CN103618674A - A united packet scheduling and channel allocation routing method based on an adaptive service model - Google Patents
A united packet scheduling and channel allocation routing method based on an adaptive service model Download PDFInfo
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Abstract
The invention provides a united packet scheduling and channel allocation routing method based on an adaptive service model. The method comprises a cognitive information database, an inference learning database, and an adaptive route scheduler which are connected successively. The cognitive information database is used for acquiring and modeling cognitive information. The inference learning database is used for predicting and inferring a resource distribution sub decision. The adaptive route scheduler is used for making a global optimal decision for united packet scheduling and channel allocation and controlling the allocation of whole network resources. The method makes the global optimal routing decision with a united packet scheduling and channel allocation algorithm, may substantially enhance the service quality of different multimedia businesses in a mobile cognitive wireless network, and adjusts a decision model in a mobile cognitive wireless network protocol highly dynamically and adaptively. The separation of the cognitive information and the decision model from the route enables the algorithm to be easily expanded and maintain so as to give an impetus and support to a mobile cognitive wireless network QoS routing method with a unified standard.
Description
Technical field
The invention belongs to field of computer technology, especially be applied to the associating packet scheduling based on self adaptation service model and the method for routing of channel allocation under mobile environment of cognitive radio network, be specifically related to a kind of associating packet scheduling and channel allocation method for routing based on self adaptation service model.
Background technology
Cognitive radio is a kind of new technology of expanding on the basis of software radio, and Joseph doctor Mitola as far back as 1999 Nian You Sweden KTHs proposes.Traditional fixed frequency spectrum distribution system has been broken in the appearance of cognitive radio technology, the current wireless environment of its perception in real time, find out the mandate frequency spectrum not being used, by opportunistic spectrum, access, dynamically utilize these " frequency spectrum holes ", realize the secondary utilization of authorizing frequency spectrum, improved the utilance of authorizing frequency spectrum, effectively alleviated the problem being becoming tight radio spectrum resources day.In cognition wireless network, there are two kinds of users, primary user (Primary User, PU) and time user (Secondary User, SU).Primary user has distributed the authorized user of fixed frequency range, and it has preferential right to frequency spectrum.Inferior user is also called cognitive user, and it is primary user's communication not to be caused under the prerequisite of interference, the unauthorized user that dynamically insertion authority frequency spectrum, utilization " frequency spectrum hole " communicate.
Traditional cognition wireless network provides the single service of doing one's best for data transmission service, and along with some multimedia services are as the continuing to bring out of video request program, IP phone, the service quality of network (QoS) has been proposed to the requirement of different levels.For example real-time voice business need propagation delay time and shake are little, allow to exist certain mistake; And data transmission service requires reliability high, general to delay requirement.In addition, in mobile cognition wireless network, the translational speed of each node, Move Mode, access frequency spectrum are all random, and this just causes network topology, channel width may change at any time.The dynamic of mobile wireless cognition network inherence has determined that it is difficult to meet the QoS requirement of different business.How according to different QoS of survice demands, to provide different services is that mobile cognition wireless network is endeavoured the difficult point solving all the time, and comprehensively the cognitive information of each layer is carried out the key technology that Route Selection is solution QoS.
Through the retrieval of prior art document is found, the research of the current QoS method for routing for mobile cognition wireless network is also in the starting stage, and the generally acknowledged model of neither one.He Qing and Zhou Huaibei (He Qing, Zhou Huaibei.Research on the routing algorithm based on Qos requirement forcognitive radio networks[C] .2008International Conference on Computer Science and SoftwareEngineering.2008) according to the difference of channel capacity and time delay, joint network layer and MAC layer adopt the thought of minimum spanning tree to find out best routed path, for reducing the time delay of whole route data repeating process.This QoS routing algorithm is attempted between channel capacity and time delay, to find a balance point, finally draws a Route Selection with best Q oS performance.But the QoS performance of network not merely refers to a kind of so single performance of time delay, the different different occasions that are applied in have different performance requirements.In addition, in this QoS routing mechanism, cognitive information and control information are merged, make agreement be difficult to expansion and safeguard.Cognition wireless network must distribute required Internet resources adaptively according to different QoS of survice demands, different network environments, with this, reach the optimization distribution of Internet resources and the raising of network service quality, thereby mobile cognition wireless network needs the method for routing of new expandable resource.
Summary of the invention
Mobile cognition wireless network must be selected route adaptively according to dynamic networking situation, usable spectrum and different QoS of survice demands, with this, improves user satisfaction and network resource utilization.Therefore, for the transmission demand of finishing service within the shortest time, must combine and consider packet scheduling and spectrum allocation may based on QoS of survice, carry out Route Selection.
Based on above understanding and for weak point of the prior art, the object of the invention is to overcome existing mobile cognition wireless network resource allocation mechanism poor expandability, the weak deficiency of adaptive ability, for QoS of survice diversified demand, cognition wireless network is changeable feature dynamically, proposes the associating packet scheduling based on self adaptation service model and the method for routing of channel allocation in a kind of mobile cognition wireless network.The present invention is by the different levels demand of business, network condition, usable spectrum unified Modeling, when selecting route, consider scheduling and channel strategy, make global optimum's routing decision, thereby reached the object that meets adaptively different business QoS demand, improves network overall quality of service.
According to associating packet scheduling and the channel allocation method for routing based on self adaptation service model provided by the invention, comprise the cognitive information storehouse, reasoning learning database and the self adaptation routing scheduling device that connect successively, wherein:
Cognitive information storehouse obtains and modeling for cognitive information;
Reasoning learning database is divided prediction and the reasoning of gamete decision-making for resource;
Self adaptation routing scheduling device is used for associating packet scheduling and channel allocation is made global optimum's decision-making, controls the distribution of whole Internet resources.
Preferably, described cognitive information storehouse comprises QoS of survice demand information, network node information, usable spectrum information, wherein:
QoS of survice demand information comprises propagation delay time, delay variation, throughput and packet loss;
Network node information comprises the neighbor information of node;
Usable spectrum information comprises distribution condition, interference constraints, the benefit situation of frequency spectrum.
Preferably, described reasoning learning database comprises packet scheduling policy module and spectrum allocation may policy module, wherein:
Packet scheduling policy module, for adjusting in real time a business and the shared weight of each index according to the situation of change of current network, is dynamically adjusted each packet residing position in waiting list;
Spectrum allocation may policy module is for predicting the spectrum allocation may of next round according to current spectrum allocation may situation.
Preferably, described self adaptation routing scheduling device is the effector of Resource Allocation in Networks, controlling each packet at scheduling, route, the transmission channel of network node, described self adaptation routing scheduling device according to the information self-adapting of current network node and neighbor node adjust packet scheduling strategy and spectrum allocation may strategy, from candidate's set of data packets, find out the packet of applicable scheduling, from the distributed frequency spectrum of prediction, find out available channel, thus associating packet scheduling and channel allocation by the packet of present node from optimum route transmission.
Compared with prior art, the present invention has following beneficial effect: the routing policy generating by associating packet scheduling and channel allocation within the shortest time is the comprehensive optimal solution of the overall situation, met the QoS demand of different business, reduce the possibility of rebuilding route simultaneously, therefore can greatly promote the service quality of network.In addition, separated with combined dispatching by cognitive information, reasoning self-decision, make this self adaptation service model be more prone to expansion and safeguard.
Accompanying drawing explanation
By reading the detailed description of non-limiting example being done with reference to the following drawings, it is more obvious that other features, objects and advantages of the present invention will become:
Fig. 1 is the interaction concept schematic diagram of self adaptation service model and conventional wireless network agreement.
Embodiment
Below in conjunction with specific embodiment, the present invention is described in detail.Following examples will contribute to those skilled in the art further to understand the present invention, but not limit in any form the present invention.It should be pointed out that to those skilled in the art, without departing from the inventive concept of the premise, can also make some distortion and improvement.These all belong to protection scope of the present invention.
Self adaptation service model (AdaptiveService) comprises cognitive information storehouse, reasoning learning database and self adaptation route, as shown in Figure 1.Wherein: cognitive information storehouse obtains and modeling for cognitive information, reasoning learning database is divided prediction and the reasoning of gamete decision-making for resource, and self adaptation route associating packet scheduling and channel allocation are made global optimum's routing decision, control the distribution of whole Internet resources.The key component of self adaptation service model is self adaptation route, by mutual a series of cognitive information, is controlling the assigning process of Internet resources.Specifically comprise three phases: (1) cognitive information is obtained and modeling.Each layer of cognitive information of each network node joins in cognitive information storehouse and unified Modeling.(2) resource is divided gamete decision-making prediction and reasoning.According to the cognitive information in cognitive information storehouse, generate corresponding packet scheduling strategy and channel assignment strategy, and corresponding Candidate Set.(3) global optimum's routing decision generates.Packet based on selecting in Candidate Set and available channel, select next-hop node.
Described cognitive information storehouse comprises QoS of survice demand information, network node information, usable spectrum information.Wherein: QoS of survice demand information comprises propagation delay time, delay variation, throughput and packet loss, different business shared weighted in network, the shared weight of each demand parameter in each business is also different, can obtain like this priority of each business by weighting.What network node information was stored is the neighbor information of node, is the important references information of carrying out Route Selection.Usable spectrum information comprises distribution condition, interference constraints, the benefit situation of frequency spectrum.
Described reasoning learning database comprises packet scheduling strategy and spectrum allocation may strategy.Wherein: packet scheduling strategy is the foundation of each node scheduling packet, it adjusts a business and the shared weight of each index in real time according to the situation of change of current network, dynamically adjusts each packet residing position in waiting list; Spectrum allocation may strategy is according to the spectrum allocation may of current spectrum allocation may situation prediction next round.
Described self adaptation route is the effector of Resource Allocation in Networks, is controlling each packet at scheduling and the transmission channel of network node.It according to the information self-adapting of current network node and neighbor node adjust packet scheduling strategy and spectrum allocation may strategy, from candidate's set of data packets, find out the packet of applicable scheduling, from the distributed frequency spectrum of prediction, find out available channel, thereby associating packet scheduling and channel allocation are selected optimum route.
Below in conjunction with specific embodiment, technical scheme of the present invention is further elaborated.Whole invention implementation procedure is as follows:
1. cognitive information obtains and modeling
(1) QoS of survice
QoS of survice demand information comprises that propagation delay time, delay variation, throughput and packet loss describe.By these being affected to the synthetic determination of the factor of QoS, provide the priority of each business.
A={a
1, a
2..., a
n, a
i(i=1,2,3 ..., n) represent different business, as voice, video, data etc.
T={t
1, t
2..., t
n, t
i(i=1,2,3 ..., n) represent different business shared weight in network.
qoS level other judgement index and weight
C={c
1, c
2..., c
nrepresent that respectively QoS adjudicates index: propagation delay time, delay variation, throughput, packet loss.Each business to QoS index require different, so each index shared weight under each business is different.If respectively adjudicate the weight coefficient of index in each business, be expressed as W
i=[w
i1, w
i2, w
i3, w
i4] represent respectively propagation delay time, delay variation, throughput, packet loss shared weight coefficient in i kind business.
each service priority
According to each business weight and each QoS index weights, be weighted, obtain the priority S of each business
ifor:
(2) network node information
By neighbor node list storage from the relevant information with this node adjacent node, shown in following network node list:
| NID | NChannel | NWLength | NPostition | VelocityVector | ExpireTime |
NID represents neighbor node address; NChannel represents the frequency range of neighbor node work; NWLength such as represents in neighbor node at the queue length of pending grouping; NPositinon records the geographical position of neighbor node; VelocityVector represents the velocity of neighbor node; ExpireTime represents the life span of neighbor node.
(3) usable spectrum
Suppose to have N cognitive user, M usable spectrum, the usable spectrum information in cognitive storehouse is as follows:
L={l
n,m| l
n,m∈ { 0,1}}
n * Mrepresent the distribution condition of frequency spectrum to cognitive user, l
n,m=1 represents that cognitive user n can be used channel m
B={b
n,m| b
n,m>=0}
n * M, represent the income that cognitive user n obtains on channel m.
C={c
n,m| c
n,m>=0}
n * M, represent to produce on channel m with cognitive user n the number of users conflicting.
I={i
n, k, m| i
n, k, m∈ { 0,1}}
n * N * M, represent a certain available channel, if different cognitive user is used simultaneously, between cognitive user, may produce interference.I
n, k, m=1 represents to produce interference.
2. resource is divided prediction and the reasoning of gamete decision-making
(1) packet scheduling strategy
When considering packet scheduling strategy, also must consider the stand-by period of current group, in order to avoid the business of some low priority can not get scheduling in starvation.The dispatching priority that obtains thus each traffic packets is Q
i=cS
i+ (1-c) R
i, wherein c is adjustment factor, R
ifor the stand-by period of grouping.Packet scheduling implementation of strategies process is as follows:
The first step, the more stand-by period of all groupings on new node.
Second step, more the priority Q of all groupings on new node
i=cS
i+ (1-c) R
i.
The 3rd step, adjusts each according to up-to-date packet-priority and is grouped in the position on waiting list, according to priority sequence from high to low.
The 4th step, chooses N the grouping that priority is the highest and divides into groups as candidate.
(2) spectrum policy
Concrete implementation process is as follows:
The first step, creates usable spectrum matrix L, benefit matrix B, collison matrix C, interference constraints matrix I.
Second step, according to usable spectrum matrix L, collison matrix C, interference constraints matrix I creates noiseless allocation matrix T.
The 3rd step, according to noiseless allocation matrix T and benefit matrix B, creates and optimizes allocation matrix R.
The 4th step, according to optimizing allocation matrix R according to service priority allocated channel.
The 5th step, upgrades usable spectrum matrix L, collison matrix C, interference constraints matrix I, optimizes allocation matrix R, carries out next round spectrum allocation may.
3. global optimum's routing decision generates
Self adaptation route is the core of self adaptation service model, when receive from application layer a service request time, drive concurrently packet scheduling strategy and the spectrum policy of reasoning learning database, global optimum's routing decision is made in the sub-decision-making of candidate that then learning database is returned by inference.Concrete implementation process is as follows:
The first step, self adaptation route is waited for the service request from application layer, when having grouped data to transmit, from corresponding candidate, dispatches set of packets and usable spectrum collection is selected packet and usable spectrum; During this time, if any one Candidate Set is empty, turn second step;
Second step, calls corresponding sub-decision-making, generates the Candidate Set under current state, and the Candidate Set of generation is returned to self adaptation route.Self adaptation route is that next-hop node is selected in packet according to current cognitive information and Candidate Set.
The selection of next-hop node has following several principle: (1) distance is the shortest, (2) link length the most stable, (3) waiting list is the shortest, (4) channel is the most stable.The distance d of present node and neighbor node
irepresent; If present node and neighbor node are mutually near showing that link is more stable, if away from showing that link is unstable, use rd
irepresent the relative motion situation between two nodes; The waiting list length wl of packet in neighbor node
irepresent, according to the preoption value of formula neighbor node, can be expressed as: p
i=c
1d
i+ c
2rd
i+ (1-c
1-c
2) wl
i, c
1, c
2for adjustment factor, priority valve minimum and and present node between in optimizing allocation matrix R, to have the neighbor node of channel will be next-hop node.
Above specific embodiments of the invention are described.It will be appreciated that, the present invention is not limited to above-mentioned specific implementations, and those skilled in the art can make various distortion or modification within the scope of the claims, and this does not affect flesh and blood of the present invention.
Claims (4)
1. the associating packet scheduling based on self adaptation service model and a channel allocation method for routing, is characterized in that, comprises the cognitive information storehouse, reasoning learning database and the self adaptation routing scheduling device that connect successively, wherein:
Cognitive information storehouse obtains and modeling for cognitive information;
Reasoning learning database is divided prediction and the reasoning of gamete decision-making for resource;
Self adaptation routing scheduling device is used for associating packet scheduling and channel allocation is made global optimum's decision-making, controls the distribution of whole Internet resources.
2. associating packet scheduling and the channel allocation method for routing based on self adaptation service model according to claim 1, is characterized in that, described cognitive information storehouse comprises QoS of survice demand information, network node information, usable spectrum information, wherein:
QoS of survice demand information comprises propagation delay time, delay variation, throughput and packet loss;
Network node information comprises the neighbor information of node;
Usable spectrum information comprises distribution condition, interference constraints, the benefit situation of frequency spectrum.
3. associating packet scheduling and the channel allocation method for routing based on self adaptation service model according to claim 1, is characterized in that, described reasoning learning database comprises packet scheduling policy module and spectrum allocation may policy module, wherein:
Packet scheduling policy module, for adjusting in real time a business and the shared weight of each index according to the situation of change of current network, is dynamically adjusted each packet residing position in waiting list;
Spectrum allocation may policy module is for predicting the spectrum allocation may of next round according to current spectrum allocation may situation.
4. associating packet scheduling and the channel allocation method for routing based on self adaptation service model according to claim 1, it is characterized in that, described self adaptation routing scheduling device is the effector of Resource Allocation in Networks, controlling each packet in the scheduling of network node, route, transmission channel, described self adaptation routing scheduling device according to the information self-adapting of current network node and neighbor node adjust packet scheduling strategy and spectrum allocation may strategy, from candidate's set of data packets, find out the packet of applicable scheduling, from the distributed frequency spectrum of prediction, find out available channel, thereby associating packet scheduling and channel allocation by the packet of present node from optimum route transmission.
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