CN107454602B - Channel allocation method based on service type in heterogeneous cognitive wireless network - Google Patents
Channel allocation method based on service type in heterogeneous cognitive wireless network Download PDFInfo
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Abstract
The invention requests to protect a channel allocation method based on service types in a heterogeneous cognitive wireless network. The problem that channel resources allocated by secondary users are not matched with service types of the secondary users due to the fact that diversity and distinctiveness of frequency spectrum resources are not fully considered for current channel allocation is solved, firstly, different requirements of the secondary users with different service types on the channels are fully considered, meanwhile, interference caused by the secondary users on a main network is considered, a parameter S used for representing the whole channel allocation matching degree is designed, and the S is abstracted into a target function; and secondly, based on a high-efficiency improved genetic algorithm, carrying out optimization solution on the S objective function so as to obtain the maximum channel allocation matching degree. The experimental result shows that the algorithm can optimally distribute channel resources which accord with the service characteristics of a group of secondary users under the condition that the interference of the secondary users to the main network is as small as possible, and the overall satisfaction degree of the system is improved.
Description
Technical Field
The invention belongs to the field of mobile communication, and particularly relates to a channel allocation method based on service types.
Background
Channel allocation is an important matter of radio resource management. Compared with the traditional cognitive wireless network, different types of wireless access technologies in the heterogeneous cognitive wireless network have great differences in aspects such as system capacity, transmission rate, capability of meeting the QoS (quality of service) and the like, so that the heterogeneous cognitive wireless network has stronger diversity and dynamics, and the heterogeneous cognitive wireless network has to comprehensively consider the network selection problem in the traditional heterogeneous network and the channel allocation problem in the cognitive wireless network. Different algorithms have respective specific objective functions such as spectrum utilization, fairness, throughput and the like when channels are allocated to secondary users.
The channel allocation algorithm proposed in the document [ ALNWAIMI G, ARSHAD K, MOESSNER K. dynamic allocation algorithm with interference management in co-existing networks [ J ]. IEEE Communications Letters,2011,15(9):932-934] minimizes interference while maximizing spectrum utilization rate on the premise of guaranteeing QoS of secondary users. Since the channel allocation is a resource optimization allocation problem, the advantages of the intelligent optimization algorithm and the combination thereof are more prominent. The literature [ ZHAO Zhi-jin, PENG Zhen, ZHEN Shi-lian et al.Cognitic radio allocation using evolution algorithms [ J ]. IEEE Transactions on Wireless Communications,2009,8(9): 4421-. The document [ HASAN N, EJAZ W, EJAZ N, et al.Network selection and channel allocation for selecting a channel sharing in 5G terrestrial networks [ J ]. IEEE Access,2016,4: 980-.
Disclosure of Invention
The present invention is directed to solving the above problems of the prior art. A channel allocation method based on service types in a heterogeneous cognitive wireless network is provided, wherein the overall satisfaction degree of the system is improved. The technical scheme of the invention is as follows:
a channel allocation method based on service types in a heterogeneous cognitive wireless network comprises the following steps;
101. firstly, normalizing network attribute parameters including time delay D, jitter J, cost E and bandwidth B by utilizing dispersion standardization;
102. dividing the service types of the secondary users into real-time services and non-real-time services, and defining a network selection matching degree parameter S of comprehensive interference;
103. and (3) optimizing by using an improved genetic algorithm by taking the matching degree parameter S as an objective function to obtain an overall channel allocation result, wherein the genetic algorithm is improved as follows: when initializing chromosomes, randomly initializing the first N-1 chromosomes, setting the Nth chromosome as a group of solutions meeting constraint conditions, and after each iteration, if the maximum adaptive value of all chromosomes is smaller than the maximum adaptive value in the previous iteration, keeping the chromosomes with the maximum adaptive values in the previous generation to the next generation.
Further, the matching degree parameter S in step 102 is:
u is the number of secondary users in the cognitive network, fiThe specific network attribute parameters selected when the secondary user is subjected to channel allocation are represented as follows:
(1) when service (i) is 1, it indicates that the ith user service type is a real-time service, the real-time service pays more attention to the time delay and jitter parameters, and the algorithm comprehensively considers the interference factors, so that the interference, the time delay and the jitter are selected as the channel allocation standard of the secondary user of the real-time service:
fi＝(Ii,j+Di,j+Ji,j)/3 (2)
wherein Ii,j，Di,j，Ji,jRespectively representing interference, time delay and jitter corresponding to the ith user selection network j; (2) when service (i) is 2, it indicates that the ith user service type is non-real-time service, and the non-real-time service pays more attention to cost, bandwidth and other parameters, so that interference, cost and bandwidth are selected as the non-real-time service secondary user channel allocation standard:
fi＝(Ii,j+Ei,j+Bi,j)/3 (3)
wherein Ii,j，Di,j，Ji,jRespectively representing the interference, the cost and the total bandwidth corresponding to the ith user selected network j.
Further, the optimization with improved genetic algorithm using S as the objective function comprises
Subject to：
(1) The secondary users cannot be allocated to the channels occupied by the primary users;
(2) different secondary users cannot be assigned to the same channel.
Further, the network attribute parameters of step 101 may be divided into two categories: a benefit type parameter and a cost type parameter, wherein the benefit type parameter comprises a bandwidth; the cost-type parameters include time delay, jitter, and cost, and the normalization methods of the two types of parameters in step 101 respectively include:
benefit type parameters:
cost type parameters:
x is a set of parameters of the same type, xiIs a single parameter of a set of parameters,
for normalized parameters, the normalized parameters range from 0 to 1.
Further, in step 102, the service types of the secondary user are divided into two types, which are a real-time service and a non-real-time service, respectively, where the real-time service selects interference, delay and jitter as a channel allocation standard, and the non-real-time service selects interference, cost and bandwidth as a channel allocation standard.
Furthermore, the heterogeneous cognitive wireless network model is formed by crossing and overlapping coverage areas of various wireless access systems, a heterogeneous network environment is formed by N main networks, a certain number of channels are arranged under each main network, the channel characteristics are the same under the same network, and the spectrum sharing mode is Overlay.
The invention has the following advantages and beneficial effects:
the invention considers the channel allocation method under the heterogeneous cognitive wireless network environment aiming at the problem that the diversity and the distinguishability of frequency spectrum resources are not fully considered in the current channel allocation. According to different requirements of different secondary users on QoS parameters, an overall channel allocation matching degree parameter is designed to serve as an optimization objective function, channel resources which meet the service characteristics of the secondary users are allocated to the secondary users, and the overall satisfaction degree of the system is improved.
Drawings
FIG. 1 is a heterogeneous cognitive wireless network scenario of the present invention;
FIG. 2 is a comparison of the performance of an improved genetic algorithm versus a classical genetic algorithm;
FIG. 3 is a graph of channel assignment matching and algorithm iteration number;
FIG. 4 is a graph of average delay of secondary users of real-time services versus the number of iterations of the algorithm;
FIG. 5 is a graph of average jitter of secondary users of real-time services versus the number of iterations of the algorithm;
FIG. 6 is a graph of average cost of secondary users of non-real time services versus the number of iterations of the algorithm;
FIG. 7 is a graph of average bandwidth of secondary users of non-real-time traffic versus the number of iterations of the algorithm;
FIG. 8 is a graph of channel allocation match and primary user occurrence;
fig. 9 is a graph of channel allocation matching degree versus the number of secondary users.
Detailed Description
The technical solutions in the embodiments of the present invention will be described in detail and clearly with reference to the accompanying drawings. The described embodiments are only some of the embodiments of the present invention.
The technical scheme for solving the technical problems is as follows:
the method of the invention considers a plurality of attribute characteristics of the channel resources and the service types of the secondary users, maximizes the user service satisfaction degree by optimizing the matching degree of the service types of the secondary users and the allocated channel resources, and the algorithm can optimally allocate the channel resources meeting the service requirements of a group of secondary users on the premise of ensuring that the interference of the secondary users to the main network is as small as possible.
The channel allocation method provided by the invention comprises the following steps:
firstly, normalizing network attributes such as time delay D, jitter J, cost E and bandwidth B by utilizing dispersion standardization.
And step two, defining a network selection matching degree parameter S of comprehensive interference.
And step three, taking the S as a target function, and optimizing by using an improved genetic algorithm to obtain an overall channel allocation result.
In order to verify the invention, a simulation experiment is carried out on an MATLAB platform, and the following simulation scenes are set: the scene is distributed with 6 different types of main networks, the number of channels under each main network is 8, the network scene is shown in fig. 1, and the main network characteristic parameters are shown in table 1:
TABLE 1
The parameters of the genetic algorithm and the particle swarm algorithm in the simulation are set as shown in the table 2:
TABLE 2
In order to further highlight the superiority of the improved genetic algorithm in the invention, the genetic algorithm provided by the invention is compared with the classical genetic algorithm.
FIG. 2 is a graph comparing an improved genetic algorithm with a classical genetic algorithm. It can be seen that the adaptive value of the improved genetic algorithm always tends to rise with the increase of the number of iterations, while the classical genetic algorithm fluctuates and the adaptive value of the improved genetic algorithm is always higher than the latter. The penalty function is added when the adaptive value function is designed, the adaptive value corresponding to the solution which does not meet the constraint condition is very small, the initial population of the improved genetic algorithm contains the solution which meets the condition, and the optimal solution is reserved in each evolution, so the adaptive value of the improved genetic algorithm is very high at the beginning, and the population is evolved towards the direction of the optimal solution along with the progress of the iterative process. Thus, the improved genetic algorithm is due to the classical genetic algorithm, both in terms of operating efficiency and quality of the channel allocation solution.
In order to further highlight the superiority of the present invention, the improved genetic algorithm for distinguishing the service types provided by the present invention is compared and analyzed with the genetic algorithm and the particle swarm algorithm which do not distinguish the service types in the documents [ HASAN N, EJAZ W, EJAZ N, et al. network selection and channel allocation for spread mapping in 5G heterologous networks [ J ]. IEEE Access,2016,4: 980-.
Fig. 3 shows the channel allocation matching degree S versus the number of iterations. After the operation of the algorithms is finished, the channel allocation results of the four algorithms all meet the constraint conditions. With the increase of the iteration times, the S of each algorithm is continuously increased, and the algorithm is evolved towards a more optimal solution. Meanwhile, it can be seen that the convergence rates of several algorithms are equivalent, but the channel allocation matching degree S of the improved genetic algorithm for distinguishing service types of the present invention is always the largest, because the present invention distinguishes the service types of the secondary users, the attribute parameters considered when allocating channels to the secondary users with different service types are different, the corresponding matching is also higher, and the comparison algorithm does not distinguish the service types of the secondary users, the present invention can better allocate channels conforming to the service types of the secondary users to the different secondary users.
Fig. 4-7 show the secondary user QoS parameter versus the number of iterations.
Fig. 4 shows the relationship between the average delay of the real-time service sub-user and the number of iterations.
Fig. 5 shows the relation between the average jitter of the real-time service sub-users and the number of iterations.
Fig. 6 shows the average cost of non-real-time traffic sub-users versus the number of iterations.
Fig. 7 shows the relationship between the average bandwidth of the non-real-time service sub-users and the iteration number.
It can be seen from fig. 4-7 that the QoS parameters of the four algorithms are increased with the increase of the number of iterations, because the four algorithms consider the corresponding parameters when performing channel allocation, but the present invention has the highest cost for both the delay of the real-time service secondary user and the jitter or the bandwidth of the non-real-time service secondary user, because all the parameters are considered when performing channel allocation for the secondary users whose algorithms do not distinguish the service types in the comparison document, and the algorithm of distinguishing the service types of the present invention emphasizes the delay and the jitter on the real-time service and emphasizes the cost and the bandwidth on the non-real-time service, so that the present invention has higher pertinence and pertinence, and the corresponding QoS parameters are also the largest.
Fig. 8 shows the relationship between the channel allocation matching degree and the occurrence probability of the primary user. The occurrence probability of the main users is set to be 0-0.5. It can be seen from the simulation diagram that as the occurrence rate of the primary users increases, the S of the 4 algorithms decreases, but the present invention can obtain the highest S no matter which primary user occurs, because the increase of the occurrence rate of the primary users causes more channels to be occupied by the primary users, the channels with better characteristics left for the secondary users are reduced, and the number of the secondary users needing to allocate channels is fixed, so that a part of the secondary users can only be allocated to the channels with lower matching degree, and the present invention can better allocate the channels according with the service types of the secondary users by differentiating the service types of the secondary users.
Fig. 9 shows the channel allocation matching degree versus the number of sub-users. The number of the secondary users is set to be 10-20, the relation between the channel allocation matching degree and the number of the secondary users is obtained as shown in fig. 9, and as can be seen from a simulation graph, as the number of the secondary users increases, the S of the four algorithms is in a descending trend, but no matter how many the number of the secondary users is, the S obtained by the genetic algorithm for distinguishing the service types is superior to that of the other three algorithms. This is because the number of channels with good channel characteristics is certain, and each secondary user must allocate different channels, so that the more the number of secondary users, the more the number of secondary users allocated to channels with poor characteristics, and the lower the S, on the other hand, the invention distinguishes the service type of secondary users, and the result of channel allocation is more matched with the service type of secondary users, so the invention can better allocate channels according with the service type to secondary users.
The above examples are to be construed as merely illustrative and not limitative of the remainder of the disclosure. After reading the description of the invention, the skilled person can make various changes or modifications to the invention, and these equivalent changes and modifications also fall into the scope of the invention defined by the claims.
Claims (3)
1. A channel allocation method based on service types in a heterogeneous cognitive wireless network is characterized by comprising the following steps;
101. firstly, normalizing network attribute parameters including time delay D, jitter J, cost E and bandwidth B by utilizing dispersion standardization;
102. dividing the service types of the secondary users into real-time services and non-real-time services, and defining a network selection matching degree parameter S of comprehensive interference;
103. and (3) taking the matching degree parameter S as an objective function, and optimizing by using an improved genetic algorithm to obtain an overall channel allocation result, wherein the genetic algorithm is improved as follows: when initializing chromosomes, randomly initializing the first N-1 chromosomes, setting the Nth chromosome as a group of solutions meeting constraint conditions, and after each iteration, if the maximum adaptive value of all chromosomes is smaller than the maximum adaptive value in the last iteration, keeping the chromosomes with the maximum adaptive value in the previous generation to the next generation;
the matching degree parameter S of step 102 is:
u is the number of secondary users in the cognitive network, fiThe specific network attribute parameters selected when channel allocation is performed on the ith secondary user are represented as follows:
(1) when service (i) is 1, it indicates that the ith user service type is a real-time service, and selects interference, time delay and jitter as the distribution standard of the real-time service secondary user channel:
fi＝(Ii,j+Di,j+Ji,j)/3 (2)
wherein Ii,j，Di,j，Ji,jRespectively represent the ith user selectionSelecting interference, time delay and jitter corresponding to the network j;
(2) when service (i) is 2, it indicates that the ith user service type is non-real-time service, and selects interference, cost and bandwidth as the non-real-time service secondary user channel allocation standard:
fi＝(Ii,j+Ei,j+Bi,j)/3 (3)
wherein Ii,j，Ei,j，Bi,jRespectively representing the interference, cost and bandwidth corresponding to the ith user selection network j;
the optimization by using the matching degree parameter S as an objective function and using an improved genetic algorithm comprises
Subject to：
(1) The secondary users cannot be allocated to the channels occupied by the primary users;
(2) different secondary users cannot be assigned to the same channel;
in step 102, the service types of the secondary user are divided into two types, which are a real-time service and a non-real-time service, respectively, where the real-time service selects interference, delay and jitter as a channel allocation standard, and the non-real-time service selects interference, cost and bandwidth as a channel allocation standard.
2. The method for allocating channel based on service type in heterogeneous cognitive wireless network according to claim 1, wherein the network attribute parameters of step 101 can be divided into two categories: a benefit type parameter and a cost type parameter, wherein the benefit type parameter comprises a bandwidth; the cost-type parameters include time delay, jitter, and cost, and the normalization methods of the two types of parameters in step 101 respectively include:
benefit type parameters:
cost type parameters:
x is a set of parameters of the same type, xiFor a single parameter in a set of parameters, i represents the ith user,
for normalized parameters, the normalized parameters range from 0 to 1.
3. The method for allocating channel based on service type in the heterogeneous cognitive wireless network according to one of claims 1 to 2, wherein the heterogeneous cognitive wireless network model is formed by intersecting and overlapping coverage areas of various wireless access systems, a heterogeneous network environment is formed by N main networks, each main network has a certain number of channels, the channel characteristics are the same under the same network, and the spectrum sharing mode is Overlay.
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