CN104507168A - Distributed topology control method for cognitive Ad Hoc network - Google Patents

Distributed topology control method for cognitive Ad Hoc network Download PDF

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CN104507168A
CN104507168A CN201410829689.9A CN201410829689A CN104507168A CN 104507168 A CN104507168 A CN 104507168A CN 201410829689 A CN201410829689 A CN 201410829689A CN 104507168 A CN104507168 A CN 104507168A
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subgraph
topology
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CN104507168B (en
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王玺钧
盛敏
翟道森
张琰
李建东
郭彦涛
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Xidian University
CETC 54 Research Institute
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/54—Allocation or scheduling criteria for wireless resources based on quality criteria
    • H04W72/541—Allocation or scheduling criteria for wireless resources based on quality criteria using the level of interference
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
    • H04W40/246—Connectivity information discovery
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention discloses a distributed topology control method for a cognitive Ad Hoc network, and mainly solves the problems of mutual interference of secondary users and failure in guarantee of connectivity of networks of the secondary users in the prior art. An implementation process of the distributed topology control method for the cognitive Ad Hoc network includes: enabling each node in the network to sequentially broadcast a HELLO packet twice, and receiving the HELLO packet of initial neighbor nodes to set up a local two-hop topology subgraph; constructing a shortest path tree according to energy consumption link cost weight on the basis of the local two-hop topology subgraph, and on the basis of the shortest path tree, constructing a local generation subgraph capable of guaranteeing user communication; adjusting transmission power according to one-hop neighbor nodes in the local generation subgraph; enabling all nodes in the network and links between the nodes and the logical neighbor nodes thereof to form a whole network topology; after topology construction is finished, performing independent channel selection for the nodes in the network. The distributed topology control method for the cognitive Ad Hoc network has the advantages of low complexity and realization of guaranteeing communication of user networks and eliminating user interference, and is applicable to the cognitive Ad Hoc network.

Description

The distribution topology control method of cognitive Ad Hoc network
Technical field
The invention belongs to wireless communication field, particularly a kind of method building network topology structure, can be used for cognitive Ad Hoc network.
Background technology
Cognitive Ad Hoc network is the combination of cognition network and traditional Ad Hoc network, it is a kind of wireless network communications system being full of development potentiality, this network is except having the self-organizing of traditional Ad Hoc network, self-configuring, adaptive ability, also there is the perception to frequency spectrum resource, chance access and the ability of dynamic assignment, neatly for the various environment supported without fixed communication infrastructure, the utilance of existing band resource can be improved.
Among the many factors of the cognitive Ad Hoc network performance of impact, topology of networks is a very important importance, therefore how to optimize the topological structure of cognitive Ad Hoc network, strengthen network topology fault-tolerant ability and for upper layer communication agreement provide good bottom topological support be Topology Control research emphasis.In cognitive Ad Hoc network, user is divided into two classes, and a class is primary user, and another kind of is time user.Primary user enjoys the preferential right of channel.When primary user does not use channel, secondary user can use this channel.Because secondary user can only access channel to opportunistic, the connectedness of secondary user network is easily subject to the impact of primary user.When primary user will use certain channel, secondary user will vacate this channel to protect the proper communication of primary user and be in silent status, and node of mourning in silence can reduce the connectedness of time user network, can cause the segmentation of network time serious.In order to reduce the impact of primary user on secondary user network connectedness, researcher has proposed some Topology Control Algorithms, as the article " Robust TopologyControl in Multi-hop Cognitive Radio Network " that the authors such as Jing Zhao deliver on IEEE INFOCOM 2012, and the article " Generalized-Bi-Connectivity for Fault Tolerant Cognitive RadioNetwork " that the author such as Hai Liu delivers on IEEE ICCCN2012.The algorithm of Jing Zhao etc. can ensure the connection of time user network, but can not eliminate the interference between time user.The algorithm of Hai Liu etc. can eliminate the interference of time user further, but can not distributedly perform, and algorithm complex is high.
Summary of the invention
The object of the invention is to the problem for above-mentioned prior art, propose a kind of distribution topology control method of cognitive Ad Hoc network, ensure the connectedness of time user network, eliminate the interference between time user, reduce complexity.
To achieve these goals, network topology control method of the present invention comprises the steps:
(1) in network, each node u sends the HELLO-1 bag of oneself, and receives the HELLO-1 bag of a jumping neighbors transmission, and this HELLO-1 comprises ID sequence number and the positional information of u node;
(2) in network, each node u sends the HELLO-2 bag of oneself, and receives the HELLO-2 bag of a jumping neighbors transmission, and this HELLO-2 comprises ID sequence number and the positional information that one of u node jumps neighbors;
(3) in network, each node u builds the local double bounce topology subgraph of oneself
(3a) each node u in network, according to HELLO-1 and the HELLO-2 package informatin received, determines the annexation of oneself and double bounce neighbors, and the annexation between these neighborss, sets up local double bounce topology subgraph
(3b) according to local double bounce topology subgraph, each node u calculates any two node x having an annexation, the link energy consumption weight w between y in the double bounce topology subgraph of local p(x, y) and link range weight w d(x, y);
(4) in network, each node u builds local spanning subgraph S u=(V (S u), E (S u)):
(4a) each node u in network will local spanning subgraph S unode set V (S u) be initialized to local double bounce topology subgraph in all nodes, limit is gathered E (S u) be initialized to empty set;
(4b) based on local double bounce topology subgraph each node u is according to link energy consumption weight w p(x, y), building with u is root, the shortest path tree T of all nodes in local double bounce topology subgraph u=(V (T u), E (T u)), wherein for all nodes in local double bounce topology subgraph, E (T u) for forming all limits of shortest path tree, and these limits are recorded to locally connected subgraph S uin, namely
(4c) each node u in network is according to shortest path tree T ufind the node conflicted with oneself, form set CN u, and initialization conflict subgraph is CS u, wherein V (CS u)=CN u, E ( CS u ) = { ( x , y ) | x , y ∈ CN u , ( x , y ) ∈ E ( G u 2 ) } ;
(4d) each node u detects respective conflict connected subgraph CS uwhether be communicated with, if be communicated with, node u is then at CS uupper structure local spanning subgraph T u'; If be not communicated with, node u then exists upper structure stainer spanning tree T u'; If above-mentioned two steps all cannot perform, node u then knows that h jumps neighborhood information, builds local h and jumps topological subgraph and upper structure stainer spanning tree T u';
(4e) node u is by limit collection E (T u') be recorded to local spanning subgraph limit collection E (S u) in, namely by node V (T u') be recorded to local spanning subgraph set of node V (S u) in, namely by node V (T u') be recorded to logic conflict neighbours and collect LCN uin, i.e. LCN u=V (T u'), then node u passes through the mode of inundation LCN uwith E (S u) topology information send to S uin all nodes;
(4f) each node u upgrades the local spanning subgraph S of oneself according to the topology information that other nodes are sent ulCN is collected with logic conflict neighbours u, will local spanning subgraph S uon one jump neighbors v as logic neighbors, and form logic neighbors collection: LN u={ v ∈ V (S u) | (u, v) ∈ E (S u);
(5) in network, each node u determines the transmitting power of oneself, is adjusted to the minimum power that can cover required for all logic neighborss by transmitting power:
(6) all nodes in network and the link combinations between each node and the logic neighbors of oneself are got up, form final full mesh topology, i.e. G=(V (G), E (G)), wherein V (G) is nodes all in network, E (G)=and (u, v) | u ∈ V (G), v ∈ LN u;
(7), after topology constructing completes, each node u in network starts allocated channel:
(7a) node u collects LCN to logic conflict neighbours uin all nodes send request allocated channel bag RAC (Require Assignment Channel);
(7b) LCN uin all nodes receive RAC bag after, feedback channels distribute bag AC (AssignmentChannel) to node u, inform its channel distributed;
(7c) node u collects all LCN uin the AC bag of node feedback, select channel that also unappropriated, channel quality is best (or primary user's acquistion probability minimum channel), as the available channel of oneself;
(7d) each node disjoint performs said process, until all nodes all distribute channel.
Tool of the present invention has the following advantages:
1) joint Power of the present invention controls and channel allocation, controls to make the independent sets of time user network not form the cut set of network, thus ensure the connectedness of time user network by power; Distribute different channels to the secondary user of interference mutually by channel allocation, thus eliminate the interference between time user.
2) the present invention controls by power to construct the topology being suitable for channel allocation, and the connectedness avoiding high complexity judges, thus reduces the overall complexity of algorithm.And secondary user only needs local topology information, therefore algorithm can run in a distributed manner.
3) the present invention is owing to reducing the transmitting power of node, think the connectedness that ensures time user network and the number of channel needed for Lothrus apterus few.
Accompanying drawing explanation
Fig. 1 is the cognitive Ad Hoc network scene schematic diagram that the present invention is suitable for;
The maximum power topology that Fig. 2 is formed when being 50 meshed network scene;
Fig. 3 is flow chart of the present invention;
Fig. 4 is the sub-process figure building local spanning subgraph in the present invention;
Fig. 5 is the exemplary plot of interior joint u topology constructing of the present invention;
Fig. 7 is the simulating, verifying figure that the present invention generates topology;
Fig. 8 is the simulation comparison figure of the present invention and other Topology Control Algorithm average transmission radiuses;
Fig. 9 is the simulation comparison figure of the present invention and other Topology Control Algorithm average channel numbers and maximum channel number.
Embodiment
Below in conjunction with accompanying drawing, embodiment of the present invention is described in further detail.
With reference to Fig. 1, the cognitive Ad Hoc network that the present invention uses is made up of n the node be distributed in two dimensional surface region.Each node on behalf one time user, and there is unique ID sequence number, and it self positional information can be obtained by GPS or other location technologies.All nodes are subject to the impact of same primary user, and primary user can use any one channel in C channel.Each node can send data in any one channel in C channel, simultaneously interception data on other all channels, and in addition each node does not exist any difference in physical structure, initial setting up, functional characteristic, parameter index etc.In a network, the wireless channel between arbitrary node is additive white Gaussian noise channel.Node is communicated with surroundings nodes by omnidirectional antenna, and maximum transmission power is P max.The transmitting power P of arbitrary node u ucan regulate continuously between a minimum and a maximum, i.e. 0≤P u≤ P max.Transmission radius r is correspond to the transmission range of node transmitting power, and the necessary and sufficient condition that there is wireless link between any two nodes is the transmission radius r that Euclidean distance between them is less than or equal to node.When node each in network all uses the topological structure formed during maximum power transfer for maximum power topology, as shown in Figure 2, maximum power topological representation is: G max=(V (G max), E (G max)), wherein V (G max) be node set, represent network node, E (G max) be limit set, represent the wireless link existed between node.
With reference to Fig. 3, performing step of the present invention is as follows:
Step 1, in network, each node u sends the HELLO-1 bag of oneself, and receives the HELLO-1 bag of a jumping neighbors transmission.
Be positioned at all nodes of the transmission radius of node u, one of composition node u jumps neighbors collection VN u 1 = { v 1 ∈ V ( G max ) | ( u , v 1 ) ∈ E ( G max ) } , Wherein secondary user v 1be 1 jumping with the distance of secondary user u;
Each node u in network is with maximum transmission power P maxjump neighbors to one of u and broadcast a HELLO-1 bag, containing the ID sequence number of node u and the positional information of node u in HELLO-1 bag;
Each node u in network receives one and jumps neighbors with maximum transmission power P maxthe HELLO-1 bag of broadcast.
Step 2, according to the HELLO-1 bag in above-mentioned steps 1, in network, each node u sends the HELLO-2 bag of oneself, and receives the HELLO-2 bag of a jumping neighbors transmission.
All nodes that node u (comprises double bounce) and can arrive within double bounce, the double bounce neighbors collection of composition node u ∪ represents two unions of sets, & & represent and, wherein secondary user v 2be 2 jumpings with the distance of secondary user u;
After each node u in network receives the HELLO-1 bag of all jumping neighbors transmissions, with maximum transmission power P maxjump neighbors to one of u and broadcast a HELLO-2 bag, in HELLO-2 bag, jump ID sequence number and the positional information of neighbors containing all one of u;
Each node u in network receives one and jumps neighbors with maximum transmission power P maxthe HELLO-2 bag of broadcast.
Step 3, in network, each node u builds the local double bounce topology subgraph of oneself
(3a) each node u in network jumps HELLO-1 and the HELLO-2 package informatin of neighbors transmission according to receive one, obtains and records oneself all double bounce neighbors v 12iD sequence number and positional information, wherein v 12 ∈ VN u 2 ;
(3b) each node u is according to the positional information of the positional information of oneself and double bounce neighbors, calculates any two node x, the minimum emissive power P between y directly required for transmission x,y:
P x , y = βd x , y α
Wherein, β is received signal to noise ratio threshold value, and determine according to the sensitivity of receiver and bit error rate requirement, when Signal reception signal to noise ratio snr is greater than threshold value, this signal can be correctly received, and α is path-loss factor, d x,ynode x, the Euclidean distance between y;
(3c) according to the minimum emissive power calculated, the annexation between double bounce neighbors is judged, if P x,ybe less than the maximum transmission power P of node max, then determine node x, between y, there is annexation; Otherwise, there is not annexation between y in node x;
(3d) each node u is according to the annexation between double bounce neighbors, sets up local double bounce topology subgraph G u 2 = ( V ( G u ) , E ( G u ) ) , Wherein local double bounce topology subgraph node set be V ( G u 2 ) = VN u 2 ∪ { u } , { u} represents the set that node u forms, local double bounce topology subgraph limit set be: E ( G u 2 ) = { ( x , y ) | x , y ∈ V ( G u 2 ) , P x , y ≤ P max } , Namely for in any two node x, y, works as P x,y≤ P maxtime, limit (x, y) ∈ E (G u);
(3e) according to local double bounce topology subgraph, each node u calculates any two node x having an annexation, the link energy consumption weight w between y p(x, y):
w p(x,y)＝P x,y
Wherein, p x,yfor any two have the node x of annexation, the minimum transmit power between y directly required for transmission;
(3f) according to above-mentioned Euclidean distance and node ID sequence number, node u calculates any two node x having an annexation, the distance weighting w between y d(x, y):
w d(x,y)＝d x,y，
Wherein, d x,yany two node x having an annexation, the Euclidean distance between y.
Step 4, in network, each node u builds local spanning subgraph S u=(V (S u), E (S u)), and determine the logic neighbors of oneself.
Idiographic flow is as shown in Figure 4:
(4a) each node u in network will local spanning subgraph S unode set V (S u) be initialized to local double bounce topology subgraph in all nodes, namely limit is gathered E (S u) be initialized to empty set;
(4b) based on local double bounce topology subgraph with link energy consumption weight w p(x, y) is link weight, and node u is by using dijkstra's algorithm or bellman-ford algorithm, and building with u is root, throughout in the shortest path tree T of all nodes u=(V (T u), E (T u)), wherein for all nodes in local double bounce topology subgraph, E (T u) for forming all limits of shortest path tree, thus in subrange, obtain the shortest path arriving arbitrary node in the double bounce topology subgraph of local, and these limits are recorded to locally connected subgraph S uin, namely E ( S u ) ⇐ E ( T u ) ∪ E ( S u ) , represent assignment;
(4c) node u is according to shortest path tree T uthe node conflicted with oneself is found to form conflicting nodes collection CN u, and build conflict subgraph CS u, wherein V (CS u)=CN u, E ( CS u ) = { ( x , y ) | x , y ∈ CN u , ( x , y ) ∈ E ( G u 2 ) } ; The conflicting nodes collection of node u is defined as: along shortest path tree T uthe set that all nodes that double bounce can reach are formed, namely wherein N u 1 = { v | ( u , v ) ∈ E ( T u ) } ,
(4d) each node u detects respective conflict connected subgraph CS uwhether be communicated with, if be communicated with, node u is then at CS uupper structure local spanning subgraph T u'; If be not communicated with, node u then exists upper structure stainer spanning tree T u'; If above-mentioned two steps all cannot perform, node u then obtains h and jumps neighborhood information, builds local h and jumps topological subgraph and upper structure stainer spanning tree T u';
(4d1) node u detects conflict subgraph CS by using Kruskal algorithm or Prim algorithm uwhether be communicated with;
If (4d2) CS ube be communicated with, node u is then at CS uupper structure local spanning subgraph T u'.First, node u is by spanning subgraph T u' in set of node be initialized as CS uin all nodes, i.e. V (T u')=V (CS u), Jiang Bianji is initialized as E (T u')=(x, y) | x, y ∈ V (T u'), (x, y) ∈ E (T u); Then, node u is by CS uin all limits, by distance weighting w d(x, y) is link weight, sorts from small to large; Finally, node u judges CS in order successively uin two end points on each limit at T u' in whether be communicated with, if be not communicated with, join limit collection E (T u') in, otherwise, then do not add.Said process carries out always, until judged all limits, and generates final spanning subgraph T u'.
If (4d3) CS ube not communicated with, node u utilizes document V.J.Rayward-Smith and A.Clare, " Onfinding steiner vertices, " NETWORKS, vol.16, no.3, pp.283 – 294, the TMR algorithm in 1986. exists upper structure stainer (steiner) spanning tree T u'.Wherein, represent at local double bounce subgraph in delete node u and association thereof the subgraph that obtains of limit; At structure stainer spanning tree T u' time, V (CS u) in all nodes form fundamental segment point sets, and in node form Steiner-node collection;
If when (4d4) step (4c2) and (4c3) all cannot perform, node u obtains h by information interaction and jumps neighborhood information, builds local h and jumps topological subgraph and upper structure stainer spanning tree T u'.First, node u utilizes the method for step one and step 2 to know that h jumps the information of neighbors by sending Hello bag, and then, node u utilizes the method for step 3 to build local h and jumps topological subgraph finally, node u utilizes the method for step (4c3) to exist upper structure stainer spanning tree T u', at structure stainer spanning tree T u' time, V (CS u) in all nodes constitute fundamental segment point set, in node form Steiner-node collection;
(4e) node u is by limit collection E (T u') be recorded to local spanning subgraph limit collection E (S u) in, namely by node V (T u') be recorded to local spanning subgraph set of node V (S u) in, namely by node V (T u') be recorded to logic conflict neighbours and collect LCN uin, i.e. LCN u=V (T u'); Then, node u passes through the mode of inundation LCN uwith E (S u) topology information send to S uin all nodes;
(4f) each node u receives the topology information that other nodes send, and upgrades the local spanning subgraph S of oneself according to these topology informations ulCN is collected with logic conflict neighbours uif, the spanning subgraph S of any one node v received vin, about this edge is just joined S by the limit being linked to oneself uin, if LCN vin contain oneself, v is just recorded to oneself LCN by node u uin; Finally, node u will local spanning subgraph S uon one jump neighbors v as logic neighbors, and form logic neighbors collection: LN u={ v ∈ V (S u) | (u, v) ∈ E (S u);
With reference to Fig. 5, wherein scheme (a) and represent the local double bounce topology subgraph that node u builds by exchanging Hello-1 and Hello-2 bag figure (b) represents the shortest path tree T that node u utilizes energy consumption weight to build u; Figure (c) represents that node u is at T uin the conflict neighbor node collection CN that finds u; Figure (d) represents the conflict subgraph CS that node u builds u; By judging known CS ube communicated with, so node u directly performs step (4d2), at CS uupper structure local spanning subgraph T u', as shown in figure (e); The spanning subgraph S that node u finally builds uas shown in figure (f).
Step 5, the transmitting power of oneself, according to the above-mentioned logic neighbors determined, is adjusted to the minimum power required for the logic neighbors that can cover farthest by each node u namely
Step 6, according to above topology control procedure, each node disjoint in network determines the annexation with the logic neighbors of oneself, all nodes in network and the link combinations between each node and the logic neighbors of oneself are got up, form final full mesh topology, i.e. G=(V (G), E (G)), wherein V (G)=V (G max), E (G)=and (u, v) | u ∈ V (G), v ∈ LN u.
Step 7, after topology constructing completes, each node u in network selects transmitting channel.
(7a) node u maximum transmit power P maxbroadcast request allocated channel bag RAC on a common control channel, other nodes need this bag of transfer again when receiving this bag, until logic conflict neighbours collect LCN uin all nodes all receive RAC bag till;
(7b) LCN uin all nodes receive RAC bag after, check the channel oneself distributed, and feedback channels distribute bag AC to node u, wherein contain the channel that this node has distributed in channel allocation bag AC, if this node also unallocated channel just bag AC is designated as sky wrap.
(7c) node u collects all LCN uin the AC bag of node feedback, the channel selecting primary user's acquistion probability minimum from also unappropriated channel, as the available channel of oneself;
(7d) each node disjoint performs said process, until all nodes all distribute channel.
Effect of the present invention further illustrates by emulation:
(1) simulated conditions
In simulating scenes, network node is evenly distributed on a 1000 × 1000m at random 2two dimensional surface region in.The threshold value of received signal to noise ratio SNR is set to-80dBm, and path-loss factor α value is 4.In network, all nodes adopt identical maximum transmission power, wherein maximum transmission power P max=256mW, corresponding maximum transmitted radius R max=400m.Suppose that primary user can have influence on all secondary user nodes.
(2) content and result is emulated
Emulation 1, verifies that in the scene of 20 nodes topology that the inventive method generates can ensure the connectedness of secondary user.
Fig. 6 shows: figure a is that maximum power is topological, and figure b is the topology that the inventive method generates, the channel of numeral peer distribution wherein, and figure c is the conflict graph that figure b is corresponding, the topology of time user network when figure d, e, f are primary user's difference busy channel 4,5,6.Can be found out by this emulation, topology that the inventive method generates is when primary user takies arbitrarily a channel, and the network of secondary user remains connection.
Emulation 2, with the inventive method and document H.Liu, Y.Zhou, X.Chu, Y.-W.Leung, and Z.Hao, " Generalized-bi-connectivity for fault tolerant cognitive radio network; " in Proc.IEEEICCCN, Munich, Germany, Aug.2012, GBC, GBC+DC algorithm in pp.1 – 8. and maximum power topology MaxPower emulate node average transmission radius, and result as shown in Figure 7.
Fig. 7 shows: increasing with nodes, the average transmission radius of maximum power topology MaxPower remains unchanged, be 400m, and the average transmission radius of other algorithms constantly reduces, wherein the average transmission radius of the inventive method is minimum, in addition the inventive method can maintain the energy consumption shortest path of node end-to-end, and therefore the inventive method can well reduce the energy consumption of node, increases the life cycle of network.
Emulation 3, with the inventive method and GBC, GBC+DC algorithm and maximum power topology MaxPower to average needed for the number of channel emulate, result is as shown in Figure 8.
Fig. 8 shows: along with increasing of nodes, the average channel number of maximum power topology MaxPower linearly increases, and other algorithms remain unchanged substantially, and average channel number needed for the inventive method is minimum, comparing GBC algorithm decreases more than 40%, compares GBC+DC algorithm and decreases more than 17%.
Emulation 4, with the inventive method and GBC, GBC+DC algorithm and maximum power topology MaxPower maximum to institute needed for a number of channel emulate, result is as shown in Figure 9.
Fig. 9 shows: along with increasing of nodes, the maximum channel number of maximum power topology MaxPower linearly increases, GBC's and GBC+DC is comparatively large, and maximum channel number needed for the inventive method is minimum, compare GBC algorithm less more than 60%, compare GBC+DC algorithm and decrease more than 45%.Maximum channel number needed for network is fewer, illustrates that the robustness of algorithm is better, and GBC and GBC+DC algorithm possibly cannot normally perform when total number of channels is less, and robustness is better in the process of the present invention in institute.

Claims (9)

1. a distribution topology control method for cognitive Ad Hoc network, comprises the steps:
(1) in network, each node u sends the HELLO-1 bag of oneself, and receives the HELLO-1 bag of a jumping neighbors transmission, and this HELLO-1 comprises ID sequence number and the positional information of u node;
(2) in network, each node u sends the HELLO-2 bag of oneself, and receives the HELLO-2 bag of a jumping neighbors transmission, and this HELLO-2 comprises ID sequence number and the positional information that one of u node jumps neighbors.
(3) in network, each node u builds local double bounce topology subgraph
(3a) each node u in network, according to HELLO-1 and the HELLO-2 package informatin received, determines the annexation of oneself and double bounce neighbors, and the annexation between these neighborss, sets up local double bounce topology subgraph
(3b) according to above-mentioned local double bounce topology subgraph, each node u calculates any two node x having an annexation, the link energy consumption weight w between y in the double bounce topology subgraph of local p(x, y) and link range weight w d(x, y), w p(x, y) is by following formulae discovery: w p(x, y)=P x,y, wherein, P x,yit is minimum emissive power; w d(x, y) is by following formulae discovery: w d(x, y)=d x,y, wherein d x,yfor internodal Euclidean distance;
(4) according to above-mentioned local double bounce topology subgraph, in network, each node u builds local spanning subgraph S u=(V (S u), E (S u)):
(4a) each node u in network will local spanning subgraph S unode set V (S u) be initialized to local double bounce topology subgraph in all nodes, limit is gathered E (S u) be initialized to empty set;
(4b) based on local double bounce topology subgraph each node u is according to link energy consumption weight w p(x, y), building with u is root, the shortest path tree T of all nodes in local double bounce topology subgraph u=(V (T u), E (T u)), wherein for all nodes in local double bounce topology subgraph, E (T u) for forming all limits of shortest path tree, and these limits are recorded to locally connected subgraph S uin, namely
(4c) each node u in network is according to shortest path tree T ufind the node conflicted with oneself, form conflicting nodes collection CN u, and initialization conflict subgraph is CS u, wherein V (CS u)=CN u, E ( CS u ) = { ( x , y ) | x , y ∈ CN u , ( x , y ) ∈ E ( G u 2 ) } ;
(4d) each node u detects respective conflict connected subgraph CS uwhether be communicated with, if be communicated with, node u is then at CS uupper structure local spanning subgraph T u'; If be not communicated with, node u then exists upper structure stainer spanning tree T u'; If above-mentioned two steps all cannot perform, node u then obtains h and jumps neighborhood information, builds local h and jumps topological subgraph and upper structure stainer spanning tree T u';
(4e) node u is by limit collection E (T u') be recorded to local spanning subgraph limit collection E (S u) in, namely by node V (T u') be recorded to local spanning subgraph set of node V (S u) in, namely by node V (T u') be recorded to logic conflict neighbours and collect LCN uin, i.e. LCN u=V (T u'), then node u passes through the mode of inundation LCN uwith E (S u) topology information send to S uin all nodes;
(4f) each node u upgrades the local spanning subgraph S of oneself according to the topology information that other nodes are sent ulCN is collected with logic conflict neighbours u, will local spanning subgraph S uon one jump neighbors v as logic neighbors, and form logic neighbors collection: LN u={ v ∈ V (S u) | (u, v) ∈ E (S u);
(5) in network, each node u determines the transmitting power of oneself, is adjusted to the minimum power that can cover required for all logic neighborss by transmitting power:
(6) all nodes in network and the link combinations between each node and the logic neighbors of oneself are got up, form final full mesh topology, i.e. G=(V (G), E (G)), wherein V (G) is nodes all in network, E (G)=and (u, v) | u ∈ V (G), v ∈ LN u;
(7) according to above topology, each node u in network selects transmitting channel:
(7a) node u collects LCN to logic conflict neighbours uin all nodes send request allocated channel bag RAC (Require Assignment Channel) on a common control channel;
(7b) LCN uin all nodes receive RAC bag after, feedback channels distribute bag AC (AssignmentChannel) to node u, inform the channel selected;
(7c) node u collects all LCN uin the AC bag of node feedback, the channel selecting primary user's acquistion probability minimum from also unappropriated channel, as the available channel of oneself;
(7d) each node disjoint performs said process, until all nodes all distribute channel.
2. the distribution topology control method of cognitive Ad Hoc network according to claim 1, wherein in step (1) and the network described in step (2), each node u sends HELLO-1 and the HELLO-2 bag of oneself, refer to each node u in network, with maximum transmission power P maxa HELLO-1 and HELLO-2 bag is broadcasted respectively apart from all nodes of oneself transmission radius to being positioned at.
3. the distribution topology control method of cognitive Ad Hoc network according to claim 1, HELLO-1 and HELLO-2 that wherein step (1) and the initial neighbors of the reception described in step (2) send wraps, and refers to that each node u in network receives its neighbors with maximum transmission power P maxhELLO-1 and the HELLO-2 bag of broadcast.
4. the distribution topology control method of cognitive Ad Hoc network according to claim 1, determination oneself wherein described in step (3a) and the annexation of double bounce neighbors, and the annexation between these double bounce neighborss, set up local double bounce topology subgraph carry out as follows:
(3a1) each node u jumps HELLO-1 and the HELLO-2 package informatin of neighbors according to receive one, obtains and records ID sequence number and the positional information of HELLO-1 and HELLO-2 bag interior joint, these neighborss formation double bounce neighbors collection
(3a2) each node u is according to the positional information of the positional information of oneself and double bounce neighbors, calculates any two node x, the minimum emissive power P between y directly required for transmission x,y:
P x , y = β d x , y α
Wherein, β is received signal to noise ratio threshold value, and determine according to the sensitivity of receiver and bit error rate requirement, α is path-loss factor, and { u} represents the set that node u forms, d x,ybe node x, the Euclidean distance between y, if P x,ybe less than the maximum transmission power P of node max, then determine node x, between y, there is annexation; Otherwise, there is not annexation between y in node x;
(3a3) each node u is according to the annexation between double bounce neighbors, sets up local double bounce topology subgraph wherein local topology subgraph node set be local topology subgraph limit set be: E ( G u 2 ) = { ( x ^ , y ^ ) | x ^ , y ^ &Element; V ( G u 2 ) , P x ^ y ^ < P max } .
5. the distribution topology control method of cognitive Ad Hoc network according to claim 1, the shortest path tree in wherein said step (4b) builds by using dijkstra's algorithm or bellman-ford algorithm.
6. the distribution topology control method of cognitive Ad Hoc network according to claim 1, the stainer spanning tree in wherein said step (4d) is built by TMR algorithm.
7. the distribution topology control method of cognitive Ad Hoc network according to claim 1, wherein described in step (4e) LCN uwith E (S u) topology information send to S uin all nodes, be that node maximum transmit power in network adopts inundation mode to propagate topology information.
8. the distribution topology control method of cognitive Ad Hoc network according to claim 1, the node u wherein described in step (7a) collects LCN to logic conflict neighbours uin all nodes send request allocated channel bag RAC, being node maximum transmit power propagates RAC bag by the mode of inundation.
9. the distribution topology control method of cognitive Ad Hoc network according to claim 1, the LCN wherein described in step (7b) uin all nodes receive RAC bag after, feedback channels distribute bag AC to node u, be LCN uin all node maximum transmit powers by the mode of clean culture, bag AC is sent to node u.
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