CN101998381A - Fingerprint type intelligent switching decision method based on fuzzy logic and system - Google Patents

Abstract

The invention relates to a fingerprint type intelligent switching decision method based on fuzzy logic. Switching decision is divided into two stages: the first stage is a switching decision study stage during network deployment and the second stage is a switching decision executing stage during network operation. The first stage comprises the following steps of: carrying out fuzzy treatment on accurate measurements of various switching indicators to form a fingerprint; and the set of fingerprints forms a decision reference knowledge base which is also called a fingerprint base. The second stage comprises the following steps of: carrying out fuzzy treatment on real-time input accurate measurements of various switching indicators; carrying out fuzzy logic matching computation with the fingerprints of the decision reference knowledge base; and switching the decision. The invention also discloses a system using the method. The method and the system disclosed by the invention can be used for transferring a part of treating expense after switching the decision from an operating stage to a network deployment stage, and therefore, the real-time treating expense of the operating stage is reduced and the operating speed and efficiency are enhanced.

Description

A kind of finger-print type intelligence switch determining method and system based on fuzzy logic

Technical field

The present invention relates to network communication field, more particularly, relate to a kind of finger-print type intelligence switch determining method and system based on fuzzy logic.

Background technology

In the realization of handover management, usually according to switching the position of initiating execution, handover decisions is divided into: the switching MCHO (Mobile Controlled Handoff) of portable terminal control, the switching NCHO (Network Controlled Handoff) of network control and the auxiliary switching MAHO (MobileAssisted Handoff) of portable terminal.

Existing 802.11 WLAN (wireless local area network) standards are used the MCHO mode, measure and finish the handover decisions process by local side STA (station).The MCHO mode is lower to the intelligence requirement of network side, and network configuration can be fairly simple.Meet the early stage software and hardware technology development level of wireless access wide band technology.But the problem that causes is: because network side lacks the information that STA moves, have only after showing ground informing network side STA mobile message, packet just can upgrade route, thereby causes switching delay big, and packet loss is many.On the contrary, mobile communication network all adopts the MAHO mode at present, participates in measuring-signal by client, and carries out handover decisions by network side.Like this, switch decision and execution are all finished jointly by the logic entity of network both sides, but the too big problem of real-time processing expenditure of operation phase can occur.

Therefore, need a kind of switch determining method and system, overcome the above-mentioned defective that exists in the prior art.

Summary of the invention

Technical problem to be solved by this invention is, the too big problem of real-time processing expenditure of operation phase can occur at existing MAHO switching mode, proposes a kind of finger-print type intelligence switch determining method based on fuzzy logic.

The present invention solves the employed technical scheme of its technical problem: conventional switch decision is resolved into two stages: the switch decision learning phase the when phase I is network design; Switch decision execution phase when second stage is the network operation.At the switch decision learning phase, by using local side STA (station, local side) carries out field survey at the wireless network of having disposed, training system, obtain maximum optimum access points AP (the Access Point that satisfies customer service and application demand in the actual measurement on the one hand, wireless access points), its optimum standard defines with the application demand user voluntarily according to professional, note corresponding all kinds of switching indicating device measured values on the other hand, set up an optimum switch decision like this with reference to knowledge base, i.e. " fingerprint base ", corresponding " fingerprint " that all kinds of switching indicating devices characterize of the optimum AP in a certain moment, set up switch decision with reference to knowledge base after, use for the STA of whole network; In the handover decisions execution phase, for arbitrary STA in the network, the functional entity of network side is at first measured all kinds of switching indicating devices of current time STA, i.e. " fingerprint on site "; Again with optimum switch decision reference library in the historical data of all kinds of switching indicating devices carry out matching ratio, find then and current time switches that indicating device is immediate historically to switch indicating device, thereby find out corresponding optimum AP, make switch decision.

Fundamentals of Mathematics fuzzy logic of the present invention makes full use of the characteristics of finger-print type method, utilizes user's domain knowledge and experience, progressively improves the performance of switch decision by the repetition training system.

Adapt to the diversity of various measurement data in the wireless environment, the uncertainty of polytropy and judgement and ambiguity by fuzzy logic.Because the complexity of wireless environment, no matter be the learning phase of switch decision, still the execution phase of switch decision, the mapping relations between all kinds of switching indicating devices and optimum switch decision are difficult to accurately modeling to be expressed, and therefore using the fuzzy mathematics expression formula sets up mapping relations.Same because the complexity of wireless environment, the switching indicating device measurement data in executed in real time stage can not the samely be reappeared historical record, switch the indicating device measurement data and switch the indicating device historical record data and do not have accurate matching relationship, therefore fuzzy logic is measured the measured value of real time phase and the distance between the historical record data, calculate the matching degree between them quantitatively, thereby make optimum switch decision.

The present invention has defined the class that a tlv triple characterizes the obfuscation in certain moment and has switched indicating device:

C wherein _aBe the indicating device name, its physical meaning is the classification that indicating device is switched in expression, lv _bBe the semantic variant value, its physical meaning is the good and bad degree of performance that the class paid close attention to of user is switched indicating device, Be fuzzy membership functions,

Its physical meaning is that a certain class of certain t is constantly switched indicating device for lv _bDegree of membership.

The switch decision learning phase comprises the steps:

S11) in the wireless environment of having disposed, a certain physical location at the local side place obtains the optimum access points in the actual measurement, also is optimum AP, notes the accurate measured value of corresponding all kinds of switching indicating devices;

S12) utilize the accurate measured value obfuscation of fuzzy membership functions, form in one " fingerprint " all kinds of switching indicating devices among the step S11;

S13) " fingerprint base " in the formation actual measurement.

In step S11, optimum standard defines with the application demand user voluntarily according to professional.

In step S12, " fingerprint " is defined as:

P wherein _iThe a certain physical location at expression local side STA place, C is the set of all indicating device names, LV is the set of all semantic variant values, a ∈ [1, n] its physical meaning is to have the n class to switch indicating device, its physical meaning of b ∈ [1, k] is the good and bad degree that k switching indicating device arranged.

In step S13, judgement also is " fingerprint base " with reference to knowledge base, is the set of all " fingerprints ": SRD (P)={ SR (p ₁), SR (p ₂) ... SR (p _i) ... | p _i∈ P}, wherein p _iThe a certain physical location at expression local side STA place, P represents the set of local side STA physical location.

The switch decision execution phase comprises the steps:

S21) utilize the accurate measured value obfuscation of the switching indicating device of all categories that fuzzy membership functions will import in real time, form one " fingerprint on site ";

S22) utilize fuzzy logic coupling " fingerprint base ";

S23) switch decision.

" fingerprint on site " among the step S21 is:

Wherein C is the set of all indicating device names, and LV is the set of all semantic variant values, and its physical meaning of a ∈ [1, n] is to have the n class to switch indicating device, and its physical meaning of b ∈ [1, k] is the good and bad degree that k switching indicating device arranged.

In step S22, with each the bar SR (p in " fingerprint on site " SI (t) and " fingerprint base " _i) compare, calculating matching degree, the matching degree formula is:

W wherein _i, l _iBe used for regulating the weight between the different classes of switching indicating device, w _iBe linear weight, l _iBe index weight, current optimum access points is has peaked FitnessDegree (p _i) at learning phase p _iPairing access points.

Order STA switches to this access points that step S22 obtains among the step S23, and so far switch decision is finished.

A kind of finger-print type intelligence switch decision system based on fuzzy logic is provided, comprises:

Fuzzy membership functions unit: be used to store the user-defined ambiguity function that all kinds of switching indicating devices are carried out obfuscation;

Fuzzier unit: be used for accurate measured value obfuscation, form in one " fingerprint ", and this " fingerprint " sent into " fingerprint " database or fuzzy engine unit to all kinds of switching indicating devices of input.

" fingerprint " database: being used for bank switching judgement learning phase all " fingerprints ", is the set of " fingerprint ";

Fuzzy engine unit: every the fingerprint that is used for current " fingerprint " that fuzzier unit is sent into and " fingerprint " database compares, and calculates matching degree, obtains optimum access points, and result of calculation is sent into the handover decisions unit;

The handover decisions unit: according to the current optimum access points that fuzzy engine unit is sent into, the order local side switches to this access points.

Utilize method and system disclosed by the invention, can transfer to the network design stage to a part of processing expenditure of switch decision from the operation phase, thereby reduce the real-time processing expenditure of operation phase, improved the speed of service and efficient.

Description of drawings

Fig. 1 is a kind of finger-print type intelligence switch determining method flow chart based on fuzzy logic that a preferred embodiment of the present invention provides;

Fig. 2 is the switch decision learning phase flow chart that a preferred embodiment of the present invention provides;

Fig. 3 is the switch decision execution phase flow chart that a preferred embodiment of the present invention provides;

Fig. 4 is the structural representation of a kind of finger-print type intelligence switch decision system based on fuzzy logic of providing of a preferred embodiment of the present invention.

Embodiment

In order to make purpose of the present invention, technical scheme and advantage clearer,, the present invention is further elaborated below in conjunction with drawings and Examples.Should be appreciated that specific embodiment described herein only in order to explanation the present invention, and be not used in qualification the present invention.

The embodiment of the invention be in office building corridor, campus deploy one 802.11 WLAN (wireless local area network), comprise 2 radio access point AP1 and AP2,1 mobile client STA.

Fig. 1 is a kind of finger-print type intelligence switch determining method flow chart based on fuzzy logic that the embodiment of the invention provides.As shown in Figure 1, comprise two stages: switch decision learning phase and switch decision execution phase.

Fig. 2 is the switch decision learning phase flow chart that the embodiment of the invention provides, and as shown in Figure 2, comprising:

S11) in the wireless environment of having disposed, a certain physical location at the local side place obtains the optimum access points in the actual measurement, notes the accurate measured value of corresponding all kinds of switching indicating devices;

S12) utilize the accurate measured value obfuscation of fuzzy membership functions, form in one " fingerprint " all kinds of switching indicating devices among the step S11;

S13) " fingerprint base " in the formation actual measurement.

In the present embodiment, the corresponding following content of step S11:

At first need to define the criterion of selecting optimum AP in this WLAN (wireless local area network), for example can define throughput Throughput and be optimum to the maximum, max{T} promptly satisfies condition;

Wireless local area network AP 1 is become several positions, p with the common area dividing that effectively covers of AP2 ₁, p ₂..., p _i...;

In each position with STA manual intervention ground and AP ₁Or AP ₂Connect, survey end-to-end Throughput.For example at position P ₁, near the AP in the space scanning ₁, AP ₂, measure AP1 and AP2 and divide other throughput, if when satisfying condition max{T}, STA just in time with AP ₁Connect, then this moment AP ₁Be optimum AP;

Measure AP this moment ₁, AP ₂Corresponding signal strength signal intensity RSSI and signal interference ratio SIR.AP for example ₁_ RSSI=-60dbm, AP ₁_ SIR=75db, AP ₂_ RSSI=-70dbm, AP ₂_ SIR=70db.

In step S12, " fingerprint " is defined as:

P wherein _iThe a certain physical location at expression local side STA place, C is the set of all indicating device names, LV is the set of all semantic variant values, a ∈ [1, n] its physical meaning is to have the n class to switch indicating device, its physical meaning of b ∈ [1, k] is the good and bad degree that k switching indicating device arranged.

In the present embodiment, the corresponding following content of step S12:

Use fuzzification function, with the measured value obfuscation:

$({\mathrm{AP}}_1\_\mathrm{RSSI},\mathrm{high},{\mathrm{\μ}}_{{\mathrm{AP}}_1\_\mathrm{RSSI},\mathrm{high}}(-60)),$ $({\mathrm{AP}}_1\_\mathrm{SIR},\mathrm{low},{\mathrm{\μ}}_{{\mathrm{AP}}_1\_\mathrm{SIR},\mathrm{low}}\left(75\right)),$

$({\mathrm{AP}}_2\_\mathrm{RSSI},\mathrm{high},{\mathrm{\μ}}_{{\mathrm{AP}}_2\_\mathrm{RSSI},\mathrm{high}}(-70)),$ $({\mathrm{AP}}_2\_\mathrm{SIR},\mathrm{low},{\mathrm{\μ}}_{{\mathrm{AP}}_2\_\mathrm{SIR},\mathrm{low}}\left(70\right))$

Obtain four tlv triple:

(AP ₁_ RSSI, high, 0.8), its implication is AP ₁Switching indicating device RSSI belong to " height " and degree be 0.8;

(AP ₁_ SIR, low, 0.6), its implication is AP ₁Switching indicating device SIR belong to " low " and degree be 0.6;

(AP ₂_ RSSI, high, 0.7), its implication is AP ₂Switching indicating device RSSI belong to " height " and degree be 0.7;

(AP ₂_ SIR, low, 0.5), its implication is AP ₂Switching indicating device SIR belong to " low " and degree be 0.5;

Construct a fingerprint: f:SR (p ₁)-AP ₁, its implication is at position p ₁, select AP ₁As the pairing one group of obfuscation radio environment measurements value of optimal network, deposit " fingerprint base " in.A Here it is fingerprint: { (AP ₁_ RSSI, high, 0.8), (AP ₁_ SIR, low, 0.6), (AP ₂_ RSSI, high, 0.7), (AP ₂_ SIR, low, 0.5)-AP ₁

In step S13, judgement also is " fingerprint base " with reference to knowledge base, is the set of all " fingerprints ": SRD (P)={ SR (p ₁), SR (p ₂) ... SR (p _i) ... p _i∈ P}, wherein p _iThe a certain physical location at expression STA place, P represents the set of STA place physical location.

In the present embodiment, the corresponding following content of step S13:

Mobile STA is to another position p _i, repeat above step, construct next bar knowledge.

When STA has traveled through all position p _iAfter, then generate with reference to knowledge base, also be " fingerprint base ": SRD (P)={ SR (p ₁), SR (p ₂) ... SR (p _i) ... | p _i∈ P}, the example of knowledge base is placed on network side.

So far, the switch decision learning phase finishes.

Fig. 3 is the switch decision execution phase flow chart that the embodiment of the invention provides, and as shown in Figure 3, comprising:

S21) the accurate measured value obfuscation that utilizes all categories that fuzzy membership functions will import in real time to switch indicating device forms one " fingerprint on site ";

S22) utilize fuzzy logic coupling " fingerprint base ";

S23) switch decision.

" fingerprint on site " among the step S21 is:

Wherein C is the set of all indicating device names, and LV is the set of all semantic variant values, and its physical meaning of a ∈ [1, n] is to have the n class to switch indicating device, and its physical meaning of b ∈ [1, k] is the good and bad degree that k switching indicating device arranged.

In the present embodiment, the corresponding following content of step S21:

WLAN (wireless local area network) enters the operation phase, and the user uses STA to move freely in network.AP ₁, AP ₂Regularly detect STA continuously, as STA a certain position in network, AP ₁, AP ₂Measure the wireless signal feature of STA, i.e. AP ₁_ RSSI, AP ₁_ SIR, AP ₂_ RSSI, AP ₂_ SIR.

Utilize and the same fuzzy membership functions obfuscation in-site measurement value of learning phase, obtain " fingerprint on site " SI (t _Current), for example:

SI(t _current)＝{(AP ₁_RSSI，high，0.5)，(AP ₁_SIR，low，0.5)，(AP ₂_RSSI，high，0.7)，(AP ₂_SIR，low，0.4)}

In step S22, with each the bar SR (p in " fingerprint on site " SI (t) and " fingerprint base " _i) compare, calculating matching degree, the matching degree formula is:

W wherein _i, l _iBe used for regulating the weight between the different classes of switching indicating device, w _iBe linear weight, l _iBe index weight, current optimum AP is has peaked FitnessDegree (p _i) at learning phase p _iPairing access points, whether decision triggers switching according to certain condition in system then, switches to optimum access points.

In the present embodiment, the corresponding following content of step S22:

Network side AP ₁, AP ₂Obtain " fingerprint on site " SI (t by cooperation _Current), with each the bar SR (p in " fingerprint base " _i) compare, calculate matching degree:

W wherein _i, l _iBe used for regulating the weight between the different classes of switching indicating device, w _iBe linear weight, l _iBe index weight.

For example, SI (t _Current) and SR (p ₁) matching degree:

Suppose to get after certain the w value and 1 value FitnessDegree (p ₁)=0.044

Similarly, FitnessDegree (p is arranged ₂), FitnessDegree (p ₃) ... FitnessDegree (p _i), find out maximum wherein:

Max{FitnessDegree(p ₁)，FitnessDegree(p ₂)......FitnessDegree(p _i)......}

Suppose it is FitnessDegree (p ₁)=0.044 maximum, then position p ₁Corresponding AP ₁Be the access points AP of current optimum.

In the present embodiment, the corresponding following content of step S23:

Order local side STA switches to this access points, and so far switch decision is finished.

Fig. 4 is the structural representation of a kind of finger-print type intelligence switch decision system based on fuzzy logic of providing of the embodiment of the invention.As shown in Figure 4, comprise fuzzy membership functions unit 41, fuzzier unit 42, " fingerprint " database 43, fuzzy engine unit 44 and switch decision unit 45.

Fuzzy membership functions unit 41 is used to store the user-defined ambiguity function that all kinds of switching indicating devices are carried out obfuscation; Fuzzier unit 42 is according to the accurate measured value obfuscation to all kinds of switching indicating devices of the fuzzy membership functions of fuzzy membership functions unit 41 storage, form in one " fingerprint ", if learning phase, then this " fingerprint " sent into " fingerprint " database 43, be represented by dotted lines learning phase among the figure, if the execution phase is then sent into this " fingerprint " fuzzy engine unit 44, represent the execution phase with solid line among the figure; " " database 43 is used for bank switching judgement learning phase all " fingerprints " to fingerprint, is the set of " fingerprint "; Every fingerprint that fuzzy engine unit 44 is used for current " fingerprint " that fuzzier unit 42 is sent into and " fingerprint " database 43 compares, and calculates matching degree, obtains optimum access points, and result of calculation is sent into handover decisions unit 45; The current optimum access points that handover decisions unit 45 is sent into according to fuzzy engine unit, the order local side switches to this access points.

The above only is preferred embodiment of the present invention, not in order to restriction the present invention, all any modifications of being done within the spirit and principles in the present invention, is equal to and replaces and improvement etc., all should be included within protection scope of the present invention.

Claims (10)

1. the finger-print type intelligence switch determining method based on fuzzy logic is characterized in that, comprises the steps:

S1) during network design, carry out switch decision study;

S2) during the network operation, carry out switch decision and carry out.

2. method according to claim 1 is characterized in that, also comprises: fuzzy membership functions μ of consumer premise justice is to the accurate measured value obfuscation of all kinds of switching indicating devices.

3. as method as described in the claim 2, it is characterized in that, also comprise: define a tlv triple: A class that is used for characterizing the obfuscation in certain moment is switched indicating device, wherein, and c _aBe the indicating device name, its physical meaning is the classification that indicating device is switched in expression, lv _bBe semantic variant, its physical meaning is the good and bad degree of performance that the class paid close attention to of user is switched indicating device,

Be the fuzzy membership functions of consumer premise justice,

Its physical meaning is that a certain class of certain t is constantly switched indicating device for lv _bDegree of membership.

4. as method as described in the claim 3, it is characterized in that described step S1 comprises the steps:

S11) in the wireless environment of having disposed, a certain physical location at the local side place, according to application demand and measured performance effect, the optimum access points of predefine obtains and writes down the accurate measured value of corresponding all kinds of switching indicating devices again;

S12) utilize the accurate measured value obfuscation of fuzzy membership functions, form in one " fingerprint " all kinds of switching indicating devices among the step S11;

S13) " fingerprint base " in the formation actual measurement.

5. as method as described in the claim 4, it is characterized in that " fingerprint " described in the step S12 is:

P wherein _iThe a certain physical location at expression local side place, C is the set of all indicating device names, and LV is the set of all semantic variant values, and its physical meaning of a ∈ [1, n] is to have the n class to switch indicating device, and its physical meaning of b ∈ [1, k] is the good and bad degree that k switching indicating device arranged.

6. as method as described in the claim 5, it is characterized in that " fingerprint base " described in the step S13 is the set of all " fingerprints ": SRD (P)={ SR (p ₁), SR (p ₂) ... SR (p _i) ... | p _i∈ P}, wherein p _iThe a certain physical location of expression local side, P represents the set of local side physical location.

7. as method as described in the claim 6, it is characterized in that described step S2 comprises the steps:

S21) utilize the accurate measured value obfuscation of all kinds of switching indicating devices that fuzzy membership functions will import in real time, form one " fingerprint on site ";

S22) utilize fuzzy logic coupling " fingerprint base ";

S23) switch decision.

8. as method as described in the claim 7, it is characterized in that " fingerprint on site " is defined as described in the step S21:

Wherein C is the set of all indicating device names, and LV is the set of all semantic variant values, and its physical meaning of a ∈ [1, n] is to have the n class to switch indicating device, and its physical meaning of b ∈ [1, k] is the good and bad degree that k switching indicating device arranged.

9. as method as described in the claim 8, it is characterized in that step S22 further comprises: with each the bar SR (p in " fingerprint on site " SI (t) and " fingerprint base " _i) compare, calculating matching degree, the matching degree formula is:

W wherein _i, l _iBe used for regulating the weight between the different classes of switching indicating device, w _iBe linear weight, l _iBe index weight, current optimum access points is has peaked FitnessDegree (p _i) at learning phase p _iPairing access points.

10. the finger-print type intelligence switch decision system based on fuzzy logic is characterized in that, comprising:

Fuzzy membership functions unit: be used to store the user-defined ambiguity function that all kinds of switching indicating devices are carried out obfuscation;

Fuzzier unit: be used for accurate measured value obfuscation, form in one " fingerprint ", and this " fingerprint " sent into " fingerprint " database or fuzzy engine unit to all kinds of switching indicating devices of input;

" fingerprint " database: being used for bank switching judgement learning phase all " fingerprints ", is the set of " fingerprint ";

Fuzzy engine unit: every the fingerprint that is used for current " fingerprint " that fuzzier unit is sent into and " fingerprint " database compares, and calculates matching degree, obtains optimum access points, and result of calculation is sent into the handover decisions unit;

The handover decisions unit: according to the current optimum access points that fuzzy engine unit is sent into, the order local side switches to this access points.

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