CN103179632B

CN103179632B - Based on energy-optimised and network life cross-layer routing method in cognitive radio cellular network network

Info

Publication number: CN103179632B
Application number: CN201310023802.XA
Authority: CN
Inventors: 吴启晖; 宋绯; 张尧然; 徐煜华; 程云鹏; 郑学强
Original assignee: COMMUNICATION ENGINEERING COLLEGE SCIENCE & ENGINEEIRNG UNIV PLA
Current assignee: COMMUNICATION ENGINEERING COLLEGE SCIENCE & ENGINEEIRNG UNIV PLA
Priority date: 2013-01-23
Filing date: 2013-01-23
Publication date: 2016-06-29
Anticipated expiration: 2033-01-23
Also published as: CN103179632A

Abstract

Based on energy-optimised and network life cross-layer routing method in a kind of cognitive radio cellular network network, relate to the cognition wireless electrical domain in wireless communication technology, different dynamic spectrum resources is had for cognitive user each in cognitive radio cellular network network, and the feature that wireless cognition node energy is limited, the present invention proposes new route metric standard, and by adopting cross-layer technology, associating consideration is distributed to the Route Selection in cognition network and channel, shortest-path first algorithm is transformed, avoid respective nodes overload, exhaust the problem that energy causes losing efficacy in advance, delay the node death time, extend network life, simultaneously because the existence of more effective nodes, cognitive nodes is made to have more via node to select, thus decreasing average energy consumption on the whole, improve communication access success rate.

Description

Based on energy-optimised and network life cross-layer routing method in cognitive radio cellular network network

Technical field

The present invention relates to the cognition wireless electrical domain in wireless communication technology, be especially one in conjunction with cross-layer technology, the link-state routing algorithm of application enhancements and new dynamic routing module, in cognitive radio networks, carry out the new method of routing.

Background technology

At present, along with to improving constantly of wireless communication needs and quickly increasing of radio communication service kind, the demand of radio spectrum resources is also exponentially increased by people, and this makes " frequency spectrum is deficient " in radio communication contradiction between problem and existing fixed frequency spectrum allocative decision ineffective assignment.Cognitive radio technology is as a kind of emerging technology being intended to improve spectrum utilization efficiency, authorized user (primaryuser can ensured, be called for short PU) service quality when, allow cognitive user or secondary user's (secondaryuser, it is called for short SU) in the way of dynamic access, utilize the idle frequency range of authorized user, can effectively solve the effective ways of the problem of " frequency spectrum is deficient ", there is important practical significance and wide application prospect.

Based in cognitive radio cellular network network, by adopting multi-hop transmission, the coverage of single cognition network base station can be significantly expanded, but owing to the geographical position of PU is different with working frequency range, cause each SU frequency spectrum resource that can use can in time, place and change, thus cognitive radio networks and general multi-channel wireless network are distinguished.This also makes us must must account for the frequency spectrum resource isomerism of each SU when selecting multi-hop relay node, should improve spectrum utilization efficiency, avoid the mutual interference between the interference to PU and SU again.

In the wireless network, base station generally has stronger electric power support, but radio node mostly adopts battery to power, but under current technical conditions, the energy that battery can be provided by is limited and preciousness, once the running down of battery of one or more node, may result in the inefficacy of node, the decline of network performance, the inefficacy of even whole network.Therefore, how when communicating, the node resource such as reasonable distribution frequency spectrum, link, energy, thus while meeting telex network demand, lowering energy consumption as much as possible, extending network life, being a problem demanding prompt solution.

In traditional network model, between each layer, function distinguishing is clear and definite, all information only flow at adjacent interlayer, but in cognitive radio, use due to the change of frequency spectrum resource and dynamic access technology, when selecting route, the user's request of the power control, channel distribution and the top layer that consider bottom must be combined, this necessity of making use of momentum breaks the information flow obstacle between each layer, introduce cross-layer technology rather popular in recent years, the converging information of each layer is carried out complex optimum to coming together, just can obtain gratifying performance.

Summary of the invention

The purpose of the present invention is to propose to a kind of cross-layer routing algorithm being applied to cognitive radio cellular network network, its purpose is under the premise meeting telex network rate requirement, reduce the energy expenditure of radio node, extend network life, improve network performance.

The technical scheme is that

Based on energy-optimised and network life cross-layer routing method in a kind of cognitive radio cellular network network, it includes: network status initialization, obtains the common signal channel collection C of each node_ijStep；By calculating every energy consumption metric that can lead to link, choose cognitive user nodes and arrive the step of the minimum route of the energy consumption of cognitive base station；The step of channel distribution is carried out for each section of link.

The present invention specifically includes following steps:

Step (1) network status initialization, completes following work:

Each cognitive user i, i ∈ in 1.1 networks 1 ..., N} obtains the node status information of self, and including geographical location information, available communication channel collection and current battery power value, each node arranges the maximum lifetime TTL=Hop of aforementioned nodes status information_max, Hop_maxMaximum hop count for the max-forwards number of times of information arranged according to network size and the most Zhongdao cognitive base station of all nodes；The maximum lifetime TTL of the node status information of this node He this node status information is carried out broadcast transmission by each node on a control channel；

1.2 each nodes receive not less than after the node status information of maximum lifetime, its life span and information hop count are added 1, and forward this node status information, if the life span of the node status information received has equalized to maximum lifetime, then directly abandon；

1.3 cognitive base stations receive from the node status information of each node and store, and obtain the mutual distance between each node and can be used to the common signal channel collection communicated, structure network topological diagram；Wherein, the following public affairs of common signal channel centralized procurement between each node

Formula calculates:

C_ij=C_i∩C_j,i∈(1,...,N,B),j∈(1,...,N,B)

Wherein, C_ijCommon signal channel collection between representation node i and node j, each channel can both be used for directly communication by node i, j, is called link；I, j are nodal scheme, C_iAnd C_jThe available communication channel collection of representation node i and node j respectively, N is cognitive user and number of nodes, and B represents cognitive base station；

During initialization, the routing table of cognitive base station is empty；

In step (2) network, arbitrary cognitive user wanting access base station communication initiates access request by controlling channel to base station, and request includes the data length L of needs transmission and the communications speed R of needs；

After the cognitive base station of step (3) receives the access request of user, this user of iterative computation arrives the Optimization route of cognitive base station, realizes by performing following steps:

3.1 initiate the communications speed R in request and the maximum transmission power of each node on network according to cognitive user, calculate the led to link of each node, if the power demand P of communication between any two node i, j_ijMeet following formula, then node i, the link that formed between j be for can lead to link；

P_{ij} = (2^{\frac{R}{W}} - 1) N_{0} {Wd}_{ij}^{α} < P_{\max}, i &Element; (1, . . ., N, B), j &Element; (1, . . . N, B)

Wherein, W is communication channel bandwidth, and R is the communications speed needed, N₀For known Background Noise Power spectrum density, d_ijFor the air line distance between node i to node j and in step 1.3 obtain each node between mutual distance, α is radio transmission fading coefficients,Represent the radio signal fading gains at free-space propagation；

If the power demand P calculated_ijLess than the node peak power P initiating requesting node_max, then it is assumed that link i → j can lead to link, one jump up to；

3.2 measure the weights that can lead to link as every in step 2.1, energy consumption metric COST using dynamic energy consumption_ijFollowing formula is adopted to calculate:

COST_ij=P_ij*T*(E_max/(E_i-P_ij*T)),i∈(1,...,N,B),j∈(1,...,N,B)

Wherein, P_ijFor the power demand of communication, T is the transmission time once communicated, T=L/R, and L is the data length needing to send, and R is communications speed；E_maxFor energy content of battery maximum, Ei is the energy content of battery value that node i is current；

3.3 with cognitive base station for source point, and the energy consumption for each cognitive nodes measures tax initial value: the energy consumption of the node i being wherein joined directly together with base station is the COST of its direct link_iB, its next-hop node NEXT_i=B；All and cognitive base station does not have the node of direct link, and its energy consumption tolerance initial value arriving cognitive base station is ∞；

3.4 cognitive bases stand in and all are joined directly together with oneself, or in the node that can be arrived by other node relaying in routing table, select energy consumption metric COST_ijMinimum node i adds in routing table entry, and updates the energy consumption metric of all node j being joined directly together with node i；Update method is, if the energy consumption tolerance originally arriving node j is ∞, then its energy consumption tolerance is set to COST_j=COST_i+COST_ij, its next-hop node NEXT_j=i；If the energy consumption tolerance originally arriving node j is not ∞, and COST_i+COST_ij<COST_j, then COST it is set to_j=COST_i+COST_ij, its next-hop node is set to i, otherwise not changes；

If 3.5 cognitive user initiating access request are added to routing table, then end step 3；Otherwise, step 3.4 is returned；

Step (4) is according to the routing table obtained in step 3, cognitive user for initiating access request provides access path, and be the channel that each section of link selection is suitable, route and Channel assignment result are returned to cognitive user by controlling channel, the method for the distribution of aforementioned suitable channel is:

4.1 channel numbers concentrated according to common signal channel by each section of link, namely the number of available channel is ranked up；

First 4.2 be the link distribution channel that available channel is minimum, randomly chooses a channel as communication channel in its all available channels, concentrates at the available channel of all links having public point with this link simultaneously and is left out by this channel；

If 4.3. all links have been allocated channel all, then continue step 4.4；Otherwise, step 4.1 is returned

Route and channel allocation result are returned to cognitive user by controlling channel by 4.4 cognitive base stations.

After step (5) determines a route, channel has been distributed by all in this route in cognitive base station, concentrates from the available channel of the adjacent node of place both link ends node and deletes；

Step (6) routing update, completes following work:

6.1 cognitive user are to cognitive base station sending node state updating information.Cognitive user is before each sign off, in the end to cognitive base station sending node energy state more fresh information in a packet；When perceiving self available channel collection and changing, utilize and control channel, to cognitive base station sending node available channel collection more fresh information.

6.2 cognitive base stations update the status information of the respective nodes of storage, update network topology according to step 1.3 after receiving node usable spectrum state updating information or the node energy more fresh information of cognitive user transmission.

In the 1.1 of the step (1) of the present invention, the maximum lifetime TTL of the node status information of this node and this node status information by controlling channel, is adopted the mode of CSMA to carry out broadcast transmission by each node.

In the step 3.1 of the present invention, α is radio transmission fading coefficients, takes α=3 or α=4.

Beneficial effects of the present invention:

This algorithm of the present invention improves based on link-state routing algorithm, link-state routing algorithm is also known as OSPF, it is based on the shortest-path first algorithm of Dijkstra, the Link State leading to neighbours each other is exchanged between each node, each node generates network topology according to the Link State collected, carry out operation independent in this locality, find the shortest path leading to other node or network.This algorithm makes improvements, the topology of whole honeycomb is responsible for collecting in the lasting cognitive base station of, energy powerful by computing capability, for each cognitive nodes distribution Optimization route sending access request, configuration is simple, there is fast convergence rate, the features such as routing overhead is little, it is possible to be quickly found out the source node Optimization route to cognitive base station.

The present invention can adapt to the dynamic change of environment.Cognitive base station can adjust routing table in time when cognitive user nodes state changes, it is possible to makes optimized route frequency spectrum co-allocation in real time.

The present invention is in routing decision, it is contemplated that node frequency spectrum state, energy state change, it is possible to while selecting least energy route, promoted internodal justice, it is to avoid decrease the situation of the node pre-mature exhaustion energy of individual loads weight.

The routing algorithm implementation complexity of the present invention is low.The radio node of each finite energy need not be collected separately the network information, is routed.And base station lasting by energy, that computing capability is powerful is uniformly carried out, it is to avoid the double counting of each node, and the improper decision-making that local decision may result in.

The routing update easy maintenance of the present invention, updates bag load little.This method only just sends state and updates bag when node state changes to base station, and its energy state is incidentally updated in access communications bag, thus decreasing routing overhead, significantly reduces the transmission pressure of common signal channel.

Accompanying drawing explanation

Fig. 1 is algorithm flow chart of being suggested plans in the present invention.

Fig. 2 is instantiation artificial network model in the present invention.

Fig. 3 is suggested plans the observable index that route with shortest path and least energy relatively in the present invention.

Fig. 4 is that the rate that is successfully accessed suggested plans in the present invention with shortest path and least energy route compares.

Fig. 5 is that the network life suggested plans in the present invention with shortest path and least energy route compares.

Detailed description of the invention

Below in conjunction with drawings and Examples, the present invention is further illustrated.

As it is shown in figure 1, a kind of provided by the invention based on energy-optimised and network life cross-layer routing method, as shown in Figure 1 and Figure 2, detailed description of the invention is as follows:

The present invention is applied to network structure as shown in Figure 2.Wherein cognitive base station provides access service for all cognitive user in community.Each cognitive user is accessed by the mode of single-hop or multi-hop.Cognitive base station and each cognitive user have a cognitive antenna and a traditional antenna.Cognitive antenna is used for the cognitive user dynamic spectrum access communication to cognitive base station, traditional antenna uses a common signal channel, each user is transmitted by the mode of CSMA on this common signal channel, node frequency spectrum state is reported to cognitive base station, carrying out route requests, cognitive base station assigns route frequency spectrum co-allocation decision-making also by common signal channel to cognitive user.

The present invention adopts algorithm flow as shown in Figure 1.This algorithm flow is mainly made up of following six step:

Step (1) network status initialization；

After the cognitive base station of step (3) receives the access request of user, calculate this user and arrive the Optimization route of cognitive base station；

Step (4) is according to the routing table obtained in step 3, and the cognitive user for initiating access request provides access path, and is the channel that each section of link selection is suitable, and by controlling channel, route and Channel assignment result are returned to cognitive user；

Step (6) routing update, completes following work.

Embodiment:One specific embodiment of the present invention is described below, and system emulation adopts the discrete event simulation network system that C language is built, and parameter setting does not affect generality.In the present embodiment, 49 cognitive nodes and 1 cognitive base station are randomly dispersed within the scope of 400m*400m, the maximum transmission power of each node is 1w, primary power is 10J, channel width W=1MHz, each node has the random several available channels in whole 10 channels, Background Noise Power N at random₀=9.76*10^-11W/Hz, radio transmission fading coefficients α=3.In simulations, randomly choosing a cognitive nodes each second, sending a length to cognitive base station is the stream of 1Mbit, and traffic rate demand is 1Mbit/s.Under this network scenarios, we have been respectively adopted minimum hop routing, least energy route and our proposed new method for routing and have been routed.Simulation time is 500s.

In emulation, the coordinate of each node is respectively as follows:

Table 1: all node coordinates

Node serial number	Abscissa (m)	Vertical coordinate (m)
			0	285	301
1	166	11
			2	194	399
3	99	289
			4	335	30
5	43	152
			6	184	155
7	94	273
			8	56	265
9	32	324
			10	203	289
11	359	217
			12	164	333

13	288	14
			14	55	318
15	41	326
			16	381	152
17	230	342
			18	117	275
19	15	24
			20	303	106
21	270	59
			22	161	205
23	371	47
			24	244	81
25	225	314
			26	374	45
27	241	56 5 -->
			28	359	205
29	316	4
			30	14	385
31	275	3
			32	4	45
33	345	232
			34	294	248
35	310	238
			36	150	351
37	20	20
			38	40	258
39	307	346
			40	71	227
41	310	39
			42	143	236
43	53	181
			44	370	232
45	332	321
			46	310	55
47	345	109
			48	195	284
49	272	192

Wherein, the node being numbered 0 is cognitive base station, and all the other are cognitive access node.The usable spectrum collection of each node is respectively as follows:

Table 2: all node usable spectrum collection

Node serial number

Abscissa (m)

0	1,2,3,4,7
		1	0,1,2,3,4,6,7,9
2	1,2,4,6,7,8,9
		3	0,2,5,7,8
4	0,1,2,4,6,7,9
		5	1,2,3,4,7,8,9
6	0,1,2,3,5,6,7,8
		7	0,2,4,6,7
8	2,3,4,6,7
		9	2,4,7,8,9
10	0,1,3,4,7,8,9 6 -->
		11	0,7,9
12	0,3,5
		13	0,1,4,5,6,8,9
14	1,2,4,6,7
		15	1,2,3,5,7,8
16	3,4,7,9
		17	1,6,7,9
18	0,3,5,6,7,8
		19	1,7
20	1,2,5,6,8,9
		21	0,1,3,4,5,6,7,8
22	0,4,5,8
		23	0,1,3,5,6,8
24	3,4,5,6,7
		25	0,2,3,4,5,7,9
26	0,5,6,7,8,9
		27	2,3,4,7,8
28	0,1,2,3,4,5,6,7,8
		29	5,9
30	0,3,5,7
		31	0,1,2,4,5
32	1,3,6,8
		33	4,6,8,9
34	0,1,3,4,5,9
		35	0,1,4,6,7
36	1,2,4,5,8
		37	0,1,3,5,6
38	4,5,8,9
		39	0,8
40	0,2,3,4,6,8
		41	0,1,2,4,5,8

42	0,1,2,6,9
		43	1,2,3,5,6,8,9
44	0,1,3,4,7,8,9
		45	2,4,6,7,8,9
46	3
		47	0,1,2,3,5,6
48	3,6,7,9 7 -->
		49	2,6,7,8,9

Fig. 2 gives the network topological diagram of this emulation case.

Fig. 3 gives employing three kinds of different method for routing, accesses the average energy loss-rate of session relatively every time.

Fig. 4 gives employing three kinds of different method for routing, and all cognitive nodes access the success rate of cognitive base station and compare.

Fig. 5 gives employing three kinds of different method for routing, and passage in time, the number of nodes lost efficacy because of depleted of energy compares.

As can be seen from Figure 5, new method for routing, significantly postpone the time of node death because of energy decline, it is fair that this has not only promoted between node, also provide more trunk node selection for follow-up route, although to such an extent as to the route that every time selects of new method for routing is not least energy route, but as can see from Figure 3, it is in whole simulation process, accesses the average energy consumption of session still less than least energy method for routing every time.Simultaneously it can be seen from figure 4 that because new method for routing makes more node lifetime extend so that the via node that can use increases relatively, thus what improve cognitive nodes is successfully accessed rate.And in simulations, for the ease of several method for routing are compared, we only select in emulation before every time, all without the node lost efficacy as access node under three kinds of method for routing, considering that there is obvious postponement the node failure time of new route, it can be higher in other two kinds of methods for the practical capacity of all nodes offer access service.

Part that the present invention does not relate to is all same as the prior art maybe can adopt prior art to be realized.

Claims

1. based on energy-optimised and network life a cross-layer routing method in cognitive radio cellular network network, it is characterized in that it includes: network status initialization, obtain the common signal channel collection C of each node_ijStep；By calculating every energy consumption metric that can lead to link, choose cognitive user nodes and arrive the step of the minimum route of the energy consumption of cognitive base station；The step of channel distribution is carried out for each section of link；It specifically includes following steps:

Step (1) network status initialization, completes following work:

1.3 cognitive base stations receive from the node status information of each node and store, and obtain the mutual distance between each node and can be used to the common signal channel collection communicated, structure network topological diagram；Wherein, the common signal channel centralized procurement between each node calculates with following formula:

C_ij=C_i∩C_j,i∈(1,...,N,B),j∈(1,...,N,B)

During initialization, the routing table of cognitive base station is empty；

In step (2) network, arbitrary cognitive user wanting access base station to communicate initiates access request by controlling channel to cognitive base station, and this access request includes the data length L of needs transmission and the communications speed R of needs；

After the cognitive base station of step (3) receives the access request of cognitive user, this cognitive user of iterative computation arrives the Optimization route of cognitive base station, realizes by performing following steps:

3.1 initiate the maximum transmission power Pmax of each node on the communications speed R of needs in access request and network according to cognitive user, calculate the led to link of each node, if the power demand P of communication between any two node i, j_ijMeet following formula, then node i, the link that formed between j be for can lead to link；

P_{i j} = (2^{\frac{R}{W}} - 1) N_{0} {Wd}_{i j}^{α} < P_{m a x}, i &Element; (1, ..., N, B), j &Element; (1, ..., N, B)

3.2 measure the weights that can lead to link as every in step 3.1, energy consumption metric COST using dynamic energy consumption_ijFollowing formula is adopted to calculate:

COST_ij=P_ij*T*(E_max/(E_i-P_ij*T)),i∈(1,...,N,B),j∈(1,...,N,B)

Wherein, P_ijFor the power demand of communication, T is the transmission time once communicated, T=L/R, and L is the data length needing to send, and R is the communications speed needed；E_maxFor energy content of battery maximum, E_iIt it is the current energy content of battery value of node i；

3.3 with cognitive base station for source point, and the energy consumption for each cognitive nodes measures tax initial value: the energy consumption of the node i being wherein joined directly together with cognitive base station is the COST of its direct link_iB, its next-hop node NEXT_i=B；All and cognitive base station does not have the node of direct link, and its energy consumption tolerance initial value arriving cognitive base station is ∞；

If 4.3. all links have been allocated channel all, then continue step 4.4；Otherwise, step 4.1 is returned；

Route and channel allocation result are returned to cognitive user by controlling channel by 4.4 cognitive base stations；

Step (6) routing update, completes following work:

6.1 cognitive user are to cognitive base station sending node state updating information；Cognitive user is before each sign off, in the end to cognitive base station sending node energy state more fresh information in a packet；When perceiving self available channel collection and changing, utilize and control channel, to cognitive base station sending node available channel collection more fresh information；

2. based on energy-optimised and network life cross-layer routing method in cognitive radio cellular network network according to claim 1, it is characterized in that in the 1.1 of described step (1), the maximum lifetime TTL of the node status information of this node and this node status information by controlling channel, is adopted the mode of CSMA to carry out broadcast transmission by each node.

3., based on energy-optimised and network life cross-layer routing method in cognitive radio cellular network network according to claim 1, it is characterized in that in described step 3.1, α is radio transmission fading coefficients, takes α=3 or α=4.