CN102917362B - Multi-domain resource allocation method based on collaborative optimization in ubiquitous wireless network - Google Patents

Abstract

The establishing of a multi-domain resource allocation model based on collaborative optimization in a ubiquitous wireless network is based on basic principles of economics, takes research of a common multi-domain resource allocation model as a main line, uses the synergetics and the chaos theory as a main research tool, combines with service QoS (Quality Of Service) for guarantee, and comprehensively considers multiple domains of users, services, networks, resources and the like, so as to to achieve the balanced distribution of ubiquitous wireless network resources. According to the invention, a wireless network is regarded as a supply and demand model, the allocation of the network resources is regarded as a production and consumption model; production and consumption model is combined with user service QoS, adaptive modulation technique, the effective bandwidth theory and the effective capacity theory are used for allocating resources to users, the charge circumstances of operators are taken into consideration, and utility value is used for measuring satisfaction of the users; and from mesoscopic perspective, the operators operating various heterogeneous wireless networks are regarded as collaborative subsystems, the establishing of the multi-domain resource allocation model of multiple operators based on the collaborative optimization in the ubiquitous wireless network is achieved based on the chaos theory and the perspective of the synergetics, and the resource allocation of the ubiquitous wireless network is realized by combing the chaotic optimization and collaborative optimization.

Description

Based on the multi-domain resource allocation method of cooperate optimization under ubiquitous wireless network

Technical field

The present invention is based on the visual angle of chaology and synergetics, utilize economics general principle and method, ensure in conjunction with QoS of survice, consider multiple territories such as user, business, network and resource, establish the multiple domain resource allocator model based on cooperate optimization under ubiquitous wireless network, be applied to general Resourse Distribute in the wireless network.

Background technology

Ubiquitous wireless network, can, whenever and wherever possible for people, allow people enjoy the wireless network of ubiquitous service, its communication service object expands to anything by people, as Smart Home, intelligent transportation etc.Existing ubiquitous radio network technique comprises the wireless communication protocols such as 3G, LTE, GSM, WLAN, WiMax, RFID, Zigbee, Bluetooth and technology.These networks also exist very big-difference, and be difficult to phase trans-substitution each other, society is about to march toward the wireless Internet epoch, and the demand of people to radio communication is also constantly expanded, but rare wireless network resource is the suppression to the unlimited desire of people.This just requires that researcher is under set Radio Resource condition, continually develops new technology to promote the development of wireless communication technology.

Under set wireless network resource, the largest data transfer digit rate that network can provide is certain.From macroscopic perspective, operator continually develops new technology to be devoted to improve network resource utilization, elevator system throughput; From microcosmic angle, user's real concern be that can the resource distributing to him meet its demand.Operator can not provide free service, therefore very large Practical significance is had to the research of pricing mechanism, operator will drop into a large amount of costs to the construction of network and providing of network service, need to regain it by price charging invest and obtain income, meanwhile, the charge of operator can play again the effect guiding consumer consumption behavior.

Chaology is a kind of method having matter thinking and quantitative analysis concurrently, in order to research trends system, is that system is from a kind of Evolution Theory becoming suddenly disordered state in order.Synergetics be then research cooperative system from the Evolution of disorder to order, cooperative system refer to be made up of, can be formed in an ad-hoc fashion macroscopic view many subsystems space, time or Function in Order structure development system.Chaology and synergetics are closely connected.The development of ubiquitous wireless network depends on existing plurality of wireless networks technology, and due to internetwork otherness, these networks are by long-term co-existence, and from macroscopic perspective, ubiquitous wireless network comprises multiple heterogeneous wireless network; See angle from Jie, these heterogeneous wireless networks can be considered as collaborative subsystem by respectively, as GSM subsystem, LTE subsystem, wlan subsystem etc.; From microcosmic angle, the network user can be considered as collaborative subsystem again by respectively, and the quantity of the network user is huge, and the consumer behavior of each user is random, but is stable periodic motion.

Based on the visual angle of chaology and synergetics, the business that user is various under ubiquitous wireless network and the demand of QoS, adopt economic theory and method, ensure in conjunction with QoS of survice, consider multiple territories such as user, business, network and resource, setting up the multiple domain resource allocator model based on cooperate optimization, is the inexorable trend of ubiquitous wireless network commercialization development.

Summary of the invention

Technical problem: the object of the invention is the visual angle based on chaology and synergetics, adopt Principles of Economics and method, ensure in conjunction with QoS of survice, consider multiple territories such as user, business, network and resource, set up the multiple domain resource allocator model based on cooperate optimization under ubiquitous wireless network, realize the equilibrium assignment of ubiquitous wireless network resource.

Technical scheme: the present invention is based on economics general principle and method, serve as theme with the general multiple domain resource allocator model of ubiquitous wireless network, be main research tool with synergetics and chaology, ensure in conjunction with QoS of survice, consider multiple territories such as user, business, network and resource, set up the multiple domain resource allocator model based on cooperate optimization under ubiquitous wireless network, driven the optimization of Resourse Distribute by the cooperate optimization of each heterogeneous wireless network.

The present invention plans ubiquitous wireless network and is considered as a supply-demand model, and operator sells the right to use of wireless network resource, and user buys the right to use of Radio Resource to operator according to its demand.Wireless network resource distributed and regard a production-consumption model as, the resources of production comprise frequency spectrum, time, space, energy, code word, buffering area etc. in the wireless network; The producer is various network entity and control algolithm; Consumer is the miscellaneous service of user.Ensure in conjunction with QoS of survice, adopt adaptive modulation technology, effective bandwidth theory, available capacity theoretical, utilize the appropriately combined network data transmission service providing some of dissimilar resource, operator can not provide free service, should consider the charge situation of operator.

Different isomerization wireless network is runed by different operators, sees angle, different operators is considered as collaborative subsystem from Jie, based on the visual angle of chaology and synergetics, sets up multi-operator and to fix a price the optimization of synergistic mechanism.In ubiquitous wireless network, the scheduling of each heterogeneous wireless network take frame as the cycle, for each scheduling frame, general total number of users is in the wireless network certain, with ubiquitous wireless network user social welfare maximization for aims of systems, with the cooperate optimization of each isomery RNS for constraint, consider that ubiquitous wireless network has complexity, chaotic property and concertedness, cooperate optimization and chaos optimization are combined, complete the optimization of ubiquitous wireless network.

Based on the visual angle of the multi-domain resource allocation method of cooperate optimization based on chaology and synergetics under the ubiquitous wireless network of the present invention, realized the equilibrium assignment of resource by the operation of ubiquitous wireless network cooperate optimization mechanism, it comprises following flow process:

1). the division of collaborative subsystem: user selects network concurrent to go out request access signal based on the resource allocator model of maximization of utility;

2). chaos optimization: the chaos optimization of each collaborative subsystem internal;

3). cooperate optimization: the cooperate optimization between collaborative subsystem;

4). combined optimization: the combined optimization of chaos optimization and cooperate optimization is to reach the Resourse Distribute of ubiquitous wireless network;

Wherein:

Being divided into of described collaborative subsystem: ubiquitous wireless network comprises multiple heterogeneous wireless network, and different heterogeneous wireless networks is managed by different operators, a user may be in the coverage of many carrier networks, user selects the network that its effectiveness can be made maximum based on the resource allocator model of maximization of utility, ensure in conjunction with QoS of survice in production-consumption model, consider multiple territories of user, business, network and resource, adaptive modulation technology is adopted to improve spectrum efficiency, for user i distributes power resource p _i, adopt the arrival process of effective bandwidth theory modeling business, adopt the service process of available capacity theoretical abstraction business, when effective bandwidth obtains balanced with available capacity, reach the optimum state of business service, for user i distributes certain frequency spectrum resource f _iwith time interval resource t _i, complete effective distribution of resource, so just determine in certain heterogeneous network, ensure user iQoS grade π _istock number (the f distributed _i, t _i, p _i), the charge m of operator _i=G (f _i, t _i, p _i) and value of utility u _iuser i will select the maximum operator of its value of utility and sends request access signal, guarantees each network non-overloading simultaneously, so just can determine the active user of each operator at current scheduling frame, each operator is considered as collaborative subsystem, for Resourse Distribute is prepared.

Described chaos optimization is: the resource that each heterogeneous network has is set, in each scheduling frame can only service so many user and ensure their QoS, consider the complexity of each collaborative subsystem, chaotic property, the chaos optimization of each collaborative subsystem internal is with the consistency constraint of its correspondence for optimization aim, as shown in Figure 3, Optimizing Flow as shown in Figure 4 for Optimized model.

Described cooperate optimization is: consider the complexity of ubiquitous wireless network, concertedness, with ubiquitous wireless network user social welfare maximumly turn to target function, with each isomery RNS cooperate optimization for constraint, as shown in Figure 3, Optimizing Flow as shown in Figure 4 for Optimized model.

Described chaos optimization and the combined optimization of cooperate optimization are: Optimized model as shown in Figure 3, Optimizing Flow as shown in Figure 4, chaos optimization is entered after determining each collaborative subsystem, cooperate optimization will be entered after satisfying condition, wherein when the less system resource of number of users is superfluous, by the QoS grade π of adjustment user request _ito promote its value of utility, social welfare and resource utilization are improved, progressively realize the combined optimization of chaos optimization and cooperate optimization to complete the Resourse Distribute of ubiquitous Radio Network System, finally just can determine the QoS grade π of heterogeneous network n that user i selects, business _i, distribute stock number (f _i, t _i, p _i), corresponding spending amount m _iand the value of utility u of user _i(π _i, m _i).

Beneficial effect: the visual angle that the present invention is based on chaology and synergetics, utilize economics general principle and method, consider multiple territories such as user, business, network and resource, ensure in conjunction with QoS of survice, establish the multiple domain resource allocator model based on cooperate optimization between a kind of heterogeneous wireless network, be applied to the Resourse Distribute of ubiquitous wireless network, make the supply-demand of network reach balanced.

Accompanying drawing explanation

Fig. 1 is the ubiquitous wireless network market mechanism block diagram based on supply-demand.

Fig. 2 is the multiple domain resource allocator model block diagram based on producing-consuming.

Fig. 3 is the ubiquitous radio network optimization model framework chart based on cooperate optimization.

Fig. 4 is the ubiquitous radio network optimization FB(flow block) based on cooperate optimization.

Fig. 5 is the multiple domain resource allocator model block diagram based on cooperate optimization under ubiquitous wireless network.

Embodiment

The present invention is based on economics general principle, serve as theme with general multiple domain resource allocator model research, be main research tool with chaology and synergetics, ensure in conjunction with QoS of survice, consider multiple territories such as user, business, network and resource, set up the multiple domain resource allocator model based on cooperate optimization under ubiquitous wireless network, realize the equilibrium assignment of ubiquitous wireless network resource.

Related economic principle: effectiveness refers to the tolerance met by consuming or enjoy the leisure etc., oneself demand, desire etc. being obtained for consumer.With it, economist explains how rational consumer can bring their limited Resourse Distribute on maximum satisfied commodity to them.Relevant economics key concept and principle have: diminishing marginal benefits principle, Pareto optimality, social welfare and fairness.Diminishing marginal benefits principle refers to that consumer is when goods for consumption, and the effectiveness of per unit article to consumer is different, and they taper off relation.Pareto optimality, referring to a kind of state of Resourse Distribute, when not making anyone circumstances degenerate, and the situation of some people can not be made again to improve.Social welfare maximization maximizes more wide in range concept than user total utility, and here the latter can be regarded as the former a kind of special shape.

The present invention establishes the collaborative market model of a multi-operator, and its supply-demand relationship describes as Fig. 1.Resource Allocation in Networks is regarded as a production-consumption model, and as Fig. 2, in wireless network, the resources of production comprise frequency spectrum, time, space, energy, code word, buffering area etc.; The producer is various network entity and control algolithm; Consumer is the miscellaneous service of user.Utilize the appropriately combined of dissimilar resource, the producer can provide the network data transmission service of some.Hypothetical network has five class resources: frequency spectrum resource F, time resource T, space resources S, energy resource P and code source C, can provide two class data transport service to multiple user: the service G having QoS and the service E done one's best.Production function represents given combination of resources, can output how many products or service; Consumption function represents the product of user according to service request or the amount of service.Operator can not provide free service, should consider the charge situation of operator in above-mentioned production-consumption model, and operator will charge, assuming that the charge m of operator to the resource of user's request _i=G (f _i, t _i, s _i, p _i, c _i), f _ifor the amount that system is the frequency spectrum resource that user i distributes; t _ifor the amount that system is the time resource that user i distributes; s _ifor the amount that system is the space resources that user i distributes; p _ifor the amount that system is the energy resource that user i distributes; c _ifor the amount that system is the code source that user i distributes.Parameter (f _i, t _i, s _i, p _i, c _i) determination depend on user's request, i.e. business datum arrival process, the QoS grade π also selected with user _irelevant, QoS of survice comprises: bit error rate requirement, the limit of time delay, violation probability and packet loss, operator is that the QoS grade that user distributes must not lower than the QoS grade of user's request, and the present invention adopts adaptive modulation technology, effective bandwidth theory, available capacity theoretical to ensure QoS of survice.And user will meet situation according to its QoS and an evaluation is made in charge, namely user utility value is levied, and makes g represent the Internet resources (one or more dimensions) distributing to user, assuming that utility function is u (g), has according to law of diminishing marginal utility: represent that the effectiveness u (g) of user increases along with g and increases, but the amplitude increased diminishes, effectiveness u (g) has maximum: like this, we can determine scope 0≤u (g)≤1 of user utility value.

Adaptive modulation technology: mainly comprise following four kinds of modulation systems: BPSK, QPSK, 16QAM, 64QAM, corresponding planisphere is counted and is respectively M=2,4,16,64.Distinguishingly, M=0 corresponds to " power failure " pattern, does not namely transmit data.The fading severity of user i is depended in the determination of Adaptive Modulation pattern, i.e. the modulating mode of user i the function of signal to noise ratio γ, definition $M_l^i\left(\mathrm{\γ}\right)=\frac{\mathrm{\γ}}{{\mathrm{\γ}}_0/K}$ Wherein $K=\frac{-1.5}{\mathrm{In}(5\·{\mathrm{BER}}_i)},$ γ ₀it is a constant.Like this, we need by be divided into the continuum of 5 non-overlapping copies, 6 boundary points, are labeled as respectively m ₀=0, M ₁=2, M ₂=4, M ₃=16, M ₄=64, M ₅=∞.If drop on interval [M _l, M _l+1), then the modulating mode adopted this user i is l, distinguishingly, drop on interval [M ₀, M ₁), modulating mode is l=0, i.e. " power failure " pattern.Maximize model according to spectrum efficiency, namely maximize

$E\left[{\log }_2{M}_l^i\left(\mathrm{\γ}\right)\right]=\∫{\log }_2(1+\frac{1.5\mathrm{\γ}}{-\mathrm{In}(5\·{\mathrm{BER}}_i)}\frac{P\left(\mathrm{\γ}\right)}{\stackrel{\‾}{P}})p\left(\mathrm{\γ}\right)\mathrm{d\γ}$

Wherein $\frac{P\left(\mathrm{\&gamma;}\right)}{\stackrel{\&OverBar;}{P}}=\left\{\begin{array}{c}\frac{1}{{\mathrm{\&gamma;}}_0}-\frac{1}{\mathrm{\&gamma;K}},\mathrm{\&gamma;}\&GreaterEqual;{\mathrm{\&gamma;}}_0/K\\ 0,\mathrm{\&gamma;}<{\mathrm{\&gamma;}}_0/K,\end{array}\right.,$ $K=\frac{-1.5}{\mathrm{In}(5\&CenterDot;{\mathrm{BER}}_i)},$ BER _ifor the target error rate of user i.According to power limitation condition we derive γ ₀equation

$\frac{\sqrt{\mathrm{\&pi;}}}{\sqrt{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}K}\mathrm{erfc}\left(\frac{{\mathrm{\&gamma;}}_0}{\sqrt{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}}K}\right)-\frac{1}{{\mathrm{\&gamma;}}_0}\exp (-\frac{{\mathrm{\&gamma;}}_0^2}{\stackrel{\&OverBar;}{\mathrm{\&gamma;}}{K}^2})+1=0$

Determine γ ₀after value, just can determine value, thus determine Adaptive Modulation pattern l and the power P of user i _i, sign bit rate residing for user i, the planisphere of modulating mode l is counted, and detail parameters feature sees the following form.

Markov channel model: ζ _nt () represent that frame t transmits status of processes on subchannel n, if the transmission data of frame t on subchannel n are by reliable reception, we then think that this state is " good ", i.e. ζ _n(t)=G; Similarly, the data of transmission are failed by reliable reception, and we say that this state is " bad ", i.e. ζ _n(t)=B.Then ζ _nt the judgement of () state is

${\mathrm{\&zeta;}}_n\left(t\right)=\left\{\begin{array}{c}B,{\left|{\mathrm{\&xi;}}_n\left(t\right)\right|}^2\&le;{\mathrm{\&gamma;}}_n^{*}\\ G,{\left|{\mathrm{\&xi;}}_n\left(t\right)\right|}^2>{\mathrm{\&gamma;}}_n^{*}\end{array}\right.$

Wherein ξ _nt () is the envelope of carrier wave on frame t subchannel n, based on the character of discrete joint network model, the ζ when frame t _n(t) ζ as well and when frame t+1 _n(t+1) still probability is as well p (ζ _n(t+1)=G| ζ (t) _n=G)=1-q _n, or when frame t+1 ζ _n(t+1) becoming bad probability is p (ζ _n(t+1)=B| ζ _n(t)=G)=q _n; Similarly, the ζ when frame t _n(t) for bad and when frame t+1 ζ _n(t+1) still for bad probability is p (ζ _n(t+1)=B| ζ _n(t)=B)=1-η _n, or when frame t+1 ζ _n(t+1) probability become is p (ζ _n(t+1)=G| ζ _n(t)=B)=η _n.Then the transfer matrix of subchannel n is

$T_n=\left[\begin{array}{cc}1-q_n& q_n\\ {\mathrm{\&eta;}}_n& 1-{\mathrm{\&eta;}}_n\end{array}\right]$

Wherein $q_n=\frac{1-e^{-{\mathrm{\&gamma;}}_n^{*}}}{e^{-{\mathrm{\&gamma;}}_n^{*}}}{\mathrm{\&eta;}}_n,$ Q () represents MarcumQ function, ρ _n=J _o(2 π f _mt _f), J _o() is a class 0 rank Bessel function, f _mfor maximum doppler frequency, T _ffor the frame period.

Available capacity is theoretical: be the service process for business Network Based.For the service process of a H state FSMC model, available capacity can be expressed as

$E_C\left(\mathrm{\&theta;}\right)=-\frac{1}{\mathrm{\&theta;}}\log \left\{\mathrm{\&delta;}\right[T\&CenterDot;\mathrm{\&Phi;}\left(\mathrm{\&theta;}\right)\left]\right\}$

Wherein T is H × H transition probability matrix, and δ () is matrix spectral radius multiplier, r _h, h=1,2K, H, representing can by the bit number of destination address reliable reception in a frame, and θ is QoS index.Time slot allocation is by the s of a subchannel n time slot allocation to user i, and therefore θ is expressed as with s being the function of independent variable by we then user i is in the available capacity of 2 State Markov Model of subchannel n

$E_C^{i,n}\left({\mathrm{\&theta;}}_n^i\left(s\right)\right)=-\frac{1}{{\mathrm{\&theta;}}_n^i\left(s\right)}\log \left\{\mathrm{\&delta;}\right[T_n{\mathrm{\&Phi;}}_n^i\left({\mathrm{\&theta;}}_n^i\left(s\right)\right)\left]\right\}$

Wherein for user i determines the sign bit rate after modulation system.Then closed expression can be written as

$\mathrm{\&delta;}\left[T_n{\mathrm{\&Phi;}}_n^i\left({\mathrm{\&theta;}}_n^i\left(s\right)\right)\right]=\frac{1}{2}\{(1-q_n)e^{-{\mathrm{\&theta;}}_n^i\left(s\right)\frac{s}{S}{T}_{f}B{R}_l^i}+1-{\mathrm{\&eta;}}_n$

$+\sqrt{{[(1-q_n)e^{-{\mathrm{\&theta;}}_n^i\left(s\right)\frac{s}{S}{T}_{f}B{R}_l^i}+1-{\mathrm{\&eta;}}_n]}^2-4(1-q_n-{\mathrm{\&eta;}}_n)e^{-{\mathrm{\&theta;}}_n^i\left(s\right)\frac{s}{S}{T}_{f}B{R}_l^i}}\}$

Wherein n=1,2, K, N.

Effective bandwidth theory: the proposition of effective bandwidth theory is the arrival process based on business.Assuming that business datum arrival process is that { for this process, at time interval, [0, bits of user data number t) arrived, according to effective bandwidth theory, its asymptotic log-moment generating function is defined as A (t), t >=0}, A (t)

$\mathrm{\&Lambda;}\left(\mathrm{\&theta;}\right)=\underset{t\&RightArrow;\&infin;}{\lim }\frac{1}{t}\mathrm{InE}\left[e^{\mathrm{\&theta;A}\left(t\right)}\right],\mathrm{\&theta;}\&GreaterEqual;0$

Data arrival process A (t) of user i business meet certain QoS and require that corresponding effective bandwidth can be expressed as on subchannel n

$E_B^{i,n}\left({\mathrm{\&theta;}}_n^i\left(s\right)\right)=\frac{\mathrm{\&Lambda;}\left({\mathrm{\&theta;}}_n^i\left(s\right)\right)}{{\mathrm{\&theta;}}_n^i\left(s\right)},\&ForAll;{\mathrm{\&theta;}}_n^i\left(s\right)\&GreaterEqual;0$

Assuming that { A (t), t>=0} obeys Poisson distribution, namely n=0,1,2, K, wherein λ is constant, then have

$E_B^{i,n}\left({\mathrm{\&theta;}}_n^i\left(s\right)\right)=\frac{\mathrm{\&lambda;}\&CenterDot;(e^{{\mathrm{\&theta;}}_n^i\left(s\right)}-1)}{{\mathrm{\&theta;}}_n^i\left(s\right)},\&ForAll;{\mathrm{\&theta;}}_n^i\left(s\right)\&GreaterEqual;0$

Balanced Resourse Distribute: effective bandwidth is the modeling of the business arrival process meeting certain QoS parameter request, minimum bandwidth required when namely meeting qos requirement, effective bandwidth it is the function that a business stochastic behaviour and certain QoS require; Available capacity is the abstract of the service process meeting certain QoS parameter request, namely meets the maximum source rate that certain qos requirement lower channel can be supported in theoretical application, for business arrival process and service process, corresponding effective bandwidth respectively and available capacity order

$E_B^{i,n}\left({\mathrm{\&theta;}}_n^i\left(s\right)\right)=E_C^{i,n}\left({\mathrm{\&theta;}}_n^i\left(s\right)\right)$

The function of variable s can be determined for steadily arriving and service process, when data average arrival rate is no more than average service rate, the probability that queue size U (t) exceedes definite threshold Z obeys exponential damping

$p\{\mathrm{Delay}>D_i\}\&ap;\exp (-{\mathrm{\&theta;}}_n^i\left(s\right){\mathrm{\&tau;}}_n^i\left(s\right)D_i)$

Wherein Di is the delay bound of user i.Then channel n distributes to the smallest positive integral timeslot number of user i can be solved by following linear optimization

$s_{\min }^{i,n}=\min \left\{s\right\}$

$s.t.\exp (-{\mathrm{\&theta;}}_n^i\left(s\right){\mathrm{\&tau;}}_n^i\left(s\right)D_i)\&le;{\mathrm{\&epsiv;}}_i$

Wherein, ε _ifor the violation probability of user i, s rounds numerical value.

According to User Priority according to said process, the channel that choice situation is best successively, so just can determine the frequency spectrum f distributing to user i _i, time t _i, power p _ietc. parameter.According to Pareto optimality, it is target that Resourse Distribute can be converted into maximize social welfare (user utility sum), and consumer's monetary budget and scarce resource are the optimization problem of constraints, do not disagree with the income of operator.

From macroscopic perspective, namely ubiquitous wireless network is a network comprising multiple heterogeneous wireless network; See angle from Jie, these heterogeneous wireless networks can be considered as collaborative subsystem by respectively; From microcosmic angle, each user can be considered as collaborative subsystem again by respectively, and the quantity of the network user is huge, and the consumer behavior of each user is random, but is stable periodic motion.Here, we think that heterogeneous networks will be runed by different operators, see angle from Jie, different operators is considered as collaborative subsystem, based on the visual angle of chaology and synergetics, utilize economics general principle and method, set up the method for optimizing resources of multi-operator synergistic mechanism.

The determination of collaborative subsystem: the general resource allocator model based on maximization of utility is

$\underset{m,f,t,s,p,c}{\max }{u}_i(m_i,{\mathrm{\&pi;}}_i)$

$s.t.\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{f}_i\&le;F$

$\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{t}_i\&le;T$

$\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{p}_i\&le;P$

Wherein F, T, P are respectively each scheduling frame intermediate frequency spectrum total amount, total number of timeslots and base station power total amount.A user may be in Duo Jia operator coverage, and according to this optimization, we can know the value of utility that the service of the Ge Jia operator that user can receive reaches, thus selects operator to provide rationality foundation for user.

Ubiquitous wireless network cooperate optimization model: the scheduling of each heterogeneous wireless network time in units of the frame period in ubiquitous wireless network, for each scheduling frame, the resource that each heterogeneous network has is set, in each scheduling frame can only service so many user and ensure their QoS; For user, position may have many heterogeneous networks to cover.With ubiquitous radio network optimization for aims of systems, with each isomery RNS cooperate optimization for constraint, because ubiquitous wireless network has complexity, chaotic property and concertedness, cooperate optimization and chaos optimization are combined, under ubiquitous wireless network based on the multiple domain resource allocator model of cooperate optimization as shown in Figure 3.In model, u is the effectiveness of ubiquitous radio network optimization layer user, for ubiquitous radio network optimization level target function, mean ubiquitous wireless network user social welfare maximization, be respectively the consistency constraint that n subsystem is corresponding, u ^*for the effectiveness of heterogeneous wireless network optimization layer user, u ^*for the optimized results of heterogeneous wireless network optimization layer user utility.

Ubiquitous radio network optimization is divided into the cooperate optimization between the chaos optimization of ubiquitous radio network level and each heterogeneous wireless network, flow process as shown in Figure 4, user utility u ∈ [0,1], in chaos optimization module, variable μ is controling parameters, μ ∈ [0,4], the chaotic characteristic of consideration system, map according to Logistic, we get μ=4, if the u that chaos sequence is produced _ido not meet constraints, need to u _iconvert, make u _i(k)=d _i+ g _iu _{i, n+1}, d _ifor the lower limit of constraints, i.e. d _i=0, g _ifor the absolute value of the upper limit of constraints and the difference of lower limit, i.e. g _i=1, for collaborative heterogeneous wireless network optimization object function, variable α, k, m, c, C are all for counting; In cooperate optimization module, constant ε is used for the precision of control and optimize.

After completing above-mentioned series of optimum, we finally just can determine that user should accept the service of which operator, affiliated QoS grade π _i, the resource (f that is assigned to _i, t _i, s _i, p _i, c _i), corresponding spending amount m _iand the value of utility u of user _i.Under ubiquitous wireless network based on the basic procedure of the multi-domain resource allocation method of cooperate optimization as shown in Figure 4.

Claims (1)

1. under a ubiquitous wireless network based on the multi-domain resource allocation method of cooperate optimization, it is characterized in that the visual angle of the method based on chaology and synergetics, realized the equilibrium assignment of resource by the operation of ubiquitous wireless network cooperate optimization mechanism, it comprises following step:

1). the division of collaborative subsystem: user selects network concurrent to go out request access signal based on the resource allocator model of maximization of utility;

2). chaos optimization: the chaos optimization of each collaborative subsystem internal;

3). cooperate optimization: the cooperate optimization between collaborative subsystem;

4). combined optimization: the combined optimization of chaos optimization and cooperate optimization is to reach the Resourse Distribute of ubiquitous wireless network;

Wherein:

Being divided into of described collaborative subsystem: ubiquitous wireless network comprises multiple heterogeneous wireless network, and different heterogeneous wireless networks is managed by different operators, a user may be in the coverage of many carrier networks, user selects the network that its effectiveness can be made maximum based on the resource allocator model of maximization of utility, ensure in conjunction with QoS of survice in production-consumption model, consider multiple territories of user, business, network and resource, adaptive modulation technology is adopted to improve spectrum efficiency, for user i distributes power resource p _i, adopt the arrival process of effective bandwidth theory modeling business, adopt the service process of available capacity theoretical abstraction business, when effective bandwidth obtains balanced with available capacity, reach the optimum state of business service, for user i distributes certain frequency spectrum resource f _iwith time interval resource t _i, complete effective distribution of resource, so just determine the QoS grade π ensureing user i in certain heterogeneous network _istock number (the f distributed _i, t _i, p _i), the charge m of operator _i=G (f _i, t _i, p _i) and value of utility u _iuser i will select the maximum operator of its value of utility and sends request access signal, guarantees each network non-overloading simultaneously, so just can determine the active user of each operator at current scheduling frame, each operator is considered as collaborative subsystem, for Resourse Distribute is prepared;

Described chaos optimization is: the resource that each heterogeneous network has is set, in each scheduling frame can only service so many user and ensure their QoS, consider the complexity of each collaborative subsystem, chaotic property, the chaos optimization of each collaborative subsystem internal is with the consistency constraint of its correspondence for optimization aim;

Described cooperate optimization is: consider the complexity of ubiquitous wireless network, concertedness, with ubiquitous wireless network user social welfare maximization for target function, with each isomery RNS cooperate optimization for constraint;

Described chaos optimization and the combined optimization of cooperate optimization are: after determining each collaborative subsystem, enter chaos optimization, will enter cooperate optimization after satisfying condition, wherein when the less system resource of number of users is superfluous, by the QoS grade π of adjustment user request _ito promote its value of utility, progressively realize the combined optimization of chaos optimization and cooperate optimization to complete the Resourse Distribute of ubiquitous Radio Network System, finally just can determine the QoS grade π of heterogeneous network n that user i selects, business _i, distribute stock number (f _i, t _i, p _i), corresponding spending amount m _iand the value of utility u of user _i(π _i, m _i);

Described production-consumption model wireless network resource is distributed to regard a production-consumption model as, and the resources of production comprise frequency spectrum, time, space, energy, code word, buffering area etc. in the wireless network; The producer is various network entity and control algolithm; Consumer is the miscellaneous service of user.