CN107454602B - Channel allocation method based on service type in heterogeneous cognitive wireless network - Google Patents
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Abstract
本发明请求保护一种异构认知无线网络中基于业务类型的信道分配方法。对当前信道分配未充分考虑频谱资源的多样性和区分性，导致次级用户所分配的信道资源与其业务类型不匹配的问题，首先充分考虑不同业务类型的次用户对信道的需求不同，同时兼顾次用户对主网络所造成的干扰，设计一个用来表征整体信道分配匹配度的参数S，并把S抽象成目标函数；其次，基于高效率的改进遗传算法，对S目标函数进行最优化求解，以期获得最大化信道分配匹配度。实验结果表明该算法能在次用户对主网络造成的干扰尽可能小的条件下，优化地为一组次用户分配符合其业务特征的信道资源，系统的整体满意度得到了提升。
The present invention claims to protect a channel allocation method based on a service type in a heterogeneous cognitive wireless network. The current channel allocation does not fully consider the diversity and differentiation of spectrum resources, resulting in the problem that the channel resources allocated by secondary users do not match their service types. First, fully consider the different channel requirements of secondary users of different service types, and at the same time The interference caused by secondary users to the main network, a parameter S is designed to represent the overall channel assignment matching degree, and S is abstracted into an objective function; secondly, based on an efficient genetic algorithm, the objective function of S is optimized and solved , in order to maximize the matching degree of channel assignment. The experimental results show that the algorithm can optimally allocate channel resources in line with the service characteristics of a group of secondary users under the condition that the interference caused by the secondary users to the primary network is as small as possible, and the overall satisfaction of the system has been 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.
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