided into multiple resource units RU (Resource Unit) by introducing OFDMA technology to achieve better and more efficient communication resource management and traffic transmission. In WiFi6, the wireless access point AP may specify how many RUs are used in the entire bandwidth according to the terminal service priority situation, and may allocate RUs of different sizes to different terminals to meet the service requirement. The quality of service transmission is related to the channel condition where the RU is located, the size of the sub-carrier included in the RU, and so on. Therefore, when the terminal is switched, the transmission condition after the switching is closely related to the current RU distribution condition of the switching target AP. The AP may allocate channel bandwidths in the same time slot to different terminals according to different traffic priorities, and flexibility of traffic scheduling will be greatly increased.
Based on the special specificity of WiFi6 channel resource allocation, the method and the device construct a switching scheduling model based on service priority aiming at RU allocation conditions.
Let SSTA and SAP be the sets of terminals and APs respectively,
SSTA＝{sta a |1≤a≤N sta }，
SAP＝{AP b |1≤b≤N ap }。
wherein N is sta And N ap The number of terminals and APs in SSTA and SAP, respectively, and a and b are the terminal stas in the set, respectively a And AP (Access Point) b Corresponding subscripts. sta a Representing a plurality of terminals, AP b Representing a plurality of wireless access points.
Let h a Representing sta a Connected AP sequence number, h a =b, denote sta a Connecting to an AP b And (3) upper part.
Then N sta The terminals are connected to N ap Optimal switching scheme of each AP:
the problem of AP switching scheduling studied by the invention is how to distinguish the priority of transmission service after switching triggering, and find out the optimal switching scheduling scheme P.
When considering the AP channel resource occupancy, we consider each 26 sub-carrier RU in the AP operating bandwidth as the smallest resource unit. If there are RU containing more subcarriers in the RU allocation case, it is regarded as an RU formed by combining a plurality of 26-subcarrier RU.
Let O a Representing a terminal sta a The minimum number of resource units allocated after handover scheduling, O b Denoted as AP b The minimum number of resource units of the occupied frequency band. Let QoS a Is sta a Traffic transmission priority of (c). QoS (quality of service) a The value of (2) may be a positive integer of 1 to 10. QoS (quality of service) a The larger the value of (2) is, the description sta a The higher the traffic priority of (c).
Thus defining a utility function l (a) to represent sta in prioritizing traffic a The number of RUs is allocated to support the case of multi-service transmission. Definition z all For the multi-service transmission case where service is prioritized in the global case:
l(a)＝O a ×QoS a (1)
utility function z all The larger the value of the (E) is, the higher the service priority of the terminal after switching and scheduling can be relatively distributed to more transmission resources, and the service transmission of the electric power Internet of things terminal can be better ensured by differentiating the service priority. Therefore, in a service priority based handover scheduling model, the objective of the present invention is to maximize the service priority utility function z all 。
In the switching scheduling process based on WiFi, due to the difference of networks, multiple factors are generally required to be comprehensively considered to optimize global network transmission. Therefore, in the invention, the signal strength and the transmission cost are increased as influencing factors, and the service priority, the signal strength and the transmission cost are considered in the switching scheduling model to find the optimal scheduling scheme P.
(1) Received signal strength: using RSSI a，b Representing sta a Connecting to an AP b Is provided. And the terminal performs channel scanning after determining switching to acquire related information of surrounding APs. These include, among other things, the signal strength RSSI values of the various APs. The average received signal strength of the terminal and the connected AP under the handover scheme is considered in the present invention. Taking into account the effect of differences between orders of magnitude on computation, the RSSI is required a，b Some processing is done. Thus, the average received signal strength RSSI of the terminal can be set under the indication of the access vector G average Factor RSSI for representing correlation of received signal strength values opt Can be expressed as:
RSSI opt ＝20+RSSI average ÷10 (4)
(2) Transmission cost: the transmission expense in the transmission process is related to the current transmission cost of the AP, the energy consumption of equipment, the quality of service (QoS) level of the service which can be provided, and the like.
In the present invention we will AP b Transmission overhead C of (2) b Set to be with AP b Energy consumption level E of (2) b And its currently capable service QoS class QoS b In relation, it can be expressed as:
where α and β are the impact factors of the energy consumption level and the traffic QoS level, respectively. E (E) b Expressed as energy consumption level according to the equipment energy consumption identification, E b The value of (2) may be set to a positive integer of 1 to 5. E (E) b The larger the value of (c) is, the lower the device energy consumption efficiency of the AP is, and the larger the transmission cost is. QoS (quality of service) b The value of (2) depends on the AP b Remaining computation, storage and channel resources. QoS (quality of service) b The value of (2) may be a positive integer of 1 to 10, and QoS a Corresponding to each other. QoS (quality of service) b The larger the value of (2) is, the more AP is described b The transmission condition of the service is good, and the transmission requirement of the service with high priority can be met under the condition of sufficient calculation force. Thus QoS b The value of (C) and C b Inversely proportional.
Average transmission overhead C of global terminals average The channel resource requirements that can be referenced to a terminal are expressed as:
in summary, the optimal scheduling model proposed by the present invention may be expressed as:
st RSSI a，b ≥RSSI threshold ，h a ＝b (8)
QoS a ≤QoS b ，h a ＝b (9)
wherein, gamma, delta and epsilon are used as weight factors to respectively and correspondingly adjust the service priority, average received signal strength and the influence of transmission cost on the switching scheduling. Equation (8) shows that in the handover schedule, sta a Is received by the handover target AP b Is greater than a signal strength threshold RSSI threshold . Equation (9) shows that in the handover scheduling, the AP b Priority of the traffic that can be carried needs to be equal to or higher than sta a Priority of the traffic transmitted up.
The switching scheduling model provided by the invention ensures the transmission quality of the electric power internet of things terminal by differentiating the service priority, considers the influence of signal strength and transmission expense on transmission, ensures the stability of service transmission, optimizes the global energy consumption condition and improves the transmission quality of a network.
In order to solve the AP switching scheduling problem based on WiFi6, the invention provides a switching scheduling method based on a genetic algorithm, and the genetic algorithm is utilized to realize switching scheduling decision based on service priority.
The genetic algorithm is a method for searching an optimal solution by simulating a natural evolution process, and the solving process of the problem is converted into a process similar to the crossing and mutation of chromosome genes in biological evolution. The genetic algorithm will randomly generate a group of individuals, form a population from the individuals, and perform crossover, mutation and selection operations on the individuals in the population, continuously update the optimized population and output the optimal individuals in the population.
The present invention therefore redefines some of the meaning of the genetic algorithm in the present invention for the AP handoff problem. In the present invention a handover access vector P, i.e. a handover scheme, may be defined as an individual. One of the terminals may be defined as a gene and the ligation target h of one terminal may be defined as a trait of one gene. Thus each individual has a total of N sta Genes, each gene having N ap A trait. We will set a set of vectors sg= { P i |1≤i≤N F The collection formed is called a population, where N P Number of individuals in population, P i Expressed as an individual in the population, i is the index corresponding to the individual.At this time individual P i With N sta Corresponding to N sta Personal traits, which can be expressed as
The purpose of the crossover operation is to obtain a new individual from two individuals so that the new individual possesses the traits of the two individuals. We redefine the crossover operator, assuming individual P at this time i-1 With individual P i Cross generation P i+1 ，N random Is greater than 1 and less than N ap The specific formula is expressed as follows:
in addition, the purpose of the selection operation is to eliminate individuals with low fitness in the population. The invention eliminates individuals with low adaptability by adopting a sorting-based method. The objective function of each individual is set to formula (7), and the limitations of formulas (8) (9) need to be satisfied. The smaller the objective function value of an individual, the better the adaptability of that individual. Considering that a new individual can be generated for every two individuals in the crossover operation, the population quantity is increased after the crossover operation, assuming that only one individual is generated between the two individualsAnd each. Therefore, in the present invention the selection operation will eliminate the +.>Individual, in order to ensure the population quantity unchanged.
The purpose of the mutation operation is to expand the search range of the algorithm and avoid the algorithm from sinking into the local optimal solution. The mutation operation may be controlled by a probability p, based on which a gene in the individual is randomly selected and a new genetic trait is randomly generated.
Therefore, the AP switching decision algorithm flow based on the genetic algorithm provided by the invention is as follows:
s1: randomly generating N P Individual individuals and make up a population.
The objective function of each individual is set to formula (7), and the limitations of formulas (8) (9) need to be satisfied.
S2: performing cross operation on individuals in the population two by two, and obtaining new productsIndividuals expand into a population. The crossover operation formula is shown as (10).
S3: and performing mutation operation on the expanded population to avoid falling into a local optimal solution.
If the individual after the crossover operation and the mutation operation does not satisfy the formulas (8) and (9), the individual is regenerated until the individual satisfies the constraint. And finally, selecting the population by taking the formula (7) as an optimization function, and eliminating a part of individuals. If the operation times do not meet the algorithm iteration times n, continuing to perform the crossover, mutation and selection operation to complete the iteration. If n iterations have been performed, the optimal individuals in the population are output. The algorithm flow is shown in fig. 1.
According to the invention, the proposed WiFi6 optimization switching method based on service priority is simulated through the NS-3 simulation platform, and 8 APs are arranged in a 150 m-150 m area. Let α be 1, β be 10, γ be 1, δ be 0.5, and ε be 600.
Fig. 2 and 3 show simulation comparison results of the optimized scheduling result and the average rate between the method of the present invention and the switching method (indicated by the Handover-RSSI in the figure) for making a switching decision based on the RSSI, respectively.
Fig. 2 shows a comparison of the results of the switching scheme function value of equation (7) in two methods. The value of the transmission cost factor in the function value is relatively low at the beginning because the number of terminals is not large. The function values of both methods continue to increase as the number of terminals increases. This means that the optimization effect of the handover decision is also continuously decreasing. However, the method of the invention has the performance obviously superior to that of the Handover-RSSI, the scheduling strategy has the best performance and quickly tends to be stable.
Figure 3 shows the average rate in the results of two method simulations. At a smaller number of terminals, the AP transmission pressure is smaller, and thus the average rate of both methods is higher. As the number of terminals increases, the average rate of both approaches continues to decrease. By differentiating the service priority, the average speed of the method is always superior to that of the Handover-RSSI, the transmission of the power service can be ensured, and the transmission quality of the global network is optimized.
According to the method, a resource division mode in the WiFi6 is combined, and analysis results show that the WiFi6 can distinguish service priority from a physical layer compared with the traditional WiFi protocol. Secondly, the invention designs a switching scheduling model based on service priority. Wherein the service priorities are more precisely differentiated by considering the resource partitioning manner. While taking the received signal strength and transmission overhead into account as factors affecting the handover schedule. Finally, aiming at the switching scheduling model, the invention designs a switching scheduling method based on a genetic algorithm. Simulation experiments show that the method provided by the invention can realize accurate switching of service priority and ensure the overall quality of power service transmission.
Further, the invention also provides a WiFi6 optimized switching decision system based on service priority, which comprises:
model construction module: constructing a WiFi6 switching optimization scheduling model based on service priority;
and a calculation scheduling module: and solving the optimal scheduling model based on a genetic algorithm to realize optimal switching scheduling based on service priority.
The invention also provides a terminal, which comprises a processor and a storage medium; the storage medium is used for storing instructions; the processor is operative to perform the steps of the method of the present invention in accordance with the instructions.
The invention also proposes a computer-readable storage medium on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the method according to the invention.
While the applicant has described and illustrated the examples of the present invention in detail with reference to the drawings of the specification, it should be understood by those skilled in the art that the above examples are only preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, but not limiting the scope of the present invention, but any improvements or modifications based on the spirit of the present invention should fall within the scope of the present invention.
Claims (14)
1. The WiFi6 optimized switching decision method based on the service priority is characterized by comprising the following steps:
s1: constructing a WiFi6 switching optimization scheduling model based on service priority; comprising the following steps:
s11: SSTA and SAP are built as sets of terminals and APs respectively,
SSTA＝{sta a |1≤a≤N sta }，
SAP＝{AP b |1≤b≤N ap }，
wherein N is sta And N ap The number of terminals and APs in SSTA and SAP, respectively, and a and b are the terminal stas in the set, respectively a And AP (Access Point) b Corresponding subscript, sta a Representing a plurality of terminals, AP b Representing a plurality of wireless access points;
s12: let h a Representing sta a Connected AP sequence number, h a =b, denote sta a Connecting to an AP b On the upper part, N sta The terminals are connected to N ap The optimal switching scheme of each AP is expressed as:
s13: the WiFi6 switching optimization scheduling model based on the service priority is constructed as follows:
stRSSI a，b ≥RSSI threshold ，h a ＝b
QoS a ≤QoS b ，h a ＝b
wherein gamma, delta and epsilon are taken as weight factors, and the service priority, the average received signal strength and the influence of transmission cost on the switching scheduling are respectively and correspondingly adjusted, and the RSSI opt A factor representing the correlation of the received signal strength value, z all As a utility function, C average Average transmission overhead for global terminals; RSSI (received signal strength indicator) a，b Representing a terminal sta a Connecting to a wireless Access Point (AP) b Is a received signal strength value of (1); RSSI (received signal strength indicator) threshold Representing a signal strength threshold; qoS (quality of service) a For the terminal sta a Traffic transmission priority, qoS of (c) b Representing wireless Access Point (AP) b Is a priority of service transmission;
s2: solving the optimal scheduling model based on a genetic algorithm to realize optimal switching scheduling based on service priority;
in which a utility function z is defined all The multi-service transmission case for prioritizing services in the global case is expressed as:
l(a)＝O a ×QoS a
o in a Representing a terminal sta a Minimum number of resource units allocated after handover scheduling, qoS a For the terminal sta a Traffic transmission priority, qoS of (c) a The value of (2) may be a positive integer of 1-10, qoS a The larger the value of (2) is, the description sta a The higher the traffic priority of (2);
wherein a factor RSSI is used to represent the correlation of received signal strength values opt The calculation method is as follows:
RSSI opt ＝20+RSSI average ÷10
wherein RSSI average Indication represented in access vector GThe lower terminal average received signal strength;
wherein the average transmission overhead C of the global terminal average Expressed as:
wherein O is a Representing a terminal sta a The minimum number of resource units allocated after the handover scheduling, C b Is AP b Is expressed as:
QoS b representing AP b Alpha and beta are the influencing factors of the energy consumption level and the service QoS level respectively, E b Expressed as energy consumption level according to the equipment energy consumption identification, E b The value of (2) may be set to a positive integer of 1-5.
2. The method according to claim 1, wherein the step S2 comprises:
s21: randomly generating N P Individual individuals and form a population;
s22: performing cross operation on individuals in the population every two, and expanding the obtained new individuals into the population;
s23: performing mutation operation on the expanded population to avoid sinking into a local optimal solution;
s24: and outputting the optimal individuals in the population after the iteration conditions are met.
3. The method of claim 2, wherein the step of determining the position of the substrate comprises,
in the step S21 of the above-mentioned process,
each individual has a total of N sta Genes, each gene having N ap Seed traits, a group of vectors is assembled into SG= { P i |1≤i≤N P The collection formed is called a population, in which
The objective function of each individual is set as the optimal scheduling model and the corresponding constraints in the model need to be met.
4. The method of claim 3, wherein the step of,
in the step S22, it is assumed that the individual P is present i-1 With individual P i Cross generation P i+1 ，N random Is greater than 1 and less than N ap The formula of the crossover operation is expressed as follows:。
5. the method according to claim 4, wherein the step S23 includes:
each two individuals in the crossover operation can generate a new individual, and the population quantity is increased after the crossover operation on the assumption that only one individual is generated between the two individualsEliminating the +.f. with the highest objective function value>Individual, in order to ensure the population quantity unchanged.
6. The method according to claim 5, wherein the step S24 includes:
if the operation times do not meet the algorithm iteration times n, continuing to perform crossover, mutation and selection operation to complete iteration; if n iterations have been performed, the optimal individuals in the population are output.
7. A service priority-based WiFi6 optimized handover decision system, the system comprising:
model construction module: constructing a WiFi6 switching optimization scheduling model based on service priority; the method specifically comprises the following steps:
SSTA and SAP are built as sets of terminals and APs respectively,
SSTA＝{sta a |1≤a≤N sta }，
SAP＝{AP b |1≤b≤N ap }，
wherein N is sta And N ap The number of terminals and APs in SSTA and SAP, respectively, and a and b are the terminal stas in the set, respectively a And AP (Access Point) b Corresponding subscript, sta a Representing a plurality of terminals, AP b Representing a plurality of wireless access points;
let h a Representing sta a Connected AP sequence number, h a =b, denote sta a Connecting to an AP b On the upper part, N sta The terminals are connected to N ap The optimal switching scheme of each AP is expressed as:
the WiFi6 switching optimization scheduling model based on the service priority is constructed as follows:
stRSSI a，b ≥RSSI threshold ，h a ＝b
QoS a ≤QoS b ，h a ＝b
wherein gamma, delta and epsilon are taken as weight factors, and the service priority, the average received signal strength and the influence of transmission cost on the switching scheduling are respectively and correspondingly adjusted, and the RSSI opt A factor representing the correlation of the received signal strength value, z all As a utility function, C average Average transmission overhead for global terminals; RSSI (received signal strength indicator) a，b Representing a terminal sta a Connecting to a wireless Access Point (AP) b Is a received signal strength value of (1); RSSI (received signal strength indicator) threshold Representing a signal strength threshold; qoS (quality of service) a For the terminal sta a Traffic transmission priority, qoS of (c) b Representing wireless Access Point (AP) b Is a priority of service transmission;
and a calculation scheduling module: solving the optimal scheduling model based on a genetic algorithm to realize optimal switching scheduling based on service priority;
defining utility function z all The multi-service transmission case for prioritizing services in the global case is expressed as:
l(a)＝O a ×QoS a
o in a Representing a terminal sta a Minimum number of resource units allocated after handover scheduling, qoS a For the terminal sta a Traffic transmission priority, qoS of (c) a The value of (2) may be a positive integer of 1-10, qoS a The larger the value of (2) is, the description sta a The higher the traffic priority of (2);
wherein a factor RSSI is used to represent the correlation of received signal strength values opt The calculation method is as follows:
RSSI opt ＝20+RSSI average ÷10
wherein RSSI average Representing the average received signal strength of the terminal under the direction of the access vector G;
wherein the average transmission overhead C of the global terminal average Expressed as:
wherein O is a Representing a terminal sta a In-handoffThe minimum number of resource units allocated after scheduling, C b Is AP b Is expressed as:
QoS b representing AP b Alpha and beta are the influencing factors of the energy consumption level and the service QoS level respectively, E b Expressed as energy consumption level according to the equipment energy consumption identification, E b The value of (2) may be set to a positive integer of 1-5.
8. The system of claim 7, wherein the computing scheduling module solves the optimal scheduling model based on a genetic algorithm to implement optimal switching scheduling based on service priority, and specifically comprises:
s21: randomly generating N P Individual individuals and form a population;
s22: performing cross operation on individuals in the population every two, and expanding the obtained new individuals into the population;
s23: performing mutation operation on the expanded population to avoid sinking into a local optimal solution;
s24: and outputting the optimal individuals in the population after the iteration conditions are met.
9. The system of claim 8, wherein the system further comprises a controller configured to control the controller,
in the step S21 of the above-mentioned process,
each individual has a total of N sta Genes, each gene having N ap Seed traits, a group of vectors is assembled into SG= { P i |1≤i≤N P The collection formed is called a population, in which
The objective function of each individual is set as the optimal scheduling model and the corresponding constraints in the model need to be met.
10. The system of claim 9, wherein the system further comprises a controller configured to control the controller,
in the step S22, it is assumed that the individual P is present i-1 With individual P i Cross generation P i+1 ，N random Is greater than 1 and less than N ap The formula of the crossover operation is expressed as follows:。
11. the system according to claim 10, wherein said step S23 includes:
each two individuals in the crossover operation can generate a new individual, and the population quantity is increased after the crossover operation on the assumption that only one individual is generated between the two individualsEliminating the +.f. with the highest objective function value>Individual, in order to ensure the population quantity unchanged.
12. The system according to claim 11, wherein said step S24 includes:
if the operation times do not meet the algorithm iteration times n, continuing to perform crossover, mutation and selection operation to complete iteration; if n iterations have been performed, the optimal individuals in the population are output.
13. A terminal, comprising a processor and a storage medium;
the storage medium is used for storing instructions;
the processor being operative according to the instructions to perform the steps of the method according to any one of claims 1-7.
14. Computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the method according to any of claims 1-7.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102185905A (en) * | 2011-04-20 | 2011-09-14 | 浙江工业大学 | WIFI-based bus-mounted data transmitting and dispatching method |
| WO2014129723A1 (en) * | 2013-02-24 | 2014-08-28 | 엘지전자 주식회사 | Method for exchanging frame for low power device and apparatus therefor in wireless lan system therefor |
| WO2016165392A1 (en) * | 2015-04-17 | 2016-10-20 | 华南理工大学 | Genetic algorithm-based cloud computing resource scheduling method |
| CN108880663A (en) * | 2018-07-20 | 2018-11-23 | 大连大学 | Incorporate network resource allocation method based on improved adaptive GA-IAGA |
| CN109618283A (en) * | 2019-01-23 | 2019-04-12 | 湖南大学 | A kind of vehicular ad hoc net mobile handoff system and method based on SDN |
-
2021
- 2021-11-04 CN CN202111300350.6A patent/CN114189914B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102185905A (en) * | 2011-04-20 | 2011-09-14 | 浙江工业大学 | WIFI-based bus-mounted data transmitting and dispatching method |
| WO2014129723A1 (en) * | 2013-02-24 | 2014-08-28 | 엘지전자 주식회사 | Method for exchanging frame for low power device and apparatus therefor in wireless lan system therefor |
| WO2016165392A1 (en) * | 2015-04-17 | 2016-10-20 | 华南理工大学 | Genetic algorithm-based cloud computing resource scheduling method |
| CN108880663A (en) * | 2018-07-20 | 2018-11-23 | 大连大学 | Incorporate network resource allocation method based on improved adaptive GA-IAGA |
| CN109618283A (en) * | 2019-01-23 | 2019-04-12 | 湖南大学 | A kind of vehicular ad hoc net mobile handoff system and method based on SDN |
Non-Patent Citations (3)
| Title |
|---|
| A novel QoS aware medium access control scheduler for LTE-advanced network;Saptarshi Chaudhuri;《Computer Networks》;全文 * |
| Mingqing Liu ; Gang Wang.Wireless Power Transmitter Deployment for Balancing Fairness and Charging Service Quality.《IEEE Internet of Things Journal 》.2019,全文. * |
| 基于网络虚拟化的WiFi网络切片技术研究;梁东明;《中国优秀硕士学位论文全文数据库 信息科技辑》;全文 * |
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