CN105792258B - The cross-layer optimizing method of wireless sense network medium-rate and reliability collaboration - Google Patents
The cross-layer optimizing method of wireless sense network medium-rate and reliability collaboration Download PDFInfo
- Publication number
- CN105792258B CN105792258B CN201610227725.3A CN201610227725A CN105792258B CN 105792258 B CN105792258 B CN 105792258B CN 201610227725 A CN201610227725 A CN 201610227725A CN 105792258 B CN105792258 B CN 105792258B
- Authority
- CN
- China
- Prior art keywords
- rate
- indicate
- link
- session
- reliability
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/02—Arrangements for optimising operational condition
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W84/00—Network topologies
- H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
Abstract
The invention discloses the cross-layer optimizing methods that a kind of wireless sense network medium-rate and reliability cooperate with, the present invention is by being converted to a convex problem for the cross-layer optimizing problem that a non-convex wireless sense network rate and reliability cooperate with using variable replacement and the method for introducing intermediate variable, then Duality Decomposition and subgradient method are recycled, distributed optimization algorithm is devised, it can the distributed convex problem solved after conversion.This method has taken into account wireless sense network medium-rate and reliability the two important performance indexes, and the distributed optimization algorithm of proposition is convenient for being converted to the agreement of wireless sense network actual implementation.
Description
Technical field
The present invention relates to wireless communication transmission technique fields, specially in wireless sensor network, are transmitted by single path
The method of rate and its reliability collaboration cross-layer optimizing.
Background technique
Wireless sensor network is as being a kind of novel integrated information acquisition, information processing and information transfer capability in one
Modernization intelligent network information system, it real-time perception and can acquire various accurate environmental datas and target information, real
Communication and information exchange between existing people and physical world greatly improve human knowledge and the ability of physical world are transformed.
The development just like a raging fire of research to the basic theory of wireless sensor network and key technology both at home and abroad at present, and achieve one
Fixed research achievement.For the correlative study of rate control problems, data transmission credibility problem in wireless sensor network
Achieve certain progress.Rate control (also referred to as flow control) is resource fairness and the weight effectively distributed in wireless sensor network
Want technology.
The reliability for guaranteeing data transmission, mainly carries out in terms of two: reducing the probability of data packetloss and error；One
Loss of data or error occur for denier, then retransmission data.More and more scientific research personnel, which have been put into, improves data transmission reliably
Among the research of property.
In recent years, in wireless sensor network, the raising of message transmission rate will necessarily reduce the reliability of data transmission, because
This message transmission rate and data transmission credibility are two basic but conflicting optimization aims, are existed between the two
In one tradeoff.So we must study this compromise optimization problem.
Summary of the invention
In view of the above-mentioned deficiencies in the prior art, it is an object of the present invention to provide a kind of wireless sensor network medium-rate and reliability
The cross-layer optimizing method of collaboration, this method had not only optimized the rate of data transmission but also reliability are made to be guaranteed.
The purpose of the present invention is achieved through the following technical solutions: a kind of wireless sensor network medium-rate and reliability
The cross-layer optimizing method of collaboration, comprising the following steps:
(1) the cross-layer optimizing problem P of wireless sense network rate and reliability collaboration is established1:
Wherein, xfIndicate the transmission rate of session stream, Uf(xf) indicate rate utility function, rl,fIndicate that code rate, B indicate
The bandwidth of link l, σlIndicate noise jamming, plIndicate the power consumed on link l, pnExpression carries out node n when transmission data
The power of consumption, E (rl,f) indicate that session f uses the error probability of link l, one is defined as about code rate rl,fFunction, be
The transmission rate and error probability of tradeoff session stream f, sets weighted value w1, w1Any one value between desirable [0,1]；For
Front and back order-of-magnitude agreement is kept, w is addedf。
(2) by non-convex problem P1It is converted into convex problem P2:
Wherein x 'f=log (xf),cl,fIndicate the f meeting of l chain road
The intermediate variable of words；
(3) using Duality Decomposition method and subgradient method to problem P2Carry out distributed solution.Problem P2Dual problem
P3Are as follows:
max D(β)
s.t.β≥0
Wherein, β is the antithesis factor.Subgradient Algorithm will be used to solve the problem.
Dual problem P3Objective function it is as follows:
Therefore, problem P2Lagrangian it is as follows:
(4) being decomposed into D (β) can the distributed three classes subproblem solved:
Subproblem one:
Subproblem two:
Subproblem three:
Wherein, βl,fIt is the antithesis factor.
(5) use distributed method by P3Distributed solution is carried out, following sub-step is specifically included:
(5.1) β is initializedl,f(0), the number of iterations t=1；By βl,f(0) it brings into three antithesis subproblems, obtains cl,f
(0)、pl(0)、rl,f(0) and x 'f(0)；
(5.2) P is solved using Subgradient Algorithm3, i.e., the β of the t times iteration is found out by following formulal,fThat is βl,f(t):
βl,f(t+1)=[βl,f(t)-kβ(t)(log cl,f(t)+log rl,f(t)-x′f(t))]+
Wherein kβ(t) that indicate is step-length, [z]+=max { 0, z }；(5.3) on session stream more new session f rate x
′f, it maximizes:
And x 'f?Within the scope of, whereinFor the utility function of rate, what L (f) was indicated is session
F flows through the link set of link, sets a weighted value w1；
(5.4) the session updates code rate r on link and chain roadl,f, it maximizes:
βl,f log rl,f-(1-ω1)ωfE(rl,f)
And rl,fWithin the scope of [0,1], wherein E (rl,f) it is the function of error probability, in order to keep the front and back order of magnitude
Unanimously, thus one w of additionf；
(5.5) intermediate variable c is updated on nodel,fWith power pl, it maximizes:
And cl,fUnder two constraints, it is respectively as follows:
(5.6) step (5.1) are repeated and arrives step (5.5), until objective function convergence, obtain the optimal of wireless sensor network
Code rate r*, session rate x*And the power p consumed on link l*, to realize the association of wireless sensor network rate and reliability
Same cross-layer optimizing.
The beneficial effects of the present invention are: the present invention is by utilizing variable replacement and introducing the method for intermediate variable for one
Non-convex wireless sense network rate and the cross-layer optimizing problem of reliability collaboration are converted to a convex problem, then recycle antithesis
Decomposition and subgradient method, devise distributed optimization algorithm, can the distributed convex problem solved after being converted to.This method is taken into account
Wireless sense network medium-rate and reliability the two important performance indexes, the distributed optimization algorithm of proposition is convenient for being converted to nothing
The agreement of line Sensor Network actual implementation.
Detailed description of the invention
Fig. 1 network topological diagram；
Fig. 2 algorithmic statement figure.
Specific embodiment
In order to make above and other objects, features and advantages of the invention more obvious, will make below further details of
Explanation.
Assuming that network topology is G { N, L } wireless sensor network, whereinIndicate the collection of nodes
It closes,What is indicated is the set of the link in network,That indicate is the set of session stream, F (l)
Indicate the set of the session stream on link l, that L (f) is indicated is the link set that session f flows through link, Lout(n) that indicate is n
The set of node outgoing link.
Assuming that all nodes have sufficient energy, which transmits data using single path, it is assumed that each session stream
Transmission rate be xf, utility function Uf(xf), the boundary of the transmission rate of session stream isWork as data
When reaching the encoder of chain road, encoder first decodes it, useful information therein is extracted, then with its code rate
rl,fInformation is encoded, code rate defines 0≤r of justicel,f≤ 1, furthermore code rate rl,fIs defined as: the Information Number of input coding device
According to the ratio of rate and the transmission data rate of output coder.The rate of session f is
It can also be used as relay router and forward since the node in network both can be used as source node transmission data packet
From the data packet of other nodes, the cumulative rate for the data flow transmitted on Radio Link l this requires node is no more than nothing
The maximum link capacity of wired link is usedIt indicates maximum link capacity, then has:
Wherein B is the bandwidth of link l, σlThat indicate is noise jamming, plIndicate the power consumed on link l,
pnIt indicates to carry out the power that node n is consumed when transmission data.
Session f is defined as one about code rate r using link l error probabilityl,fFunction, be represented by E (rl,f), at this
Assume that the function is one about r in inventionl,fIncreasing function, and the function is convex function.
The error probability ξ of node end-to-end may be expressed as:
Under normal circumstances, the error probability of each of the links is very small (i.e. ξ < < 1), so mistake is general end to end
Rate can be approximate are as follows:
In order to obtain a tradeoff between the transmission rate and error probability of session stream f, a weighted value w is set1, w1
Any one value between desirable [0,1]；In order to keep front and back order-of-magnitude agreement, thus one w of additionf。
In conclusion maximization problems P1It can be expressed as follows:
In above problem model, the utility function U of session flow velocity ratef(xf) it is concave function, and E (rl,f) it is convex function
By above-mentioned model, in order to solve the above problem by distributed method, first have to guarantee the problem be one can
Isolated convex problem.It ensure that objective function is concave function now, but due in restrictive condition:
The presence of constraint, so that the feasible set of the problem cannot be guaranteed therefore cannot also guarantee that the problem is for convex set
One separable convex problem.In this part, we will will above show that problem model turns by a series of conversion
Turn to a separable convex problem.
It will constraint
Relevant conversion is carried out, is enabled
Thus by above-mentioned P1Problem is converted into following problem P2:
Wherein x 'f=log (xf),
Using Duality Decomposition method and subgradient method to problem P2Carry out distributed solution.Problem P2Dual problem P3
Are as follows:
max D(β)
s.t.β≥0
Wherein, β is the antithesis factor.Subgradient Algorithm will be used to solve the problem.
The objective function of dual problem is as follows:
Therefore, problem P2Lagrangian it is as follows:
D (β) is decomposed into can the distributed three classes subproblem solved:
Subproblem one:
Subproblem two:
Subproblem three:
Wherein, βl,fIt is the antithesis factor.
Using distributed method by P3Distributed solution is carried out, following sub-step is specifically included:
Step 1: initialization βl,f(0), the number of iterations t=1；By βl,f(0) it brings into three antithesis subproblems, obtains cl,f
(0)、pl(0)、rl,f(0) and x 'f(0)；
Step 2: solving P using Subgradient Algorithm3, i.e., the β of the t times iteration is found out by following formulal,fThat is βl,f(t):
βl,f(t+1)=[βl,f(t)-kβ(t)(log cl,f(t)+log rl,f(t)-x′f(t))]+
Wherein kβ(t) that indicate is step-length, [z]+=max { 0, z }；
Step 3: the rate x ' of more new session f on session streamf, it maximizes:
And x 'f?Within the scope of, whereinFor the utility function of rate, what L (f) was indicated is meeting
Words f flows through the link set of link, sets a weighted value w1；
Step 4: the session updates code rate r on link and chain roadl,f, it maximizes:
βl,f log rl,f-(1-ω1)ωfE(rl,f)
And rl,fWithin the scope of [0,1], wherein E (rl,f) it is the function of error probability, in order to keep the front and back order of magnitude
Unanimously, thus one w of additionf；
Step 5: intermediate variable c is updated on nodel,fWith power pl, it maximizes:
And cl,fUnder two constraints, it is respectively as follows:
Step 6: repeating step 1 and arrive step 6, until objective function convergence, obtain the optimal code rates r of wireless sensor network*、
Session rate x*And the power p consumed on link l*, to realize that the collaboration cross-layer of wireless sensor network rate and reliability is excellent
Change.
This part will pass through the convergence of matlab simulating, verifying distributed algorithm proposed by the invention.Firstly, using
Centralized algorithm is emulated by matlab solves wireless sensor network medium-rate and reliability collaboration cross-layer optimizing problem, obtains complete
Office's optimal solution.Then distributed algorithm proposed by the present invention is used, the problem is then solved by emulation, and obtained most
Termination fruit is compared with the globally optimal solution that centralized algorithm obtains, thus distribution of the verifying based on subgradient Duality Decomposition
Can algorithm obtain globally optimal solution.The convergence result of sensor node total utility is only shown herein.
Following form can be used in the function of utility function and error probability:
Partial simulation parameter setting is as follows:
w1=0.5, wf=0.1, pn=7 (dBm), βl,f=0.01, σl=-50 (dBm),
α=1.1, k=10, while we are provided with
The number of iterations t=600.Pass through Germicidal efficacy being continuously increased with the number of iterations, the variation of total utility.
As shown in Fig. 2, abscissa indicates the number of iterations, ordinate indicates total utility.Black dotted lines are that centralization solves
The value come, black curve are the distributed values for solving and, and with being continuously increased for the number of iterations, total effectiveness is increasing, and
Finally converge on a stationary value.
Claims (1)
1. the cross-layer optimizing method of a kind of wireless sensor network medium-rate and reliability collaboration, which is characterized in that including following step
It is rapid:
(1) the cross-layer optimizing problem P of wireless sense network rate and reliability collaboration is established1:
Wherein,Indicate the set of nodes,Indicate the set of the link in network,Indicate the set of session stream, F (l) indicates the set of the session stream on link l, Lout(n) indicate that n node goes out
The set of link；xfIndicate the transmission rate of session stream, Uf(xf) indicate rate utility function, rl,fIndicate that code rate, B indicate chain
The bandwidth of road l, σlIndicate noise jamming, plIndicate the power consumed on link l, pnNode n disappears when expression carries out transmission data
The power of consumption, E (rl,f) indicate that session f uses the error probability of link l, one is defined as about code rate rl,fFunction, in order to
Weigh the transmission rate and error probability of session stream f, sets weighted value w1, w1Any one value between desirable [0,1]；In order to
Front and back order-of-magnitude agreement is kept, w is addedf；
(2) by non-convex problem P1It is converted into convex problem P2:
Wherein x'f=log (xf),cl,fIndicate the f session of l chain road
Intermediate variable；
(3) using Duality Decomposition method and subgradient method to problem P2Carry out distributed solution；Problem P2Dual problem P3
Are as follows:
max D(β)
s.t.β≥0
Wherein, β is the antithesis factor；The problem is solved using Subgradient Algorithm；
Dual problem P3Objective function it is as follows:
Therefore, problem P2Lagrangian it is as follows:
(4) being decomposed into D (β) can the distributed three classes subproblem solved:
Subproblem one:
Subproblem two:
Subproblem three:
Wherein, βl,fIt is the antithesis factor；
(5) use distributed method by P3Distributed solution is carried out, following sub-step is specifically included:
(5.1) β is initializedl,f(0), the number of iterations t=1；By βl,f(0) it brings into three antithesis subproblems, obtains cl,f(0)、pl
(0)、rl,f(0) and x'f(0)；
(5.2) P is solved using Subgradient Algorithm3, i.e., the β of the t times iteration is found out by following formulal,fThat is βl,f(t):
βl,f(t+1)=[βl,f(t)-kβ(t)(logcl,f(t)+logrl,f(t)-x'f(t))]+
Wherein kβ(t) that indicate is step-length, [z]+=max { 0, z }；
(5.3) on session stream more new session f rate x 'f, it maximizes:
And x 'f?Within the scope of, whereinFor the utility function of rate, what L (f) was indicated is session f stream
The link set for crossing link sets a weighted value w1；
(5.4) the session updates code rate r on link and chain roadl,f, it maximizes:
βl,flogrl,f-(1-ω1)ωfE(rl,f)
And rl,fWithin the scope of [0,1], wherein E (rl,f) it is the function of error probability, in order to keep front and back order-of-magnitude agreement,
To one w of additionf；
(5.5) intermediate variable c is updated on nodel,fWith power pl, it maximizes:
And cl,fUnder two constraints, it is respectively as follows:
(5.6) step (5.1) are repeated and arrives step (5.5), until objective function convergence, obtain the optimal code rates of wireless sensor network
r*, session rate x*And the power p consumed on link l*, thus realize the collaboration of wireless sensor network rate and reliability across
Layer optimization.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610227725.3A CN105792258B (en) | 2016-04-13 | 2016-04-13 | The cross-layer optimizing method of wireless sense network medium-rate and reliability collaboration |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610227725.3A CN105792258B (en) | 2016-04-13 | 2016-04-13 | The cross-layer optimizing method of wireless sense network medium-rate and reliability collaboration |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105792258A CN105792258A (en) | 2016-07-20 |
| CN105792258B true CN105792258B (en) | 2019-02-22 |
Family
ID=56396467
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610227725.3A Active CN105792258B (en) | 2016-04-13 | 2016-04-13 | The cross-layer optimizing method of wireless sense network medium-rate and reliability collaboration |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105792258B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109345809A (en) * | 2018-11-19 | 2019-02-15 | 南京邮电大学 | The distributed optimization method of solar energy radio acquisition system |
| CN110995277B (en) * | 2019-12-06 | 2021-06-01 | 浙江大学 | Multi-layer neural network assisted penalty dual decomposition channel decoding method |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20140123121A (en) * | 2013-04-10 | 2014-10-22 | 한국전자통신연구원 | Method for data transmission in multi-hop network and apparatus therefor |
| CN103369599B (en) * | 2013-06-24 | 2016-07-06 | 天津理工大学 | A kind of many radio frequencies multi-Channel Wireless Mesh Network resource cross-layer optimizing method |
| CN105163380B (en) * | 2015-09-23 | 2018-05-01 | 浙江理工大学 | The distributed cross-layer optimizing method of MIMO wireless multi-hop networks |
-
2016
- 2016-04-13 CN CN201610227725.3A patent/CN105792258B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN105792258A (en) | 2016-07-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104469755B (en) | To relaying the safe transmission method of physical layer maintained secrecy with interfering nodes selection result | |
| CN108737057A (en) | Multicarrier based on deep learning recognizes NOMA resource allocation methods | |
| CN107634911A (en) | Adaptive congestion control method based on deep learning in a kind of information centre's network | |
| CN103596191B (en) | A kind of wireless sensor network intelligent configuration system and method thereof | |
| CN109753082A (en) | Multiple no-manned plane network cooperating communication means | |
| CN108430047A (en) | A kind of distributed optimization method based on multiple agent under fixed topology | |
| CN105792258B (en) | The cross-layer optimizing method of wireless sense network medium-rate and reliability collaboration | |
| CN112804107A (en) | Layered federal learning method for energy consumption adaptive control of equipment of Internet of things | |
| CN103826219A (en) | Secrecy system power allocation method capable of satisfying requirements on time delay QoS (Quality of Service) | |
| CN106879029A (en) | A kind of information transferring method of the high safety energy efficiency based on collaboration communication | |
| CN106028398A (en) | Underwater wireless sensor network topology control method based on energy consumption balance | |
| CN105873219A (en) | GASE based TDMA wireless Mesh network resource allocation method | |
| CN105099554A (en) | Multi-user transceiving method for indoor visible light communication | |
| CN108601076B (en) | The multichannel power distribution method of sustainable transmission demand driving in collection of energy wireless network | |
| CN108462643A (en) | The weak security multicast based on network code towards integer transmission rate transmits topological construction method | |
| Xu et al. | Joint topology construction and power adjustment for UAV networks: A deep reinforcement learning based approach | |
| CN105072632B (en) | A kind of method that energy efficiency optimizes in MIMO distributed base station systems | |
| CN109257204B (en) | Network energy-saving device and method based on deep learning in software defined network | |
| CN105979589A (en) | Method and system for allocating energy efficient resources of heterogeneous network | |
| CN105722203B (en) | Extensive high energy efficiency power distribution method of the antenna system based on particle swarm algorithm | |
| CN106304305A (en) | The Poewr control method of cooperation Internet of Things energy acquisition node | |
| CN104320181B (en) | A kind of maximization network encodes the EPON energy-saving scheduling methods of NC benefits | |
| Hamidouche et al. | Bio-inspired vs classical solutions to overcome the IoT challenges | |
| Jiang et al. | Human-Cyber-Physical Ubiquitous Intelligent Communication: System Architecture, Key Technologies, and Challenges | |
| CN104901913A (en) | Transceiver design method for maximizing energy efficiency based on multi-user signal and energy simultaneous interpretation system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |