CN103796211A - Distribution method of united power and channels in cognitive wireless network - Google Patents
Distribution method of united power and channels in cognitive wireless network Download PDFInfo
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Abstract
The invention discloses a distribution method of united power and channels in a cognitive wireless network. The distribution method of the united power and the channels in the cognitive wireless network comprises the steps that (1) interference between a primary user and a cognitive user on each channel in the multi-channel cognitive wireless network and among cognitive users is quantized and limited; (2) a utility function defining the behavior that each cognitive user is accessed to a frequency spectrum serves as a handling capacity, and the optimal solution of the utility function of the cognitive users is solved in a distributed mode by constructing a non-cooperative game frame so that the performance of the entire network can be optimal; (3) a corresponding price function is introduced to price resource occupation behaviors of the cognitive users, and selfishness of the cognitive users is limited; (4) a potential game model is built by utilizing a non-cooperative game, and astringency of the potential game model is guaranteed. The distribution method of the united power and the channels in the cognitive wireless network has the advantages of solving the problem of distribution of the united power and the channels in a distributed mode by utilizing the price function and the potential game model, and improving distribution fairness of network resources and the utilization efficiency of the frequency spectrum.
Description
Technical field
The invention belongs to wireless communication technology field, particularly joint Power and the method for channel allocation in a kind of cognitive radio system.
Background technology
Cognitive radio (CR:Cognitive Radio) is a kind of new technology that improves frequency spectrum resource utilization rate.Its as a kind of can perception the intelligent communication system of extraneous communication environment, allow on time domain, frequency domain and spatial domain, carry out the spectrum reuse of multidimensional and share.The core concept of CR is: by regulating networks running parameter, quantizing and limiting under the prerequisite that primary user (PUs:Primary Users) is disturbed, allow the inferior user (SU:Secondary User) based on cognitive radio function, also referred to as cognitive user, be referred to as cognitive user herein, be CR) chance formula dynamic access mandate frequency range, thus improve the availability of frequency spectrum.Mitola is the scholar who proposes the earliest cognitive radio concept, and he emphasizes that cognitive radio is a kind of radio that obtains appointed function based on model demonstration in radio association area.In other words, cognitive radio is based on frequency spectrum limited network, needs and corresponding primary user consults and select a good opportunity to access corresponding channel and realize a kind of technology of resource-sharing mutually.Up to the present, some government departments and radio standard are organized and are launched respectively corresponding policy and standard.As having proposed corresponding unauthorized device in succession in 2002,2003, FCC (FCC:Federal Communications Commission) need to possess the ability of the vacant frequency range of perception and the frequency spectrum share mode based on interference temperature; In the Britain TVWS frequency range service regeulations that communication office of Britain (Ofcom:Office of Commnications) issues, emphasize to set up TVWS frequency range database all over Britain, consider equipment cost, the equipment of not requiring possesses frequency spectrum perception ability.
Game theory is a kind of mathematical tool of research strategy form, for decision-making and the equalization problem of prediction and optimization imperfect competition making policy decision main body.Due to behavior and the income of rationality user in can analysis and prediction competitive environment, game theory is more and more used in wireless network resource configuration and power control.On the one hand, in many cognition wireless networks of isomery coexisted environment, the finiteness of usable spectrum resource makes user's competitive behavior become selfish.User can not consider the interests of other users or whole network, and the one-side number one of pursuing maximizes.On the other hand, cognitive radio is autonomous in essence, by academic environment, regulates self running parameter, thus improve, optimized network performance.For interaction and the impact of multi-user in single cognition wireless network scene, can study by game modeling.In theory of games framework, defined cognitive wireless network user is participant, corresponding new transformation parameter (frequency, power etc.) to select be behavior collection, the corresponding network performance obtaining is policymaker's effectiveness collection, determines the betting model that special scenes is corresponding, the behavior of many-sided research cognition wireless network: first, share betting model by the dynamic spectrum building between the network user, analysis user behavior under the game framework of standard, and definite running parameter is adjusted strategy; Secondly, the various optimization criterions of game theory energy design frequency spectrum share problem, generally speaking, frequency spectrum optimization utilization is a multi-objective optimization question, be difficult to obtain its optimal solution, and game theory is by seeking the balanced criterion under different game strategies, can obtain best solution of game; Moreover non-cooperative game only utilizes local information just can obtain the shared optimal resource allocation scheme of dynamic spectrum, be specially adapted to the dynamic resource configuration of distributed cognition wireless network.
Non-cooperative game is the important branch of theory of games, has been widely used in cognitive radio networks resource and has distributed.As for ad-hoc network application scene, the people such as N.Nie have proposed the distributed self-adaption channel allocation algorithm based on dive game and irrepentant study, set up the betting model of diving and have solved well adaptive channel allocation convergence problem; And in order to make cognition network user can not produce because of selfishness the skew problem of utility function, the people such as N.Nie have also proposed the distributed self-adaption channel allocation algorithm based on irrepentant study.Although this algorithm has taken into full account the fairness problem of cognitive user in the time of competitive resource, does not consider the interference that primary user is subject to; In addition, the optimization selection problem of power is not also well solved.A.Mostaani and M.F.Sabahi propose the design of a kind of new utility function on the people's such as N.Nie Research foundation, and result shows, the game process under price mechanism is a kind of game of diving, and can converge on Nash Equilibrium.In addition, first the people such as Goodman have proposed the power control algorithm based on non-cooperative game, this algorithm points out that NPG (Non-cooperative Power control Game) has and only have a Nash Equilibrium point, shows but study, and this Nash Equilibrium point is pareto ineffective.For this reason, the people such as Goodman introduce cost mechanism, set up a kind of non-cooperative power control betting model (NPGP:Non-cooperative Power control Game of Pricing) based on linear price, its cost function design is the selfishness in order to suppress user's competition, and each user is paid a price to the resource using.Result shows, NPGP can improve the Pareto efficiency of Nash Equilibrium point, but is not optimum, and its deficiency is utility function at power degeneration and produce degenerate solution at zero point.For this reason, C.W.Sung and W.S.Wong introduce to user and produce the non-linear price mechanism of disturbing proportion to be directly proportional, and have built user's utility function from information theory view.What above researcher considered conventionally is to utilize non-cooperative game model to carry out the iterative of a metamessage (power or channel), and its weak point is not have joint Power control and channel to select.
Channel allocation and power control techniques are the primary study contents that resource is distributed.The people such as Song Wuhua propose a kind of combined channel based on non-cooperative game and the PIWF algorithm of power division, improve Pareto efficiency by pricing mechanism.Although pricing mechanism can be selected cost function, the seeking of best price function needs central entity control, is not suitable for distributed network.Hao He, the people such as Jie Chen have built that a combined channel is selected and the latent betting model of power division, and under interference-limited prerequisite, abstract is a nonlinear optimal problem that can improve cognitive radio networks throughput and consideration user fairness.In addition, define the special target function of each transmission node, and the latent betting model substep of structure is sought the optimal solution of problem.Result of study shows, the game of substep orderliness can converge to Nash Equilibrium point, and meets interference constraints restriction.Wherein, Hao He, the nonlinear optimal problem that the people such as Jie Chen propose is as follows:
Will be from node
to node
transmitting-receiving node to being defined as
.If channel
,
be illustrated in
upper all nodes pair of transmission simultaneously, receiver
reception signal and interference ratio (
) can be expressed as:
Wherein,
it is node
arrive
transmission gain,
it is receiver
the thermal noise at place, is generally constant,
it is channel
in
the transmitting power of node.
From
expression formula can find out, if
be less than 1, the numerical value obtaining is so negative.This means now signal to noise ratio (
) too lowly can not continue transmitting and even can have a negative impact to the total throughout of whole network, this is because extra interference always has influence on other link.
Based on above-mentioned analysis, by combined channel select and power division with optimize the problems referred to above:
Subject to
Wherein
it is the right set of transmitting-receiving node.
to be operated in channel
on primary user.
a two-valued variable, if channel
on link
enliven, it is 1; Otherwise be 0.Constraints
mean
have and can only be in channel group
in a channel.
This nonlinear optimal problem is intended to maximize total throughout
.All transmitting nodes are intended to maximize target function, thereby, not only need to consider in target function
, also to consider the interference that primary users bear.Thus, definition is operated in channel
on primary user
as follows:
Wherein
that the cognition wireless network that primary user y is subject to disturbs,
be
the maximum interference that middle primary user is subject to,
it is the summation of all channel maximum interference.Exceed threshold value for the interference on channel
, further definition:
Wherein
represent the total interference on these channels.Then, the definition providing before utilizing, can objective definition function be:
The weak point that above method exists is: the first, really do not realize the co-allocation of distributed power ratio control and channel; The second, do not take into full account the phase mutual interference between cognitive user.But, for realizing distributed associating power and channel allocation, between each cognitive user, needing mutual bulk information, this has just increased the complexity of each cognitive user, is also a technical barrier in realization.
Summary of the invention
Technical problem to be solved by this invention is just to provide joint Power and method for channel allocation in a kind of cognition wireless network, the interference that it can fully take into account (CR-to-CR) between (CR-to-PU) between primary user and cognitive user, each cognitive user suppresses, utilize the distributed solution joint Power of cost function and latent betting model and Channel Assignment Problems, improve fairness and the spectrum utilization efficiency of Resource Allocation in Networks.
In order to solve the problems of the technologies described above, technical scheme of the present invention comprises the following steps:
1, interference suffered between primary user and cognitive user on each channel in multichannel cognition wireless network, between each cognitive user is realized and quantizes and limit;
2, the utility function that defines the behavior of each cognitive user access frequency spectrum is throughput
, carry out the distributed utility function optimal solution that solves cognitive user to reach the optimum of overall network performance by building non-cooperative game framework;
3, introduce corresponding cost function the resource occupation behavior of cognitive user is fixed a price, the selfishness of restriction cognitive user;
4, utilize non-cooperative game to be built into a latent betting model, guarantee its convergence.
Accompanying drawing explanation
Accompanying drawing of the present invention is described as follows:
Fig. 1 is the network (WSN) emulation system of one embodiment of the invention;
Fig. 2 is resource allocation flow figure of the present invention;
Fig. 3 is the flow chart of single cognitive user joint Power of the present invention and channel iteration;
Fig. 4 is the function convergence schematic diagram of diving in the present invention.
In Fig. 1,1. primary user base station; 2. primary user; 3. cognitive user.
Embodiment
Below in conjunction with drawings and Examples, the invention will be further described:
The present invention includes following steps:
1, between primary user and cognitive user on each channel in multichannel cognition wireless network between (CR-to-PU), each cognitive user (CR-to-CR) suffered interference realize and quantize and limit.
Suppose an ad-hoc cognitive radio networks being formed by multiple cognitive user, have primary user to exist around,
scope in random distribution
individual cognitive user pair, a cognitive user is to comprising a transmitting node and a receiving node; Frequency spectrum by
kindividual orthogonal channel composition,
k<
n, have simultaneously
individual primary user's node, each primary user can be operated on one or more channels; Defined cognitive user
transmitting power vector
,
for cognitive user
at channel
on transmitting power; Channel
, receiver
reception
can be expressed as:
Wherein,
it is cognitive user
channel
transmission exists
place receive signal interference ratio (
),
it is node
arrive
transmission gain;
it is receiver
the thermal noise at place, is generally constant;
it is channel
in
the transmitting power of node;
jamming equation,
for removing
other cognitive user in addition:
Set
for the maximum interference power of primary user (PU).So, channel
in all cognitive user (CR) must meet total interference of each primary user (PU):
Wherein
it is channel
middle transmitting node
to primary user
obstacle gain,
similar
jamming equation.Set thresholding
far below the actual patient maximum interference of primary users, so just can fine realization for primary user's protection problem.
For certain cognitive user in network, on certain particular channel, except receiving the interference of people having a common goal's neighbor node, self also produces and disturbs people having a common goal's neighbor node.Quantize the co-channel interference that certain cognitive user is born
as follows:
Wherein,
represent that other cognitive user is to present node
transmitting power,
that other cognitive user is to present node
link gain,
similar
jamming equation.
Similarly, the interference that this cognitive user produces other people having a common goal's neighbor node
as follows:
Cognitive user
the interference summation producing from the interference of other cognitive user and to other cognitive user of bearing can be expressed as:
2, the utility function that defines the behavior of each cognitive user access frequency spectrum is throughput
, carry out the distributed utility function optimal solution that solves cognitive user to reach the optimum of overall network performance, i.e. Nash Equilibrium by building non-cooperative game framework.
First, a game of definition is
.In betting model,
it is the set of all transmitting nodes of cognition network;
about transmitting node
behavioral strategy;
target function, transmitting node expectation maximization target function.
Secondly, define each cognitive user nodes utility function
for the throughput of transmitting-receiving node
, throughput equation is as follows:
Consider the co-channel interference between unauthorized user, should guarantee that the interference between each cognitive user can not cause the signal interference ratio at receiver place
lower than minimum signal interference ratio that can demodulation
, correspond in the design of cognitive user utility function, if we find
be less than 1, the numerical value obtaining is for negative.This means now
the too low transmitting node that causes of value can not be launched, and the throughput of whole network is contributed to negative growth.
Again, quantize the interference of CR-to-PU, CR-to-CR, it is as follows as interference constraints condition:
Wherein,
to be operated in channel
on primary user;
it is jamming equation.Constraints
mean transmitting node
to corresponding receiving node
have and can only be in channel group
in a channel, but multiple cognitive user can share same channel; This constraints is embodied in signal interference ratio
computing formula in.
3, lead-in cost function is punished the utility function behavior of fleeing from of cognitive user, suppresses the selfish sexual behaviour of cognitive user;
Consider the interference that primary user is suffered, definition is operated in channel
on primary user
as follows:
Wherein
primary user
the cognition wireless network being subject to disturbs,
be
the maximum interference that middle primary user is subject to.
For certain cognitive user in network, on certain particular channel, except being subject to the interference of people having a common goal's neighbor node, self also produces and disturbs people having a common goal's neighbor node.Defined cognitive user
co-channel interference sum be
:
For ad-hoc network, distributed network architecture makes the cognitive user in network can freely determine the messaging parameter of oneself and correspondingly improves communication link (the higher message transmission rate of self, lower error rate etc.), the resource but the restricted gender of usable spectrum resource and maximum transmitted gross power can cause vying each other between cognitive user, one of its negative effect is exactly that the Pareto efficiency of network equilibrium is low.In the environment that does not have mandatory constraints to reach, cognitive user even can be fled from original utility function according to the benefit of self.For this reason, consider above two kinds of interference, set it as the foundation of the selfish sexual behaviour of restriction cognitive user, user obtains corresponding network throughput by bidding.Lead-in cost function is punished the utility function behavior of fleeing from of cognitive user, suppresses the selfish sexual behaviour of cognitive user.Defined cognitive user
cost function and total utility function as follows:
4, utilize non-cooperative game to be built into a latent betting model, guarantee its convergence.
The object of game is to reach balanced, and namely all participants' strategy can reach a kind of stable state.Nash Equilibrium is the one equilibrium of static game of complete information, is defined as follows:
Game
in, wherein strategy combination
a Nash Equilibrium, if with respect to participant
,
to select in given all the other participant's optimal policies
participant in situation
optimal policy, that is:
Can find out that from definition Nash Equilibrium is a kind of stable state of game, when participant reaches Nash Equilibrium point, can unilaterally deviate from existing state without any a participant.
Solving Nash Equilibrium is more difficult thing, and distributed solving increased difficulty especially.Do not let on person conventionally along with the expansion of game, and iteration is accumulated observation, and wishes that process can converge to equilibrium point.Although this situation is not to have simulated to greatest extent a game process, in the time that scene is modeled as latent game, can guarantee the convergence of game process, guarantee that it converges to Nash Equilibrium.
Latent game is a kind of betting model that utility function meets specific condition.
Definition: game
in, there is the function of diving
meet following some conditions, this game is the game of diving.
Wherein
is-symbol function.Satisfy condition is i) game of accurately diving; Ii satisfies condition) be the latent game of order.
The feature of latent game is: when all participants make decisions in order, latent game can guarantee that game process converges to Nash Equilibrium point in limited step.The searching of latent function is the precondition of game modeling of diving, represent that game overall interests in convergence process promote, in other words, any one participant makes better decision-making and can make latent functional value further promote in convergence process, this point is apparent from definition, and in the game of diving, individual interest is consistent with entire system interests.
If each user from
strategy change over
time,
variation be greater than
corresponding variation, so our just network modelling one-tenth order can be dived game and then existence of assurance Nash Equilibrium point.
Prove as follows:
If right
, hypothesis is below set up:
There is following expression:
Defined from latent game, as long as meet above-mentioned inequality assumed condition,
be
latent function, meet dive game definition of order, network can be modeled as latent betting model.
The game process of diving is the process of cognitive user continuous renewal self channel selection and transmission power information.Effectiveness when first each cognitive user calculates it and use other channel according to the channel selection strategies of current other cognitive user; Then utility function value when each cognitive user is by other channel of more current use is selected current optimum utility channel in order; And then each cognitive user is selected suitable power by the effectiveness of calculating under different transmission power; By cycle calculations, obtain a kind of equilibrium state.
Like this obtain with
as the utility function expression formula of the function of diving, illustrate that the utility function value of game or latent functional value are along with the accumulation of iterative process is constantly to promote, in other words, each participant (cognitive user) in game constantly updates and optimizes channel selection and the power distribution strategies of self by comparison procedure repeatedly, distributed contribution is improved overall network effectiveness, and tend towards stability (Nash Equilibrium).
Between cognition network and primary user's network based on temperature interference model share spectrum resources; Total utility function is channel
middle cognitive user
the throughput obtaining deducts to disturb the cost function as cost.For the protection to primary user, cognitive network resource is nowhere near.If there is not the condition that suppresses transmitting node, so each cognitive user can be from the one-sided effectiveness that increases of number one in the time of access channel
, some inappropriate transmitting nodes can unilaterally be selected maximum transmitting power transmitting and not consider other situation, will certainly cause like this other suitable transmitting node because total interference of primary user is approached threshold value and can not be launched.Therefore introduce
as the restriction to cognitive user (
,
constant, as the coefficient of linear cost function).Cognitive user, selecting most suitable power and channel when maximizing the throughput self obtaining, must be considered the various interference that corresponding transmitting power produces, and therefrom obtain the cost after frequency spectrum resource with this as it.
Introduce
, correspondingly cognitive user can tend to select most suitable channel, and this cognitive user is when the work of this channel
can be less than its value in other channel work.Obviously, the throughput of cognition wireless network and fairness can be improved thus.
the selected of value will be a practical problems;
too small, may cause so interference to suppress too small and network performance is reduced;
excessive, may cause so disturbing and suppress by force and make to promote network throughput and become very difficult;
numerical value may affect convergence rate.From point of theory, as embodying the factor of primary user being disturbed to inhibition,
larger value should be got, excessively interference can be reduced rapidly, protection primary user while even exceeding primary user's thresholding to guarantee to disturb.
Introduce
.If
a kind of to the rigid macro adjustments and controls of cognitive user nodes, so
can be that one regulates and controls soft ground of cognitive user microcosmic.Cognitive user is weighed effectiveness and cost, and the messaging parameter of oneself is suitably adjusted.If the Internet resources of self asking for are more, its cost of paying is just higher so; If lacking of resource occupying, so corresponding paying is also just lower.
value will be also a practical problems,
the value throughput order of magnitude or less that coexists that should obtain with cognitive user, to regulate the effectiveness of each cognitive user when the Nash Equilibrium, improve Pareto efficiency.
Thereby each cognitive user increases the target function of self continuously, in this accurate game, to reach Nash Equilibrium point be a kind of method of practicality.In order to realize the Continuous behavior of each cognitive user, we introduce a kind of simple random access mechanism: each cognitive user with
probability upgrade self strategy.In particular, each cognitive user incipient stage of each time slot with
probability determines whether in Common Control Channel, carry out transfer of data, whether does according to the value of utility of current utility function the decision-making making new advances.If success, corresponding cognitive user access network, carries out transfer of data; Otherwise continue to intercept channel.Suppose that cognitive user all can correct perception to all channels, in the Common Control Channel that the resource request of other cognitive user and Signalling exchange information all can reach at the whole network, broadcast.Cognitive user can be known channel selection information separately each other.This scheme has guaranteed that an average time point only has a cognitive user to take action, and meanwhile exceeding the probability that a cognitive user takes action is non-zero.Take action when two or multiple cognitive user, latent function may temporarily decline simultaneously, but this can not destroy the trend of the overall monotone increasing of potential function.
Select joint Power to distribute and these two uncertain factors of channel, by distributed algorithm, resource is effectively distributed, as shown in Figure 2 and Figure 3.Wherein Fig. 2 is overall flow figure, and Fig. 3 is supplementing of Fig. 2, take the channel of determining optimum utility as cutting point, provides the concrete operations flow process of each cognitive user.Algorithm is as follows:
Step1: set ad-hoc network topology, produce at random cognitive user pair, calculate communication node link gain
, initialization cognitive user effectiveness
, channel is selected
, power
, the coefficient of bidding
,
, given cognitive user nodes target throughput
; Initialization random access procedure;
Step2: each cognitive user is calculated successively respectively current power and selected
, channel is selected
under effectiveness
and throughput
;
Step4: judge whether that all cognitive user nodes all reach maximum transmission power, if so, finish algorithm; Otherwise, carry out step5;
Step5: judge whether to reach maximum iteration time, if so, finish algorithm; Otherwise, carry out step6;
Step6: present cognitive user node calculates current power successively
under, use the effectiveness of other available channel
, and throughput
;
Step7: present cognitive user node compares the effectiveness under different channels selection
, select and there is the channel of maximum utility and upgrade, calculate corresponding throughput simultaneously
; Otherwise, return to step3;
Step8: judge the throughput that present cognitive user node obtains
if reduce node transmitting power, and enter step2; Otherwise, increase node transmitting power, and enter step2.
Comparing with the scheme for combining of original power and channel allocation, there is following advantage in this programme:
1, utilize non-cooperative game model effectively to solve distributed cognition network joint Power and Channel Assignment Problems, change the Nonlinear Optimization Problem of general objective function (aggregate network throughput) into distributed cognition user's target function.
2, build the convergence of the corresponding betting model assurance network of diving, guarantee the existence of Nash Equilibrium.
3, will disturb the cost function suppressing as cognitive user, user obtains corresponding Internet resources (throughput) by bidding.
4, take into full account the interference of CR-to-PU, CR-to-CR, realize and disturb the quantification and the restriction that suppress, and as cost function.
Embodiment
The present invention is directed to ad-hoc network model, ascending resource (power and channel) assignment problem in the single multichannel cognition wireless network of distributed solution.The type of cognitive user can be mobile phone, vehicle radio station, PDA or other.A network (WSN) emulation system is as shown in Figure 1: cognitive user
be randomly dispersed in the region of 100m*100m, available orthogonal number of channel K=4, maximum iteration time is 1000 times.All cognitive user select transmitting power and available channel as init state at random.Suppose that all cognitive user all can correct perception to all channels, in the Common Control Channel that the resource request of other cognitive user and Signalling exchange information all can reach at the whole network, broadcast.Cognitive user can be known transmitting power and corresponding channel selection information separately each other.Between not existing center to control, the enforcement of this method information interaction between each cognitive user that places one's entire reliance upon, each cognitive user makes a policy in strict accordance with algorithm.The flow process of concrete steps as shown in above-mentioned Fig. 2, Fig. 3.
Fig. 4 is the function (cost function of diving
) convergence schematic diagram, the convergence process of function of diving is consistent with whole network convergence process, because the betting model of diving all can make overall network performance further be promoted while making any one participant make better decision-making in convergence process.The utility function design of introducing price mechanism has fully demonstrated the interference inhibition between interference inhibition and the cognitive user to primary user, improves the utilance of resource distributional equity and frequency spectrum.
The foregoing is only better embodiment of the present invention; be not limited to the present invention; technical staff under any the present invention; do not departing under the prerequisite of the disclosed spirit and scope of the present invention; can do any modification and variation what implement in form and in details; but scope of patent protection of the present invention, still must be as the criterion with the scope that appending claims was defined.
Claims (7)
1. joint Power and a method for channel allocation in cognition wireless network, is characterized in that, comprises the following steps:
(1) interference suffered between primary user and cognitive user on each channel in multichannel cognition wireless network, between each cognitive user is realized and quantizes and limit;
(2) utility function that defines the behavior of each cognitive user access frequency spectrum is throughput
, carry out the distributed utility function optimal solution that solves cognitive user to reach the optimum of overall network performance by building non-cooperative game framework;
(3) introduce corresponding cost function the resource occupation behavior of cognitive user is fixed a price, the selfishness of restriction cognitive user;
(4) utilize non-cooperative game to be built into a latent betting model, guarantee its convergence.
2. joint Power and method for channel allocation in a kind of cognition wireless network according to claim 1, is characterized in that, in step (1):
Suppose an ad-hoc cognitive radio networks being formed by multiple cognitive user, have primary user to exist around,
scope in random distribution
individual cognitive user pair, a cognitive user is to comprising a transmitting node and a receiving node; Frequency spectrum by
kindividual orthogonal channel composition,
k<
n, have simultaneously
individual primary user's node, each primary user can be operated on one or more channels; Defined cognitive user
transmitting power vector
,
for cognitive user
at channel
on transmitting power; Channel
, receiver
reception
can be expressed as:
Wherein,
it is cognitive user
channel
transmission exists
place receive signal interference ratio (
),
it is node
arrive
transmission gain;
it is receiver
the thermal noise at place, is generally constant;
it is channel
in
the transmitting power of node;
jamming equation,
for removing
other cognitive user in addition:
Set
for primary user's maximum interference power, so, channel
in all cognitive user must meet total interference of each primary user:
Wherein
it is channel
middle transmitting node
to primary user
obstacle gain,
similar
jamming equation;
For certain cognitive user in network, on certain particular channel, except receiving the interference of people having a common goal's neighbor node, self also produces and disturbs people having a common goal's neighbor node; Quantize the co-channel interference that certain cognitive user is born
as follows:
Wherein,
represent that other cognitive user nodes is to present node
transmitting power,
that other cognitive user nodes is to present node
link gain,
similar
jamming equation;
The interference that this cognitive user produces other people having a common goal's neighbor node
as follows:
Cognitive user
the interference summation producing from the interference of other cognitive user and to other cognitive user of bearing can be expressed as:
3. joint Power and method for channel allocation in a kind of cognition wireless network according to claim 2, is characterized in that, in step (2):
First, a game of definition is
; In betting model,
the set of all transmitting nodes of cognition wireless network,
about transmitting node
behavioral strategy;
target function, transmitting node expectation maximization target function;
Secondly, define each cognitive user nodes utility function
for the throughput of transmitting-receiving node
, throughput equation is as follows:
Moreover, quantizing the interference of CR-to-PU, CR-to-CR, it is as follows as interference constraints condition:
4. joint Power and method for channel allocation in a kind of cognition wireless network according to claim 3, is characterized in that, in step (3):
Definition is operated in channel
on primary user
as follows:
Wherein
primary user
the cognition wireless network being subject to disturbs,
be
the maximum interference that middle primary user is subject to;
5. joint Power and method for channel allocation in a kind of cognition wireless network according to claim 4, is characterized in that, in step (4):
as cost function, each user's total utility function is:
6. joint Power and method for channel allocation in a kind of cognition wireless network according to claim 5, is characterized in that: with
as latent function,
α, βfor coefficient undetermined; Network struction is become to the order betting model of diving, can converge to Nash Equilibrium point and also meet rapidly the restriction of interference constraints by distributed game orderly, guaranteed the convergence of network.
7. joint Power and method for channel allocation in a kind of cognition wireless network according to claim 6, is characterized in that, selects joint Power to distribute and these two uncertain factors of channel, by distributed algorithm, resource effectively distributed; Its algorithm is as follows:
Step1: set ad-hoc network topology, produce at random cognitive user pair, calculate communication node link gain
, initialization cognitive user effectiveness
, channel is selected
, power
, the coefficient of bidding
,
, given cognitive user nodes target throughput
; Initialization random access procedure;
Step2: each cognitive user is calculated successively respectively current power and selected
, channel is selected
under effectiveness
and throughput
;
Step4: judge whether that all cognitive user nodes all reach maximum transmission power, if so, finish algorithm; Otherwise, carry out step5;
Step5: judge whether to reach maximum iteration time, if so, finish algorithm; Otherwise, carry out step6;
Step6: present cognitive user node calculates current power successively
under, use the effectiveness of other available channel
, and throughput
;
Step7: present cognitive user node compares the effectiveness under different channels selection
, select and there is the channel of maximum utility and upgrade, calculate corresponding throughput simultaneously
; Otherwise, return to step3;
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