CN1301037C - Distributed intelligence call acceptance control method and device - Google Patents
Distributed intelligence call acceptance control method and device Download PDFInfo
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- CN1301037C CN1301037C CNB2005100863990A CN200510086399A CN1301037C CN 1301037 C CN1301037 C CN 1301037C CN B2005100863990 A CNB2005100863990 A CN B2005100863990A CN 200510086399 A CN200510086399 A CN 200510086399A CN 1301037 C CN1301037 C CN 1301037C
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Abstract
The present invention relates to an intelligence call acceptance control method and a device based on a distributed idea, which comprises four modules: a fuzzy equivalent interference estimator, a neural network interference predictor, a feedback module of actual system performance, and a fuzzy call acceptance processor. The proposal adopts fuzzy logic for evaluating equivalent interference according to the service characteristic of a new user; a neural network technique is used for predicting a total interference level of next time of a user connected with the system. In addition, disconnection probability of a speech and a data services of a subzone and an adjacent subzone at the current time is actually measured as a system feedback in a network. The factors are synthesized and a fuzzy call acceptance controller is used for accepting a judgment. The present invention introduces a distributed idea into the intelligence call acceptance control proposal, provides the conception of the equivalent disconnection probability of the adjacent subzone, synthesizes and judges the influence of the QoS of a user of the adjacent subzone on the call acceptance control of the subzone, and ensures that acceptance to a new call can not reduce the service quality of the subzone and the adjacent subzone.
Description
Technical field
The present invention relates to call connection control method (CAC), particularly a kind of distributed intelligence call acceptance control method based on distributed thought (DICAC:Distributed Intelligent Call AdmissionControl) and device belong to the telecommunication system resources management domain.
Background technology
Call connection control method is in developing stage.Current, the research of Call Admission Control scheme mainly contains three major types in the cdma cellular communication system: a class is based on the CAC algorithm of SIR or total interfering signal power; The another kind of CAC algorithm that is based on the power system capacity analytical model; A class is based on the CAC algorithm of power controlling models again.But these algorithm researches or be confined to single operation system, or do not consider many sub-districts service quality (QoS) requirement simultaneously.
Chang Chung-Ju introduces CAC at the problems referred to above with fuzzy control theory and neural network technology in its article " the intelligence call acceptance control under different Q os requires in the Wideband CDMA Systems " (Intelligent Call Admission Control for Differentiated QoSProvisioning in Wideband CDMA Cellular Systems) literary composition, propose intelligent Call Admission Control (ICAC) scheme.This scheme is utilized fuzzy decision technology and neural network identification ability respectively, estimate that new user asks the equivalence interference that produces, connected next average interference constantly of user in the prognoses system, disturb according to two that estimate then and this sub-district of current time of system feedback and neighbor cell in the outage probability of all kinds of business, whether decision admits current call request.This ICAC algorithm can be applicable to the multi-service cdma system, can guarantee that all kinds of professional outage probabilities meet the demands.But this algorithm has the following disadvantages: only consider current area user's qos requirement during judgement, ignored the influence of the congestion condition of neighbor cell to this sub-district acceptance judging.In fact, because cellular cell adopts Microcell or picocell structure, switching phenomenon frequently takes place, if handover takes place behind the user access network, can the incision sub-district provide available resources to the user, and whether user's incision can cause this sub-district service quality to descend, and also is the major issue that consider.
Summary of the invention:
At the shortcoming that exists in the said method, the present invention considers many sub-districts multi-service qos requirement, distributed thought is introduced intelligent Call Admission Control, propose weighted sum equivalence neighbor cell drop rate notion, propose distributed intelligence call acceptance control (DICAC) method and device on this basis.
The technical solution adopted for the present invention to solve the technical problems is:
A kind of distributed intelligence call acceptance control device comprises four modules: fuzzy equivalent interference estimator module connects fuzzy call acceptance processor, and neural net interference prediction device is connected fuzzy call acceptance processor respectively with the actual system behavior feedback module.
Fuzzy equivalent interference estimator module: estimate the interference that new call request user produces;
Neural net interference prediction device: predict to have connected next interference constantly of user in the current system;
Actual system behavior feedback module: measure this sub-district of current time and neighbor cell speech and data service outage probability, calculate neighbor cell speech and data service equivalence outage probability according to neighbor cell speech of measuring and data service outage probability, with this sub-district outage probability and neighbor cell equivalence outage probability as system feedback;
Fuzzy call acceptance processor: disturb and this sub-district outage probability of current time and neighbor cell equivalence outage probability feedback information the acceptance judging that carries out new call request according to the one-step prediction that has connected the user in the equivalence interference of estimating new call request generation, the system.
A kind of intelligence call acceptance control method based on distributed thought is taken all factors into consideration many sub-districts qos requirement, utilizes neural network technology and fuzzy control theory to carry out the call acceptance judgement.Distributed thought is embodied in schemes synthesis and weighs neighbor cell user QoS admits control to this cell call influence, assurance can not reduce the service quality of this sub-district and neighbor cell to the admittance of new calling, propose the notion of neighbor cell equivalence outage probability, calculate by each sub-district outage probability weighted sum.
The scheme of taking is: adopt fuzzy decision and nerual network technique to implement Call Admission Control, promptly use BP type nerual network technique to carry out having connected in the system one-step prediction that the user produces interference, the utilization fuzzy logic is estimated the interference that new call request user produces, then, the one-step prediction that has connected the user in the equivalence interference that produces according to new call request, the system disturbs, this sub-district speech of current time and data service outage probability and neighbor cell speech and data service equivalence outage probability, utilizes the fuzzy decision technology to carry out the acceptance judging of new call request.
Fuzzy equivalent interference estimator adopts the fuzzy decision technology, according to the service parameter (peak rate, average speed, peak rate duration and outage probability require) of new call request, estimates the interference of its generation, is a fuzzy logic implementation.
DICAC utilizes the situation of change of the forecast function study cellular cell network traffic of neural net, according to the existing disturbed condition that connects in the current time system, has connected the total interference level of user when predicting next in the etching system.
The equivalence that the new call request of exporting according to fuzzy equivalent interference estimator produces is disturbed, the one-step prediction that has connected the user in the system of neural net interference prediction device output disturbs, this sub-district speech of current time and data service outage probability and neighbor cell speech and data service equivalence outage probability, fuzzy call acceptance processor utilizes the fuzzy decision technology, carries out the acceptance judging of new call request.Acceptance judging value that obtains after the processor defuzzification and admittance thresholding are made comparisons, if greater than threshold requirement, just admit new call request, otherwise refusal.
The present invention has following advantage:
1, the present invention considers distributed thought, guarantees that simultaneously this sub-district and neighbor cell user's QoS meets the demands.Acceptance judging for new arrival is called out should guarantee that current area can provide available resources, can be in the time of will guaranteeing also that the user switches to neighbor cell in communication process owing to the not congested of this switching cell yet dropped calls, thereby reduced the switching outage probability.
2, the present invention proposes the notion of neighbor cell equivalence outage probability, and each neighbor cell congestion condition embodies by the probability that this user switches to each sub-district for the influence of call acceptance judgement.The probability that the user switches to a certain neighbor cell is big more, and this sub-district outage probability proportion in equivalent drop rate is big more, and promptly the outage probability of this sub-district is big more to the influence of court verdict; Otherwise, influence less.
Description of drawings
Below in conjunction with accompanying drawing the present invention is described in further detail.
Fig. 1 is a distributed intelligence call acceptance controlling schemes system construction drawing of the present invention.
Fig. 2 is the fuzzy equivalent interference estimator schematic diagram of distributed intelligence call acceptance controlling schemes of the present invention.
Fig. 3 is the neural net interference prediction device structure chart of distributed intelligence call acceptance controlling schemes of the present invention.
Fig. 4 is the flowchart of distributed intelligence call acceptance controlling schemes of the present invention.
Fig. 5 is that distributed intelligence call acceptance control device performance evaluation index of the present invention is with new call request arrival rate change curve.
Embodiment
DICAC realizes its function as a kind of resource management control method in each cellular base stations control system, because scheme adopts distributed thought, exchange periodically jam state information between each cell base station controller, to realize the resource management of network level.System model and each functional module be as shown in Figure 1:
Fuzzy equivalent interference estimator: adopt fuzzy logic to estimate its equivalence interference according to new service characteristics.
Neural net interference prediction device: utilize the nerual network technique prognoses system to connect next constantly total interference level of user.
Actual system behavior feedback module: measure this sub-district of current time and neighbor cell speech and data service outage probability, calculate neighbor cell speech and data service equivalence outage probability according to neighbor cell speech of measuring and data service outage probability, with this sub-district outage probability and neighbor cell equivalence outage probability as system feedback.
Fuzzy call acceptance processor: disturb and this sub-district outage probability of current time and neighbor cell equivalence outage probability feedback information the acceptance judging that carries out new call request according to the one-step prediction that has connected the user in the equivalence interference of estimating new call request generation, the system.
When new calling arrives, place cell base station control system is extracted new customer service parameter (peak rate, average speed, peak rate duration and outage probability require), as the input of fuzzy equivalent interference estimator, the equivalent interference level of newly being called out according to fuzzy rule.Fuzzy equivalent interference estimator is converted to the corresponding language variable to the service parameter of new call request according to the member function in the fuzzy logic, input as fuzzy inference system, as shown in Figure 2, the user is equivalent to disturb to adopt min and the fuzzy compose operation of max to make new advances according to the fuzzy inference rule of formulating, utilize centre of area method ambiguity solution, equivalent interference value is estimated in output.
Scheme adopts has the BP type neural net of input time delay, realize the Nonlinear Mapping between input, output, and predicted interference output is not only relevant with the interference level of current time, also relevant with past interference constantly.The new calling when arriving, systematic sampling current time system interference level with the input layer of this interference value and time-delay component input neural network thereof, through hidden layer, is exported next predicted interference constantly by output layer.Neural network structure as shown in Figure 3.
Neural net interference prediction device needs before practical application through the data training.According to the real network empirical data, obtain the horizontal inputoutput data group of system interference, as the neural metwork training data.Training data passes to the hidden layer unit by input layer, is sent to output layer unit through after the hidden layer cell processing, and by the output layer unit dateout, this is a state forward direction training process successively.If the output response has error with the desired output pattern, just change error back propagation over to, error amount is successively transmitted and revises each layer connection weights along connecting path.For given training data, repeat propagated forward and error back propagation process, when training output met the demands, the network training process finished.
The distributed thought of scheme is embodied in comprehensive measurement neighbor cell user QoS this cell call is admitted the influence of controlling, and guarantees can not reduce the admittance of new calling the service quality of this sub-district and neighbor cell.When carrying out the call acceptance judgement, this sub-district outage probability is as the feedback information of systematic function in the real network, for realizing taking all factors into consideration the neighbor cell qos requirement, the notion of neighbor cell equivalence outage probability is proposed, adopt neighbor cell outage probability weighted sum to calculate:
Wherein, P
iBe the outage probability of each neighbor cell i, a
iRepresent its weight, the user that equals to make a call switches to the probability of each neighbor cell, can obtain by estimating.The estimation of switching probability is relevant with following factor: the user receives the size of each cell base station pilot signal, and the user that makes a call is apart from the distance of each cell base station, user's translational speed and direction, call duration etc.The probability that the user switches to a certain neighbor cell is big more, and this sub-district outage probability proportion in equivalent drop rate is big more, and promptly the outage probability of this sub-district is big more to the influence of court verdict; Otherwise, influence less.As can be seen, equivalent outage probability is the concentrated expression of the new user neighbor cell congestion condition that may switch.Each neighbor cell outage probability is subjected to the influence that new user switches to this sub-district probability to the influence degree of call acceptance judgement.
The equivalence that the new call request of exporting according to fuzzy equivalent interference estimator produces is disturbed, the one-step prediction that has connected the user in the system of neural net interference prediction device output disturbs and this sub-district speech of current time and data user's outage probability and neighbor cell speech and data user's equivalence outage probability, fuzzy call acceptance processor utilizes the fuzzy decision technology, carries out the acceptance judging of new call request.Acceptance judging value that obtains after the processor defuzzification and admittance thresholding are made comparisons, if satisfy threshold requirement, just admit new call request, otherwise refusal.
Said method is carried out flow process as shown in Figure 4, and step is as follows:
Step 1: the arrival of Waiting for Call request,
Step 2:1) estimate the interference that call request produces,
2) prediction current system in the user next disturb constantly,
3) measure this sub-district of current time and neighbor cell outage probability, calculate neighbor cell equivalence outage probability;
Step 3: ask acceptance judging value Z,
Step 4: compare decision value Z and threshold value Z
TH, if greater than threshold value, step 1 is refused and returned to step 5 below carrying out if do not satisfy,,
Step 5: admittance also distributes respective channel from available channel, number of users adds one in the system, returns step 1.
The performance evaluation index of distributed intelligence call acceptance control method and device with new call request arrival rate change curve referring to Fig. 5.
Claims (6)
1, a kind of distributed intelligence call acceptance control device, it is characterized in that: comprise four modules, fuzzy equivalent interference estimator module connects fuzzy call acceptance processor, and neural net interference prediction device is connected fuzzy call acceptance processor respectively with the actual system behavior feedback module;
Fuzzy equivalent interference estimator module: estimate the interference that new call request user produces;
Neural net interference prediction device: predict to have connected next interference constantly of user in the current system;
Actual system behavior feedback module: measure this sub-district of current time and neighbor cell speech and data service outage probability, calculate neighbor cell speech and data service equivalence outage probability according to neighbor cell speech of measuring and data service outage probability, with this sub-district outage probability and neighbor cell equivalence outage probability as system feedback;
Fuzzy call acceptance processor: disturb and this sub-district outage probability of current time and neighbor cell equivalence outage probability feedback information the acceptance judging that carries out new call request according to the one-step prediction that has connected the user in the equivalence interference of estimating new call request generation, the system.
2, distributed intelligence call acceptance control device according to claim 1 is characterized in that: acceptance judging value that fuzzy call acceptance processor module obtains and admittance thresholding are made comparisons, if greater than threshold value, just admit new call request, otherwise refusal.
3, distributed intelligence call acceptance control device according to claim 1 is characterized in that: fuzzy equivalent interference estimator adopts fuzzy logic to estimate its equivalent interference according to new service characteristics.
4, distributed intelligence call acceptance control device according to claim 1 is characterized in that: neural net interference prediction device utilizes the nerual network technique prognoses system to connect next constantly total interference level of user.
5, a kind of distributed intelligence call acceptance control method, it is characterized in that: adopt fuzzy decision and nerual network technique to implement Call Admission Control, promptly use BP type nerual network technique to carry out having connected in the system one-step prediction that the user produces interference, the utilization fuzzy logic is estimated the interference that new call request user produces, then, disturb according to the equivalence that new call request produces, the one-step prediction that has connected the user in the system disturbs, this sub-district speech of current time and data service outage probability and neighbor cell speech and data service equivalence outage probability, calculate neighbor cell speech and data service equivalence outage probability according to neighbor cell speech of measuring and data service outage probability, utilize the fuzzy decision technology to carry out the acceptance judging of new call request.
6, distributed intelligence call acceptance control method according to claim 5, it is characterized in that: distributed thought is introduced intelligence call acceptance control, propose the notion of neighbor cell equivalence outage probability, and adopt neighbor cell outage probability weighted sum to calculate:
Wherein, P
iBe the outage probability of each neighbor cell i, a
iRepresent its weight, the user that equals to make a call switches to the probability of each neighbor cell.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN1474617A (en) * | 2002-08-07 | 2004-02-11 | 英业达股份有限公司 | Call Cut-in control method |
| US20040116127A1 (en) * | 2002-10-07 | 2004-06-17 | Interdigital Technology Corporation | System and method for simulation of performance of measurement-based algorithms for slotted wireless communications |
| EP1521489A1 (en) * | 2003-09-30 | 2005-04-06 | Siemens Mobile Communications S.p.A. | Interference based call admission control for GSM mobile networks |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN1474617A (en) * | 2002-08-07 | 2004-02-11 | 英业达股份有限公司 | Call Cut-in control method |
| US20040116127A1 (en) * | 2002-10-07 | 2004-06-17 | Interdigital Technology Corporation | System and method for simulation of performance of measurement-based algorithms for slotted wireless communications |
| EP1521489A1 (en) * | 2003-09-30 | 2005-04-06 | Siemens Mobile Communications S.p.A. | Interference based call admission control for GSM mobile networks |
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