CN109089273A - Relay selection method based on state transition probability in a kind of Ad-Hoc network - Google Patents
Relay selection method based on state transition probability in a kind of Ad-Hoc network Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/02—Arrangements for optimising operational condition
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W40/00—Communication routing or communication path finding
- H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
- H04W40/22—Communication route or path selection, e.g. power-based or shortest path routing using selective relaying for reaching a BTS [Base Transceiver Station] or an access point
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- 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
Abstract
The present invention provides the relay selection method based on state transition probability in a kind of Ad-Hoc network, innovatively proposes the relay selection strategy based on state transition probability.The possible arrival probability of neighbor node is calculated in conjunction with node geo information and channel environmental information, and the big person of select probability carries out data transmission, thus improve data transfer performance.Meanwhile in order to reduce system operations complexity and save system capacity, using the metropolis selection criterion of transformation, the transmission state of those small probabilities is removed step by step with the greed search of simulated annealing, to considerably reduce computational space.Emulation gives influence of the algorithm parameter to arithmetic speed and success rate.Meanwhile it also showing the algorithm and has preferable control to the growth of system energy consumption and probability of failure in network topology change.
Description
Technical field
The present invention relates to the relay selections based on state transition probability in information technology field more particularly to Ad-Hoc network
Method.
Background technique
Ad-Hoc network is a kind of distributed wireless network, it does not depend on the communications infrastructure of any fixation, tight
Quickly networking can be carried out by self-organization network technology in anxious situation, and survivability is strong, in contemporary digital war, earthquake water
The fields such as calamity, field exploration are with good performance.But Ad-Hoc network is many serious as the increase of hop count is also brought
Problem.Firstly, since the mobility of network node makes network topology and transmission route dynamic changeable, thus lead to system
Operation is complicated and stability is poor.At the same time, Ad-Hoc network node energy is limited, cannot be replenished in time in many applications
Or replacement, network life are also seriously threatened[1-2].Therefore, the energy consumption problem of Ad-Hoc network oneself become restrict its development
Main bottleneck.How to reduce the energy consumption of node and extend network life, scholars have carried out further investigation from different perspectives.In reality
In the application of border, carrying out next-hop transmission for the relaying of multi-hop Ad-Hoc network selection appropriately is to save energy and shorten having for time delay
Effect means.All the time, the relay selection method based on location information gradually has been favored by people, this is because in emulation
Node coordinate can be generated at random in limitation scene areas, it in this way can the distance between calculate node.Document[3]Pass through derivation
The region division is the concentric loop with same bit error rate performance, together by the curvilinear equation of optimal relay node region envelope
When select optimal relay node in conjunction with the position of relay node and destination node.This method makes full use of space diversity characteristic
Improve error rate of system performance.Document[4]In order to extend network lifecycle, node transmitting is constructed based on node location information
Energy consumption, the cost formula for receiving energy consumption and dump energy composition, to realize the optimal transistroute choosing under three kinds of energy tradeoffs
It selects.Document[5-7]Emphasis considers node location and at a distance from destination node etc. because usually selection relays.As it can be seen that these methods
The influence such as position and transmission range is all considered, but has ignored the factors such as important path interference.Later, some scholars are again
Emphasis considers the influence of channel status.Document[8-10]The relay selection method based on average signal-to-noise ratio is had studied, i.e. selection hair
The average signal-to-noise ratio for each chain road for sending node to link with relay node is greater than the node of specified thresholding as relaying, but this is needed
It is to be understood that the priori knowledge of channel, and use and estimate that the average signal-to-noise ratio of all relay nodes has biggish overhead comprehensively.
Under the influence of the network topology of dynamic mobile and battery life, efficiency can be lower.Document[11-13]Using based on local measurement
Obtained instantaneous channel state information selects properly to relay.Decision goes out a best relay and passes in numerous optional relay nodes
Defeated path, it does not need any priori knowledge, including network topological information and geographical location information, does not need in relay node yet
Intercommunication.But best relay selection of transmission paths success or not depends on node and believes the real-time statistic of current radio channel
Breath, it is a kind of best relay selection method end to end of opportunistic, has unstability feature.In recent years, it is with energy conservation
The trunk node selection algorithm of target has become the research hotspot in wireless network[14-18]。
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Summary of the invention
It is an object of the invention to solve the problems of the above-mentioned prior art, comprehensively consider between actual conditions lower channel
Interfere with each other, transmission range and energy loss between node the problems such as, propose a kind of based on network state transition probability
Relay selection algorithm, it is intended to the transmission performance of multihop network be better achieved.
Relay selection method based on state transition probability in a kind of Ad-Hoc network, comprising:
Information source s sends message to its optimal neighbours' subset first, neighbours' subset receive after message again respectively to it is respective most
Excellent neighbours' subset continues forward pass, and so on, to the last message reaches stay of two nights t by merging；
In Ad-Hoc network, best relay is selected by constraining in numerous relay nodes for network state transition probability
Message is sent as optimal neighbours' subset, wherein the calculation method of the network state transition probability are as follows:
If the channel gain between any two node link is hi,j, transmission power pi, consider the mutual dry of adjacent link
It disturbs, therefore signal-to-noise ratio is represented by γ=(pihi,j)/(σi,j 2+∑k∈ψ,k≠i(pkhk,j)), σ2For additive white Gaussian noise power, then
Probability density function PDF is indicated are as follows:
Then:
Thus the outage probability calculation formula under Rayleigh fading channel model DF mode can be exported are as follows:
If the node in current state x integrates as A (x), then a ∈ A (x), enables terminal state for t, x ≠ t, then general from interrupting
The definition of rate can export, when the Successful transmissions probability of the cooperative nodes subset a to next-hop arbitrary node n of front jumping is 1-pout(n,
A), if enabling θ are as follows:
Then according to the property of Markov chain, system can be with to the transition probability of state y from state x is defined as:
Further, method as described above selects best relay as the optimal neighbours in numerous relay nodes
When subset sends message, unnecessary and low contribution rate neighbours' collection and behavior collection, the method for reduction are cut down are as follows:
Each state transition probability p is calculated according to (7) formulaxy(a), the lesser neighbor node of those transmission probabilities is cut off,
The biggish path of those transmission probabilities is left, so that optimization algorithm is used for the state space of operation, is so had:
Here S indicates all state sets of system, can define system from state x to quilts all in the conversion process of state y
The maximum of the neighbor node cut cuts down probability are as follows:
So cut down the maximum difference of front and back energy consumption values are as follows:
Wherein,It is the expense estimation of state x.During the execution of the algorithm, indicate that M (x) is 0 when Δ (x)=0,
There is no node variation between state x, y, then not beta pruning is iterated beta pruning as Δ (x) > 0,
It is deformed to obtain the upper bound of state reduction by (16) formula:
In order to make N'(x) in node it is suitably more, determine (17) formula upper bound M_As long as M (x) is no more than M_?.
Further, method as described above removes the transmission state of those small probabilities using greed search step by step, described
The method of greed search are as follows:
Step 1: when k=0, current solution is S (0)=x locating for Ad-Hoc network, is followed the steps below at temperature T:
Step 2: according to current solution S (k) state in which x, a neighborhood subset is generatedBy
N (S (k)) randomly obtains a new state y, calculates the beta pruning upper bound and reaches the difference of energy spent by new state y: Δ C
=M_-C(y)
Step 3: if Δ C≤0, then it represents that energy consumption is more than the upper bound, is directly refused；If Δ C > 0, random number rand ∈ is generated
Random [0,1), if the probability of acceptanceThen receiving y is next current solution.If receiving at this time
New state y, then enable S (k+1)=y, otherwise, S (k+1)=x.
Step 4: k=k+1 executes next iteration, judges whether algorithm should terminate according to given convergence criterion, if
It is to turn step 5, otherwise turns step 2.
Step 5: it returns.
The utility model has the advantages that
In the multi-hop Ad-Hoc network topology structure of practical application, each jump can cooperate to transmit the relaying of message the application
Number of nodes is variation, and present invention proposition selects suitable neighbor node subset and optimal in available neighbor node set
Relay transmission target of the activity as next-hop, while considering the energy consumption and time delay of network.As network dynamic changes, state
Space also increases in continuous variation.When state space increases, greed search and simulated annealing are imitated, removes those under
One transmission link has the node reached compared with small probability and state, to simplify the state space for operation, reaches optimization relaying
The purpose of transmission performance.
Detailed description of the invention
Fig. 1 is multi-hop Ad-Hoc network channel model；
Fig. 2 is the queuing model on transmitting time delay and relay node in distance；
The relationship of Fig. 3 scene size and system energy consumption；
Fig. 4 is the graph of relation of scene size and cooperative nodes number.
Specific embodiment
To make the object, technical solutions and advantages of the present invention clearer, the technical solution below in the present invention carries out clear
Chu is fully described by, it is clear that described embodiments are some of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
In Ad-Hoc network, selected in best by constraining in numerous relay nodes for network state transition probability
After therefore, the present invention gives the calculation method of outage probability first, and it is general then to calculate network state conversion by outage probability
Rate simultaneously determines best relay.In order to reduce the complexity of relay selection, we have proposed a kind of effective operation methods, that is, pass through
The relay node quantity that limitation participates in calculating carrys out improving operational speed, simultaneously, it is ensured that the relay node quality for participating in calculating is with excellent
Change relay transmission performance.
Consider multi-hop Ad-Hoc network, node total number ψ, node only one antennal interface.In traditional
During cooperative transmission, basic process is: information is sent to destination node t from source node s, and source node s first is to its institute
There is neighbor node broadcast message, all nodes for receiving the message forward it to next-hop again.And so on, cooperation forwarding is not
Disconnected forward pass is until incoming terminal node t.It is opened as it can be seen that this traditional broadcast mode will generate very big redundancy and system
Pin.The present invention optimizes on this basis, is used as and is turned using " optimal subset " that appropriate Pruning strategy obtains neighbor node
Target is sent out, and is forwarded forward by decoding forwarding (decode and forward, DF) trunk protocol.Assuming that participating in cooperative transmission
" optimal subset " number of nodes be a, then under path loss and fade condition, node in set a to any relay reception section
The transmission channel model of point is as shown in Figure 1.In Fig. 1, information source s sends message, neighbours' subset to its optimal neighbours' subset first
Continue forward pass to respective optimal neighbours' subset respectively again after receiving message, and so on, to the last message is by being merged into
Up to stay of two nights t.Decoding forwarding each jump in, optimal neighbours' subset always select the node closer from stay of two nights t as relay, this
Help faster to complete data communication.
State transition probability
Present invention assumes that network environment is Ad-Hoc multihop network, and to decode forward relay association in Rayleigh channel
View transmission message, and the transmission power of radio node is identical.Because of the difference of each link-quality and loss, network is likely to occur
The various phenomenons such as delay or flash.Therefore, for the ease of selecting suitable network state, the present invention defines turn between network state
Probability is changed to describe the dynamic change of Ad-Hoc network.
Assuming that transmitting node is transmitted with unit bandwidth rate R, enabling the capacity of wireless channel is C, the signal-to-noise ratio of channel
For γ, then work as channel capacity are as follows:
C=log2(1+γ) (1)
Interrupt event will be generated when channel capacity is lower than rate R[19].If the letter in Fig. 1 between any two node link
Road gain is hi,j, transmission power pi, consider interfering with each other for adjacent link, therefore signal-to-noise ratio can indicate
For γ=(pihi,j)/(σi,j 2+∑k∈ψ,k≠i(pkhk,j)) (2)
σ2For additive white Gaussian noise power, then probability density function (PDF) may be expressed as:
Then:
Thus the outage probability calculation formula under Rayleigh fading channel model DF mode can be exported are as follows:
If the node in current state x integrates as A (x), then a ∈ A (x), enables terminal state for t, x ≠ t, then general from interrupting
The definition of rate can export, when the Successful transmissions probability of the cooperative nodes subset a to next-hop arbitrary node n of front jumping is 1-pout(n,
A), if enabling θ are as follows:
Then according to the property of Markov chain, system can be with to the transition probability of state y from state x is defined as:
If x, y belong to SOT state of termination t, that is, work as x=y=t, it is clear that have pxy(a)=ptt(a)=1.In real network
In transmission process, transition probability depends on the composite factors such as relative position and the transmission channel of transmission node.
The calculating of network return value
The energy consumption of relay network node mainly includes three parts: transmission, reception and free time.Usual sending power consumption is main
The part to be considered, and receive and be often considered as constant with the power consumption when free time or ignore[20].In practice, sending power consumption takes
Certainly in the number of cooperative node and the environmental parameter of transmission message.Present invention assumes that energy all makees normalized, if sending
Energy consumption is VE, time delay consumption is VD, a is the node set for parsing message and cooperation, and m is other parsing message non-cooperative nodes collection
It closes, at this point, energy consumption VEIt may be expressed as:
VE=a+ ω m (8)
Parameter ω >=0 is overhead factor when adjusting additional nodes parsing message, if illustrating, a certain jump passes formula (8) simultaneously
Defeated node is more, and total transmitting energy consumption will be bigger, conversely, can then save system emission power.
Time delay consumes VDIt is believed that consisting of two parts.Processing is lined up on transmitting time delay and relay node in distance
Time delay.It is queuing model in Fig. 2.
In Fig. 2, data to be transmitted randomly come service organization, to be serviced by certain queue discipline etc..At this
In relay system, relay node is single antenna, and queuing system is single channel list service, and data arrival meets Poisson distribution, takes
The business time obeys exponential distribution, therefore serving-time distribution of the data packet of the relay transmission system on each relay node is[21]:
Fq(w)=1- ρ e-(μ-λ)w (9)
It is a length of when average waitingWherein, μ is transmission rate, and λ is arrival rate, and ρ is system availability, i.e.,It is therefore, total to be lined up the processing time are as follows:
The present invention sets the time delay in transmission distance as a constant qt, then:
Then system mode x is defined as follows via the network return value that a relay node reaches state y:
Assuming that current state x reaches state y by all possible path, then all possible paths is subjected to probability and added
Power, finding equation when expending minimum when reaching home may be defined as:
J*(x)=mina∈A[∑y∈N(x)pxy(a)(V(x,a,y)+ξJ(y))],ξ∈[0,1] (13)
Relay selection optimisation strategy
Complexity cuts down technology
The present invention reduces system complexity using the strategy of trimming network state space.The known all sections for being in state x
Point is A (x), and neighbours, which integrate, cuts down unnecessary and low contribution rate neighbours' collection and behavior collection as N (x), and new set can be obtained
A'(x) and N'(x).Obviously there is the node collection after cutting downNeighbours' collection
During actual complex degree is cut down, each state transition probability p is calculated according to (7) formulaxy(a), those transmission probabilities are cut off
Lesser neighbor node leaves the biggish path of those transmission probabilities, so that optimization algorithm is used for the state space of operation.So
(13) formula becomes:
Here S indicates all state sets of system.The system of can define is from state x to quilts all in the conversion process of state y
The maximum of the neighbor node cut cuts down probability are as follows:
So combine (13) and (15) formula it is found that cutting down the maximum difference of front and back energy consumption values are as follows:
Wherein,It is the expense estimation of state x.During the execution of the algorithm, indicate that M (x) is 0 when Δ (x)=0,
There is no node variation between state x, y, then not beta pruning is iterated beta pruning as Δ (x) > 0.
It is deformed to obtain the upper bound of state reduction by (16) formula:
(15) formula indicates that M (x) is smaller, by state transition probability pxyObtained N'(x) other than all nodes probability it
With it is smaller, illustrate the p of maximum probabilityxyIt has been selected into N'(x) in.Therefore, in order to make N'(x) in node it is suitably more, determine
(17) upper bound M of formula_As long as M (x) is no more than M_?.
Pruning algorithms are as shown in Algorithm1.
Above-mentioned Pruning strategy realizes the simplification operation to a large amount of state spaces, i.e., the M (x) that each interative computation obtains is only
It to be more than upper bound M_With regard to rejecting turned state y, otherwise just receive, the neighbours subset N'(x after beta pruning obtained with this) and activity
Integrate as A'(x).In this scheme, the value in the upper bound will directly affect the number of beta pruning, when upper bound value is too big or proximity state x
The sum of all neighbor node probability when, be easy to cause relaying cooperation subset rare, probability of failure increases；When upper bound value too
Small, pruning algorithms will fail substantially.Therefore, in order to reduce system complexity under the premise of guaranteeing success rate, the present invention is referred to
Simulated annealing (simulate anneal, SA) advanced optimizes[22].By modification to Metropolis criterion with
With proposition is in M (x) < M_When acceptable those state intervals in, then transition status received with certain probability, thus into
One-step optimization state space reduces system redundancy, improves convergence rate, saves system capacity.
Optimization Algorithm
Consider that multi-hop relay Ad-Hoc network state cuts down the upper bound and is converted to the probability of remaining neighbor node after new state
Both the difference of statistics, if cutting down the upper bound is less than or equal to the probability total that network is transformed into remaining node after a new state, i.e.,
Difference DELTA C < 0, then this state not receives；When Δ C >=0 item is received with certain probability.Due to quasi- in traditional Metropolis
A possibility that in then, Δ C is smaller, receives is bigger.And in the relay transmission network studied of the present invention, Δ C is bigger, receiving can
Energy property should be bigger.Therefore, the present invention utilizes the transformation criterion of Metropolis, enables df=M_-MX, then transformed to receive generally
Rate are as follows:
Wherein, q indicates that acceptance probability, x, y indicate system mode, TkIndicate the temperature value at kth moment.It is transformed
Steps are as follows for the execution of Metropolis criterion:
Step 1: when k=0, current solution is S (0)=x locating for Ad-Hoc network, is followed the steps below at temperature T:
Step 2: according to current solution S (k) state in which x, a neighborhood subset is generated
One new state y is randomly obtained by N (S (k)), calculate the beta pruning upper bound and reaches the difference of energy spent by new state y: Δ
C=M_-C(y)
Step 3: if Δ C≤0, then it represents that energy consumption is more than the upper bound, is directly refused；If Δ C > 0, random number rand ∈ is generated
Random [0,1), if the probability of acceptanceThen receiving y is next current solution.If receiving at this time
New state y, then enable S (k+1)=y, otherwise, S (k+1)=x.
Step 4: k=k+1 executes next iteration, judges whether algorithm should terminate according to given convergence criterion, if
It is to turn step 5, otherwise turns step 2.
Step 5: it returns.
Based on this, the invention proposes the ISA (improved based on Metropolis transformation criterion
Simulatedannealing) algorithm is as shown in Algorithm2.ISA (S (a)) is intended to calculate whether NextState is received,
Its course of work is as follows: setting initial value init_T, if M (S (ai)) it is not above the upper bound, it is general with the reception that (19) formula calculates
Rate qk(x, y) is received, with 1-qkThe probability of (x, y) is refused；Then directly refuse part more than the upper bound.And each
Change T value in iterative process, the coboundary of T is set, when T grows beyond terminal_T initially set according to certain slope
When, T will become init_T × β.When next time iterate to come when, by continue with front slope increase, until reaching again
terminal_T.T value is converted repeatedly, and by mathematical analysis, when the number of iterations is continuously increased, T will level off to
terminal_T。
What T was substantially carried out is the process progressively increased repeatedly, when T increases, according to (19) formula, qk(x, y) will constantly become
It is small, i.e., it can be gradually increased the branch of rejecting.When T becomes terminal_T, degenerates and seek to calculate compared with the greediness of subbranch for one
Method.
Numerical simulation
Setting network scene is square region, and starting point s and terminal t are located at scene both ends, the seat of remaining each node
It is marked in the network scenarios of 150m*150m and generates and be uniformly distributed at random.In simulation process, the initial value point of relevant parameter is set
Not are as follows: n=10, ψ=15, ω=0.5, μ=0.9, λ=0.4, ξ=0.8, initT0=.01, termianlT1=0, β 1.=
2。
Regard the Global Topological of Ad-Hoc network as Undirected graph, when the start node number of network is n, then has n (n-
1)/2 side.If n=10, then the network state variation formed may be up to many trillion.In order to illustrate that it is empty that ISA algorithm cuts down state
Between validity, the present invention verifies 1000 calculated average access shapes of the algorithm iteration under different Δ value conditions
The average probability of failure of state space number and algorithm is as shown in table 1.In table 1, actual access state space number is illustrated
The computation complexity or arithmetic speed of algorithm, average probability of failure, which expresses inventive algorithm, cannot find the general of optimal relaying
Rate.As known from Table 1, the value of Δ directly influences two important indicators of system operations complexity and algorithm probability of failure.Usually
In the case of: the value of Δ is bigger, and the state space number of actual access is fewer, and algorithm probability of failure is continuously increased；The value of Δ
Smaller, the state space number of actual access is more, and algorithm probability of failure is constantly reduced.Overall situation is: with the reduction of Δ,
Access state number shows a increasing trend, and probability of failure is in decreasing trend, this is because by (17) formula it is found that when Δ increases, then
Indicate that the upper bound for cutting down complexity increases, the state space being thus removed is more, and is left the state for actual access
It reduces therewith in space.But but there is reversed growth in probability of failure as Δ < 1, this illustrates that Δ is too small, can make status number
Mesh, which sharply increases, instead results in certain decision failures.So in actual network design, need according to demand situation reducing
A tradeoff is done in terms of system operations complexity and reduction algorithm probability of failure.The present invention takes Δ=1 for the relationship both balanced
Carry out subsequent emulation.
1 access state space number of table and failure rate
Tab.1Number of access state spaces and failure rate
Give system performance bring various influences to verify the mobility of multi-hop Ad-Hoc network, Fig. 3 give because
System energy consumption bring situation of change is given when node motion.Due to the variation of scene size, the distance between node changes, when total
In the case that interstitial content is constant, source node and destination node distance are remoter, and the total energy consumption of system is bigger.From figure 3, it can be seen that
The energy consumption of system is intersected at 67 meters when total node number n=10 and n=15, when their network scenarios is all less than 67 meters
In the range of when, interstitial content distribution it is more intensive, energy consumption is smaller.This is because in a small range, when n=15 each node neighbour
Node increase is occupied, the optional relaying of source node to destination node becomes easy.When with scene be greater than 67 meters when, the distribution of node
Becoming sparse, is influenced by power and parsing information node number is sent, system energy consumption when n=15 when ratio n=10 increases, this
When illustrate the extension for having selected more relay nodes to be made up scene when n=15.
Fig. 4 gives the relationship between scene size variation and cooperative nodes number.It observes shown in Fig. 4, at about 67 meters
Place, in the case where being less than the scene, when cooperative nodes number when n=15 is less than n=10.But with the continuous increasing of scene
Greatly, cooperative nodes number will also increase therewith after the increase of total node number mesh, and obtaining energy consumption also thus according to formula (12) will increase,
This is consistent with the energy consumption relational graph of Fig. 3.
Relay selection strategy based on network state transition probability solves multi-hop Ad-Hoc network rings to a certain extent
Highly redundant state space bring computational complexity problem in border.Metropolis criterion is carried out proper transformation by the strategy, with
Certain probability carries out state beta pruning to network state space and greed is searched for, to realize the optimum selecting of relay node.Experiment
The result shows that ISA algorithm proposed by the present invention the indexs such as system operations amount, energy consumption and success rate have all been carried out it is obvious excellent
Change, optimal relay transmission can be further realized for the later period by, which suggesting plans, provides reliable technical support.
Finally, it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
Present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that: it still may be used
To modify the technical solutions described in the foregoing embodiments or equivalent replacement of some of the technical features；
And these are modified or replaceed, technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution spirit and
Range.
Claims (3)
1. the relay selection method based on state transition probability in a kind of Ad-Hoc network characterized by comprising
Information source s sends message to its optimal neighbours' subset first, and neighbours' subset receives after message again respectively to respective optimal neighbour
It occupies subset and continues forward pass, and so on, to the last message reaches stay of two nights t by merging；
In Ad-Hoc network, best relay conduct is selected by constraining in numerous relay nodes for network state transition probability
Optimal neighbours' subset sends message, wherein the calculation method of the network state transition probability are as follows:
If the channel gain between any two node link is hi,j, transmission power pi, consider interfering with each other for adjacent link, therefore believe
It makes an uproar than being represented by γ=(pihi,j)/(σi,j 2+∑k∈ψ,k≠i(pkhk,j)), σ2For additive white Gaussian noise power, then probability is close
Spending function PDF indicates are as follows:
Then:
Thus the outage probability calculation formula under Rayleigh fading channel model DF mode can be exported are as follows:
If the node in current state x integrates as A (x), then a ∈ A (x), enabling terminal state is t, x ≠ t, then from outage probability
Definition can export, when the Successful transmissions probability of the cooperative nodes subset a to next-hop arbitrary node n of front jumping is 1-pout(n, a),
If enabling θ are as follows:
Then according to the property of Markov chain, system can be with to the transition probability of state y from state x is defined as:
2. the method according to claim 1, wherein being selected in numerous relay nodes described in best relay conduct
When optimal neighbours' subset sends message, unnecessary and low contribution rate neighbours' collection and behavior collection, the method for reduction are cut down are as follows:
Each state transition probability p is calculated according to (7) formulaxy(a), the lesser neighbor node of those transmission probabilities is cut off, is left
The biggish path of those transmission probabilities so has so that optimization algorithm is used for the state space of operation:
Here S indicates all state sets of system, and the system of can define is cut up from state x to all in the conversion process of state y
The maximum of neighbor node cut down probability are as follows:
So cut down the maximum difference of front and back energy consumption values are as follows:
Wherein,It is the expense estimation of state x.During the execution of the algorithm, indicate that M (x) is 0, i.e. state when Δ (x)=0
X, there is no node variation between y, then not beta pruning is iterated beta pruning as Δ (x) > 0,
It is deformed to obtain the upper bound of state reduction by (16) formula:
In order to make N'(x) in node it is suitably more, determine (17) formula upper bound M_As long as M (x) is no more than M_?.
3. according to the method described in claim 2, it is characterized in that, removing the transmission of those small probabilities step by step using greed search
State, the method for the greed search are as follows:
Step 1: when k=0, current solution is S (0)=x locating for Ad-Hoc network, is followed the steps below at temperature T:
Step 2: according to current solution S (k) state in which x, a neighborhood subset is generatedBy N (S
(k)) a new state y is randomly obtained, the beta pruning upper bound is calculated and reaches the difference of energy spent by new state y: Δ C=
M_-C(y)
Step 3: if Δ C≤0, then it represents that energy consumption is more than the upper bound, is directly refused；If Δ C > 0, random number rand ∈ is generated
Random [0,1), if the probability of acceptanceThen receiving y is next current solution.If receiving at this time
New state y, then enable S (k+1)=y, otherwise, S (k+1)=x.
Step 4: k=k+1 executes next iteration, judges whether algorithm should terminate according to given convergence criterion, if then
Turn step 5, otherwise turns step 2.
Step 5: it returns.
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