CN100384299C - A resource reservation intelligent call admission control method - Google Patents

Abstract

An intelligent call admission control method and device based on resource reservation, which uses the number of current system users to replace the measurement interruption probability in the ICAC scheme to implement admission judgment. It includes three modules: fuzzy equivalent interference estimator, neural network interference predictor and fuzzy call admission processor. The fuzzy equivalent interference estimator module is used to estimate the interference of new call requesting users; the neural network interference predictor is used for one-step prediction of the interference of connected users in the current system; the fuzzy call admission processor is used for the admission decision of new call requesting users. The present invention adopts a resource reservation strategy to reduce the interruption probability of various services, and uses the comparison between the current number of users in the system and the difference between the maximum number of users that the system can accommodate and the number of reserved users as one of the basis for admission judgment, which is simple and easy to calculate. The volume is small; under heavy load conditions, the probability of new call request user blocking is low.

Description

A resource reservation intelligent call admission control method

Technical field

The present invention relates to a call admission control method, in particular to a Resource Reserved Intelligent Call Admission Control (RRICAC: Resource Reserved Intelligent Call Admission Control) method and device based on resource reservation, belonging to the field of communication system resource management.

Background technique

Call admission control methods are under development. At present, there are three main categories of research on call admission control schemes in CDMA cellular communication systems: one is the CAC algorithm based on SIR or total interference signal power; the other is the CAC algorithm based on the system capacity analysis model; the other is the CAC algorithm based on the system capacity analysis model. CAC algorithm based on power control model. But these algorithm studies are either limited to a single service system, or cannot meet multiple QoS requirements of the system at the same time.

Chang Chung-Ju in his article "Intelligent Call Admission Control for Differentiated QoS Provisioning in Wideband CDMA Cellular Systems" (Intelligent Call Admission Control for Differentiated QoSProvisioning in Wideband CDMA Cellular Systems) proposed a method based on neural network identification and fuzzy The intelligent call admission control (ICAC) scheme of decision-making technology uses fuzzy decision-making and neural network identification capabilities to estimate the equivalent interference generated by new user requests and the average interference of connected users in the system, and then based on the estimated two interferences and the system The current measurement interruption probability of various services fed back determines whether to accept the call request. The ICAC algorithm can be applied to multi-service CDMA systems, and can always ensure that the probability of interruption of various services meets the requirements. However, the algorithm has the following two disadvantages: 1) the implementation is complex, and the algorithm execution process needs to continuously measure and calculate the current system outage probability, and the related calculation is large; 2) the strict guarantee of the system outage probability makes the new call request user's The probability of blocking is too high.

Invention content:

In view of the shortcomings in the above existing methods, the problem to be solved by the present invention is to provide an intelligent call admission control method and device based on resource reservation, which simplifies the implementation complexity of the algorithm and reduces the number of new calls within the tolerable range of the system interruption probability. The blocking probability of the requesting user.

The technical solution adopted by the present invention to solve the technical problem is: an intelligent call admission control device based on resource reservation, which uses the current number of system users to replace the measurement interruption probability in the ICAC solution to implement admission judgment. It includes three modules: fuzzy equivalent interference estimator, neural network interference predictor and fuzzy call admission processor. The fuzzy equivalent interference estimator module is used to estimate the interference of new call requesting users; the neural network interference predictor is used for one-step prediction of the interference of connected users in the current system; the fuzzy call admission processor is used for the admission decision of new call requesting users. An intelligent call admission control method for resource reservation, using fuzzy decision-making and neural network identification technology to implement a call admission control algorithm, using the uncertainty of fuzzy logic and expert information to perform interference and admission judgments generated by new call request users; using neural networks The non-linear and predictive nature of the identification technique allows a one-step prediction of interference from connected users in the system.

An intelligent call admission control method for resource reservation, using fuzzy decision-making and neural network identification technology to implement a call admission control algorithm, the steps are as follows:

Step 1: The fuzzy equivalent interference estimator waits for the arrival of the call request; Step 2: According to the service parameters of the new call request, use the fuzzy equivalent interference estimator to estimate the interference generated by the call request; Step 3: According to the current time in the system For the average interference of users, use the neural network interference predictor to predict the interference of connected users in the system at the next moment; Step 4: The system user counter measures the number of users in the current system; Step 5: Interference generated according to call requests, interference predicted in the system and the current number of users in the system, use the fuzzy call admission processor to find the admission judgment value Z; Step 6: If the admission judgment value Z is compared with the admission threshold Z ^TH , if Z>Z ^TH , it means that the current number of users in the system is not greater than The difference between the maximum number of users that the system can accommodate and the number of reserved users, execute step 7, otherwise, refuse to admit new users, return to step 1; step 7: the channel allocator accepts new users, allocates corresponding channels from the available channels, and the system Add one to the number of users, return to step 1; use the strategy of resource reservation to reduce the probability of system interruption, and record the system performance under different numbers of reserved users under a certain arrival rate of users, and select the reservation with the best performance value as its final value.

The fuzzy equivalent interference estimator adopts fuzzy decision-making technology to estimate the interference I _new generated by it according to the service parameters of the new call request (peak rate R _p , average rate R _m , peak rate duration T _p and outage probability requirement P _otg ) .

The neural network interference predictor designs a serial feedback neural network based on system identification and neural network principles, and takes the average interference I′ _k (n) of users in the system at the current time n as the input variable of the serial feedback neural network to Accurately predict the interference I _k ^(n+1) of connected users in the system at the next moment (n+1).

The fuzzy call admission processor uses fuzzy decision-making technology, according to the equivalent interference generated by the new call request output by the fuzzy equivalent interference estimator, the one-step predicted interference of connected users in the system output by the neural network interference predictor, and the number of users currently accommodated in the system Num, the admission decision for the new call request. In order to reduce the probability of interruption, a part of the number of users is reserved. Only when the current number of users of the system is not greater than the difference between the maximum number of users that the system can accommodate and the number of reserved users, can new call request users be admitted. Set the value of the number of reserved users according to the experimental method: under a certain arrival rate of users, record the system performance under different numbers of reserved users, and select the reserved value with the best performance as its final value.

The present invention has the following advantages:

1. The present invention adopts a resource reservation strategy to reduce the interruption probability of various services, and uses the comparison between the current number of users in the system and the difference between the maximum number of users that the system can accommodate and the number of reserved users as one of the basis for admission judgment, which is simple to implement. During the implementation of the algorithm, the current user number of the system only needs to be converted into corresponding language variables according to its member functions, and then input to the fuzzy inference engine with other parameter language variables, which is simple and easy, and the calculation amount is small.

2. The present invention reduces the blocking probability of new call request users under heavy load conditions. Because the ICAC algorithm regards the current system interruption probability as one of the main bases of the admission decision, according to the fuzzy admission processor decision rule, as long as the interruption probability is close to the required threshold, the new call request will be blocked, and the present invention is by reserving resources instead of The decision is adopted to reduce the interruption probability. Therefore, after the value of reserved resources is given, as the load increases, the blocking probability of the invention must be lower than that of ICAC.

3. The present invention adopts a reservation strategy, so that the system interruption probability is still within a tolerable range under heavy load conditions. Considering both the system interruption probability and the new call request user blocking probability, the service level of the present invention is superior to the ICAC algorithm.

Description of drawings

The present invention will be described in further detail below in conjunction with accompanying drawing,

Fig. 1 is a frame diagram of the resource reservation intelligent call admission control scheme of the present invention.

Fig. 2 is a schematic diagram of a fuzzy equivalent interference estimator of the resource reservation intelligent call admission control scheme of the present invention.

Fig. 3 is a structural diagram of the neural network interference predictor of the resource reservation intelligent call admission control scheme of the present invention.

Fig. 4 is an execution flow chart of the resource reservation intelligent call admission control scheme of the present invention.

Figure 5 is the simulation result.

Embodiment 1: An intelligent call admission control device based on resource reservation, comprising:

Fuzzy equivalent interference estimator: Estimate the interference generated by the new call request based on its business parameters.

Neural Network Interference Predictor: According to the average interference of users in the system at the current moment, it predicts the interference of connected users in the system at the next moment.

Fuzzy call admission processor: according to the equivalent interference generated by the new call request output by the fuzzy equivalent interference estimator, the one-step predicted interference of connected users in the system output by the neural network interference predictor, and the number of users currently accommodated by the system, make a new call Admissibility judgment requested.

As shown in Figure 1, the service parameters of the new call request (namely peak rate R _p , average rate R _m , peak rate duration T _p and outage probability requirement P _otg ) are input into the fuzzy equivalent interference estimator to estimate the resulting Interference I _new ; the average interference I′ _k (n) of users in the system at the current moment (n time) is input to the neural network interference predictor to predict the interference I _k ^ of connected users in the system at the next moment (n+1 time) (n+1); The fuzzy call admission processor makes an admission decision according to I _new , I _k ^(n+1) and the current number of users Num accommodated by the system.

A resource reservation intelligent call admission control method, the fuzzy equivalent interference estimator is a fuzzy execution process, as shown in Figure 2, the estimator converts the service parameters of the new call request into corresponding The linguistic variables are used as the input of the fuzzy reasoning system, and then according to the corresponding fuzzy rules, the fuzzy set of the estimated interference caused by the new call request is obtained, and its value is calculated by using the defuzzification method.

According to the principle of system identification, the neural network interference predictor models the interference of connected users in the system as a nonlinear autoregressive moving average model (NARMA), and uses the NARMA model to describe the one-step prediction of the average interference as p measured interference and A function of q predicted disturbances, namely

${\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{<span\; class="notranslate">\; ^</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; p</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ;</span>{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{<span\; class="notranslate">\; ^</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{<span\; class="notranslate">\; ^</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; q</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

Among them, I _k ^(i) represents the average interference prediction value at time i(n-q+1≤i≤n) in cell k, and I′ _k (i) represents time i(n-p+1≤i≤n) The average disturbance measurement value, H( ) is an undetermined nonlinear function. The H(·) function is approximated by designing a serial feedback neural network to achieve higher prediction accuracy, faster convergence rate and lower computational complexity. Take the average interference I′ _k (n) of users in the system at the current time n as the input variable of the serial feedback neural network to accurately predict the interference I _k ^( n+1), in order to enhance the prediction accuracy, I′ _k (n) takes the mean value of system interference in N T time periods, namely

${\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}^{<span\; class="notranslate">\; \&prime;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; I</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; N</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

Among them, N represents the length of the time window. The feedback neural network interference predictor structure is shown in Fig. 3, which contains q-layer network, each layer has a similar neural network model and a subtractor. The i-th layer network has two external inputs: the delay of the measured disturbance sample value I′ _k (n-i+2) and the first output neuron Y _{i+1, 1} (n), I′ of the previous layer The difference between _k (n-i+2) and the output of the model constitutes an error signal e _i (n), which is used to dynamically adjust the weight of the i-th neural network model. The output Y _1,1 (n) of the first model is the required predicted disturbance I _k ^(n+1) at the next moment.

The fuzzy call admission processor is a fuzzy judgment process. The admission judgment value Z obtained after defuzzification by the processor is compared with the admission threshold Z ^TH . If Z>Z ^TH , the new call request is accepted, otherwise it is rejected. The resource reservation is reflected in the selection and parameter setting of the member function of the number of users currently accommodated by the system. In order to reduce the probability of interruption, a part of the number of users is reserved. Only when the current number of users of the system is not greater than the maximum number of users that the system can accommodate Only when there is a difference between the numbers, the new call requesting user may be accepted. The maximum number of users that the system can accommodate is determined by the outage probability requirements for voice and data services:

${\mathrm{<span\; class="notranslate">\; <figure-callout\; id="1"\; label="P\; otg"\; filenames="C20051001169800121.png,C20051001169800131.png"\; state="\{\{state\}\}">P</figure-callout></span><figure-callout\; id="1"\; label="P\; otg"\; filenames="C20051001169800121.png,C20051001169800131.png"\; state="\{\{state\}\}">\; </figure-callout>}}_{\mathrm{<span\; class="notranslate">\; </span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; PR</span>}<span\; class="notranslate">\; \{</span>{\mathrm{<span\; class="notranslate">\; Z</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; <figure-callout\; id="1"\; label="SIR"\; filenames="C20051001169800121.png,C20051001169800131.png"\; state="\{\{state\}\}">SIR</figure-callout></span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}^{<span\; class="notranslate">\; *</span>}<span\; class="notranslate">\; \}</span><span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; <figure-callout\; id="1"\; label="P\; otg"\; filenames="C20051001169800121.png,C20051001169800131.png"\; state="\{\{state\}\}">P</figure-callout></span><figure-callout\; id="1"\; label="P\; otg"\; filenames="C20051001169800121.png,C20051001169800131.png"\; state="\{\{state\}\}">\; </figure-callout>}}_{\mathrm{<span\; class="notranslate">\; </span>}}^{\mathrm{<span\; class="notranslate">otg</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>$

${\mathrm{<span\; class="notranslate">\; <figure-callout\; id="2"\; label="P\; otg"\; filenames="C20051001169800131.png,C20051001169800141.png"\; state="\{\{state\}\}">P</figure-callout></span><figure-callout\; id="2"\; label="P\; otg"\; filenames="C20051001169800131.png,C20051001169800141.png"\; state="\{\{state\}\}">\; </figure-callout>}}_{\mathrm{<span\; class="notranslate">\; </span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; PR</span>}<span\; class="notranslate">\; \{</span>{\mathrm{<span\; class="notranslate">\; Z</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; <figure-callout\; id="2"\; label="SIR"\; filenames="C20051001169800131.png,C20051001169800141.png"\; state="\{\{state\}\}">SIR</figure-callout></span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}^{<span\; class="notranslate">\; *</span>}<span\; class="notranslate">\; \}</span><span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; <figure-callout\; id="2"\; label="P\; otg"\; filenames="C20051001169800131.png,C20051001169800141.png"\; state="\{\{state\}\}">P</figure-callout></span><figure-callout\; id="2"\; label="P\; otg"\; filenames="C20051001169800131.png,C20051001169800141.png"\; state="\{\{state\}\}">\; </figure-callout>}}_{\mathrm{<span\; class="notranslate">\; </span>}}^{\mathrm{<span\; class="notranslate">otg</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$

${\mathrm{<span\; class="notranslate">\; Z</span>}}_{\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; v</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; k</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; v</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; d</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; k</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; \&delta;</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; R</span>}}_{\mathrm{<span\; class="notranslate">\; G</span>}}<span\; class="notranslate">\; \&Center\; Dot;</span>{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; k</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$

In formula (5), v _{i, k} and δ _{j, k} represent the activation probabilities of voice user i and data user j in cell k respectively. The maximum value of N _{v, k} and N _{d, k} is obtained from formulas (3) to (5), and the maximum number of users accommodated by the system is the largest of the two maximum values. Set the value of the number of reserved users according to the experimental method: at a certain arrival rate of users, record the system performance under different numbers of reserved users, select the reserved value with the best performance as its final value, and then According to this value, the resource reservation intelligent call admission control method is simulated and verified.

Both the fuzzy equivalent interference estimator and the fuzzy call admission processor are designed using Mamdani type fuzzy logic system, and the area center method is used for defuzzification. The calculation formula is:

$\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; =</span>\frac{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; K</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; K</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}$

Among them, _ωi represents the weight, and _Xi represents the input fuzzy set.

The flow of the above method is shown in FIG. 4 . The process steps are as follows:

Step 1: Wait for the call request to arrive,

Step 2: Estimate the interference generated by the call request,

or step 3: predict the user interference in the current system,

Or step 4: measure the number of users in the current system,

Step 5: Calculate the admission judgment value Z,

Step 6: Determine whether the judgment value Z is greater than a fixed value Z _TH , if yes, execute the following step 7, if not, refuse to return to step 1,

Step 7: Accept and allocate corresponding channels from the available channels, add one to the number of users in the system, and return to step 1.

Refer to FIG. 5 for the performance evaluation index variation curve of the resource reservation-based intelligent call admission control method and device with the arrival rate of new call requests.

Example 2:

In wireless propagation, there are mainly path and shadow losses, users are evenly distributed in the cell, and all users have perfect power control in their local cells, that is, the power of each basic channel of the voice or data service received by the base station is equal to Constant value.

The services generated by user terminals are divided into real-time voice services and non-real-time data services. The arrival of voice and data users is subject to Poisson distribution. The voice source is modeled as a two-state discrete-time Markov chain. In the ON state ( During the conversation period), an air interface packet is generated in each frame for a long T time, and during the OFF state (silent period), no air interface packet is generated, and the average duration of the conversation and silent periods obeys the parameters 1/α and 1/β respectively The exponential distribution of the data source is characterized by a group Poisson process, the average information arrival rate is A _d , and the length of the data information is a positive random variable that obeys the geometric distribution. According to the processing gain of the data service, the data unit of the high-level protocol is further divided into one Group air interface package.

Serial Feedback Neural Network Disturbance Predictor Design uses a custom approach to train the neural network using the Levenberg-Marquardt rule.

The strategy of resource reservation is used to reduce the probability of system interruption. The comparison between the current number of users in the system and the difference between the maximum number of users that the system can accommodate and the number of reserved users is used as one of the basis for admission decision. Under a certain arrival rate of users, it is recorded in The system performance under the condition of different number of reserved users, the reserved value under the best performance is selected as its final value, and then the intelligent call admission control method of resource reservation is simulated and verified according to this value.

Claims (3)

1. A resource reservation intelligent call admission control method adopts fuzzy decision-making and neural network identification technology to implement a call admission control algorithm, and the steps are as follows: Step 1: The fuzzy equivalent interference estimator waits for the arrival of the call request; Step 2: According to the service parameters of the new call request, use the fuzzy equivalent interference estimator to estimate the interference generated by the call request; Step 3: According to the average interference of users in the system at the current moment, use the neural network interference predictor to predict the interference of connected users in the system at the next moment; Step 4: The system user counter measures the number of users in the current system; Step 5: According to the interference generated by the call request, the predicted interference in the system and the number of users in the current system, use the fuzzy call admission processor to obtain the admission judgment value Z; Step 6: If the admission judgment value Z is compared with the admission threshold Z ^TH , if Z>Z ^TH , it means that the current number of users in the system is not greater than the difference between the maximum number of users that the system can accommodate and the number of reserved users, and then perform step 7, otherwise, Refuse to accept new users, return to step 1; Step 7: The channel allocator accepts new users, allocates corresponding channels from the available channels, adds one to the number of users in the system, and returns to step 1; It is characterized in that: the strategy of resource reservation is adopted to reduce the probability of system interruption, and at a certain arrival rate of users, the system performance under different reserved user numbers is recorded, and the reserved value with the best performance is selected as the final value. value. 2. a kind of resource reservation intelligent call admission control method according to claim 1 is characterized in that: the business that user terminal produces is divided into two kinds of real-time voice business and non-real-time data business, the arrival of voice and data user All obey the Poisson distribution, and the voice source is modeled as a two-state discrete-time Markov chain. During the talk period of the ON state, an air interface packet is generated in each frame for a long T time, and no air interface packet is generated during the silent period of the OFF state. The average duration of packets, calls and silent periods obeys the exponential distribution with parameters 1/α and 1/β respectively, the data source is characterized by a group Poisson process, the average information arrival rate is A _d , and the length of the data information is a positive distribution that obeys the geometric distribution. The value is a random variable, and according to the processing gain of the data service, the high-level protocol data unit is further divided into a group of air interface packets. 3. A resource reservation intelligent call admission control method according to claim 1, characterized in that: in wireless propagation, there are mainly path and shadow losses, users are evenly distributed in the cell, and all users are in their local cell Both have perfect power control, that is, the power of each basic channel of voice or data services received by the base station is equal to a constant value.

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