CN100499422C - Method for forecasting load of self-adaptive CDMA system - Google Patents

Abstract

The disclosed method is capable of modifying predicted value of load increment in self-adaptation, making prediction of system change synchronistically as variation of physical circumstances in order to reach purpose of correct prediction based on load condition in local subzone and in adjacent subzone, and time factor. The method includes following steps: (1) determining factors of influencing load prediction in local subzone, and quantizing the said factors; (2) building up data structure for saving combined values of quantized factors and predicted values of load increment, and assigning initial values for the data structure; (3) in pilot run phase for the local subzone, carrying out self-adaptating modification for data structure based on condition of practical run; (4) in phase of practical run, predicating load increment by using data in modified data structure.

Description

Adaptive code division multiple access system load predicting method

Technical field

The present invention relates to the load predicting method of code division multiple access system, particularly forecast method is carried out in the load of code division multiple access system by adaptive means.

Background technology

Code division multiple access (Code Division Multiple Access is called for short " CDMA ") system is by distributing one group of quadrature or quasi-orthogonal pseudo noise code to give different users, realizing the system of a plurality of users in same common signal channel transmission information.

Since cdma system adopts be quadrature or quasi-orthogonal pseudo noise code modulate, a user distributes a pseudo noise code, the information that other users send is equivalent to noise for this user.Along with increasing of number of users, it is big that noise becomes, and the service quality of system will decrease.Therefore cdma system is the system of a self-interference, exists certain equilibrium relation between the number of users of service quality and acceptance service simultaneously.When user in the system surpasses some, quality of services for users will sharply reduce, and may cause a large number of users call drop in the system, a series of consequences such as block error rate (Block Error Ratio is called for short " BLER ") increase.In addition, too much user also can cause interference to the user of peripheral cell.Therefore must utilize the load control algolithm that the user of system is inserted in cdma system and obstruction etc. controlled, and guarantees to maximally utilise under the prerequisite of system stable operation the resource of system.

The prerequisite of carrying out load control is a load estimation accurately.Whether accurate to load estimation, be directly connected to the load control algolithm and whether can work effectively, thereby can the decision systems capacity fully be utilized.If load estimation is inaccurate, predicted value is bigger than actual value, causes the load control algolithm to start in advance, when the result does not also reach power system capacity, inserts with regard to refusing user's, will waste power system capacity; Otherwise if predicted value is littler than actual value, the load control algolithm does not start and inserts many users, from and may cause system overload, unsettled situation appears.Yet because the characteristic of cdma system itself, its power system capacity not only distribution situation, the customer service situation with this community user is relevant, also is correlated with the current user distribution in other sub-district of periphery, service conditions.And because the channel circumstance and the coverage thereof of each sub-district are mutually different, the interference of neighbor cell, the user distribution in the sub-district also constantly change along with the time, therefore are difficult to the increase of a system load that the user causes of new access is predicted exactly.

At present new access user is caused that the prediction that system load increases mainly contains two kinds of methods.A kind of load estimation that is based on throughput, a kind of load estimation that is based on power.

, in " WCDMA for UMTS " (Chinese can be translated into " Wideband Code Division Multiple Access (WCDMA) in the universal mobile telecommunications system ") book of publishing in 2000, introduced based on the load of throughput and estimated at Harri Holma and Antti Toskala.

Formula one:

${\mathrm{\η}}_{\mathrm{UL}}=\frac{I_{\mathrm{total}}-P_N}{I_{\mathrm{total}}}$

Wherein, η _ULBe the uplink load factor of current area, I _TotalBe the total received power of base station, P _NTotal background noise for current area.

J user in the cdma system up link has following formula.

Formula two:

$P_j=\frac{1}{1+\frac{W}{{(E_b/N_0)}_j\×{\mathrm{\υ}}_j\×R_j}}\×I_{\mathrm{total}}$

Wherein, P _jBe j user's power, W represents spreading rate, (E _b/ N ₀) _JBe j user's signal to noise ratio, R _JBe j user's service rate, υ _JIt is j user's activity factor.If P _j=L _j* I _Total, then:

Formula three:

$L_j=\frac{1}{1+\frac{W}{{(E_b/N_0)}_j\×{\mathrm{\υ}}_j\×R_j}}$

Consider the interference of other sub-districts to this sub-district, formula three can be revised as:

Formula four:

$L_j=(1+\mathrm{\α})\×\frac{1}{1+\frac{W}{{(E_b/N_0)}_j\×{\mathrm{\υ}}_j\×R_j}}$

Wherein, α is the interference factors of other sub-districts to this sub-district.

Total user that receive the base station disturbs and is:

Formula five:

$I=I_{\mathrm{total}}-P_N=\underset{j=1}{\overset{K}{\mathrm{\Σ}}}{P}_j=\underset{j=1}{\overset{K}{\mathrm{\Σ}}}{L}_j\×I_{\mathrm{total}}$

Wherein, K represents number of loads.Can obtain in conjunction with formula one and formula five:

Formula six:

${\mathrm{\η}}_{\mathrm{UL}}=\underset{j=1}{\overset{K}{\mathrm{\Σ}}}{L}_j$

When increasing a user m newly, its ascending load factor increases to:

Formula seven:

${\mathrm{\η}}_{\mathrm{UL}}^{\′}={\mathrm{\η}}_{\mathrm{UL}}+L_m$

When utilizing this method to predict, need provide (E according to type of service _b/ N ₀) _JAnd υ _JEtc. parameter, provide P according to the environment of service area _NValue with α.

Introduce below based on the load of power and estimate.The down load number of sub-district can define with the total transmitting power of down link and the maximum transmission power of base station.

Formula eight:

${\mathrm{\η}}_{\mathrm{DL}}=\frac{P_{\mathrm{total}}}{P_{\max }}$

Wherein, η _DLBe the downlink load factor, P _TotalBe the total transmitting power of down link, P _MaxIt is the maximum transmission power of base station.

After inserting a new user j, can calculate the power P that needs are distributed to this user according to this user's service rate and desired signal to noise ratio _j, then:

Formula nine:

$P_{\mathrm{total}}^{\′}=P_{\mathrm{total}}+P_j$

And then can predict the increment of down load.

In actual applications, there is following problem in such scheme: prior art all is that the user evenly distributes in the supposition sub-district, obtains the required (E of load estimation with statistics or method of emulation _b/ N ₀) _j, υ _J, the α equivalence, and in the system of reality, each cell channel environment and coverage thereof are different, user's distribution is also along with the time constantly changes in the interference of neighbor cell, the sub-district.If adopt two kinds of methods of the prior art, (E _b/ N ₀) _j, υ _J, constant value that employing provides such as α, to the prediction of load can't be as the case may be variation and make corresponding adjustment, thereby cause prediction result and actual conditions inconsistent.

Cause the main cause of this situation to be, in forecasting process to (E _b/ N ₀) _J, υ _J, equivalent what adopt is a fixing value to α, has ignored the concrete operational environment of sub-district and user distribution to the influence of these values, makes prediction can not make corresponding adjustment according to specific circumstances, thereby cause predicting the outcome inaccurate.

Summary of the invention

The technical problem to be solved in the present invention provides a kind of adaptive code division multiple access system load predicting method, the system that makes can be according to the loading condition of this sub-district, the loading condition and the time factor of neighbor cell, revise predicted values of load increment adaptively, the prediction of system is changed synchronously with the variation of concrete condition, thereby reach the purpose of accurate prediction.

In order to solve the problems of the technologies described above, the invention provides a kind of adaptive code division multiple access system load predicting method, comprise following steps:

A determines to influence the factor of cell load prediction and quantize;

B sets up in order to preserving the data structure of described factors quantization value combination and predicted values of load increment, and to this data structure initialize;

C carries out self adaptation correction according to load estimation value, actual loading value and threshold value to the described predicted values of load increment in the described data structure in the trial run stage of described sub-district;

D uses the data in the revised described data structure that incremental loading is predicted in the described sub-district actual motion stage.

Wherein, the step of described self adaptation correction further comprises following substep:

C1 inserts the preceding former load value of measuring described sub-district of new user;

C2 finds corresponding described predicted values of load increment according to the quantized value combination of current described factor in described data structure;

C3 adds the above former load value with described predicted values of load increment, obtains inserting the load estimation value behind the described new user;

C4 measure to insert the actual loading value behind the described new user, and the difference of judging this actual loading value and described load estimation value if then enter step C1, otherwise enters step C5 whether in thresholding;

C5 enters step C1 according to the described predicted values of load increment of described actual loading value correction.

Among the described step C5, described predicted values of load increment is corrected for the difference of described actual loading value and described former load value.

Among the described step C5, described predicted values of load increment is revised by the method for getting confidential interval according to statistical law repeatedly.

Also comprise following steps:

In the described sub-district actual motion stage, when finding that load estimation result and the bigger or described sub-district of actual loading deviation ratio moving law change, according to reality operation situation the described predicted values of load increment in the described data structure is carried out the self adaptation correction once more.

The initial value of described data structure is the runtime value of simulation result or other sub-districts.

Described factor comprises the loading condition of described sub-district, the loading condition of the peripheral cell of described sub-district, and time.

In described step B, each type of service is all set up related data structure.

Described type of service is a kind of or its combination in any in speech business, data service, the video traffic.

Described data structure is form or array.

By relatively finding, technical scheme difference with the prior art of the present invention is, by finding out the factor that influences this cell load prediction in the sub-district that has a specific run rule at and being quantized, adopt the suitable data structure to store the combination of above-mentioned quantification and each makes up pairing load estimation increment size to various types of traffic, load estimation increment size in the suitable algorithm self adaptation correction data structure of trial run stage employing, thus conform to actual conditions at the load estimation of guaranteeing the actual motion stage.If the moving law of sub-district changes, system can reopen adaptive process correction predicted values of load increment, thereby the variation of the variation of proof load predicted value and sub-district moving law is synchronous.

Difference on this technical scheme, brought comparatively significantly beneficial effect, promptly feasible load estimation to certain specific cell approaches the actual conditions of sub-district operation more, and when the moving law of sub-district changes, prediction also can correspondingly be adjusted automatically, reach the purpose of accurate prediction, and then avoid forecasting inaccuracy really to cause the problem that the too much a large number of users of load goes offline or power system capacity is not fully utilized.

Description of drawings

Fig. 1 is six omni cell distribution schematic diagrams of cdma system;

Fig. 2 is the flow chart of self-adaption CDMA load predicting method according to an embodiment of the invention;

Fig. 3 is the flow chart of self adaptation correction predicted values of load increment according to an embodiment of the invention.

Embodiment

For making the purpose, technical solutions and advantages of the present invention clearer, the present invention is described in further detail below in conjunction with accompanying drawing.

At first, a next specific sub-district is simply described, the universal law during its actual motion with reference to Fig. 1.As shown in Figure 1, this six omni cell distribution schematic diagram is made of 7 sub-districts sub-district 00～60 totally, and sub-district 00 is positioned at the center, sub-district 10～60 be six omnidirectionals be distributed in sub-district 00 around.

In general the service distribution of sub-district can change over time.For example sub-district 00 is divided into the working region and the zone of having a rest.When the operating time, business mainly is distributed in the working region, and when having a rest the zone, business mainly is distributed in the zone of having a rest.Therefore, when work, the influence that the neighbor cell of close working region is subjected to is bigger; When rest, the influence of receiving near the neighbor cell in the zone of having a rest is bigger.

When some sub-district was moved, the rule of himself was exactly the rule of user distribution in fact.Sub-district 00 is divided into user's close quarters and the sparse zone of user, and it is bigger obviously to be subjected to the influence of this sub-district near those neighbor cells of user's close quarters in the neighbor cell, then less near the influence that the neighbor cell in the sparse zone of user is subjected to.

Below with reference to Fig. 2, introduce the process that adopts intelligentized self-adaption CDMA load predicting method that one embodiment of the present of invention are predicted.

In step 100, determine to influence the factor of cell load prediction according to the concrete condition of sub-district.Those of ordinary skill in the art should be understood that the factor that influences cell load prediction mainly contains the loading condition of sub-district, loading condition, the time factor of neighbor cell on every side.In a preferred embodiment of the present invention, sub-district 10～60 is six omnidirectionals and is distributed in around the sub-district 00, and the loading condition of establishing sub-district 00 is f0, the loading condition of sub-district 10～60 is respectively f1～f6, time is made as t, and then the predicted values of load increment delta of this sub-district is with (f0, f1, f2, f3, f4, f5, f6, t) in any one element variation and the function that changes.

Then enter step 110, the factor that influences the cell load prediction is carried out quantification treatment.Can carry out multi-stage quantization to each factor.In a preferred embodiment of the present invention, can turn to the load capacity of each sub-district light, weigh two grades.Can be quantified as time 1 and time 2 to the time.

After this enter step 120, each type of service is adopted the combination of certain various quantized values of data structure storage and the pairing predicted values of load increment of combination of various quantized values.It is multiple that those of ordinary skill in the art should be understood that type of service that cdma system provides has, for example speech business, data service, video traffic or the like.Each type of service is distributed corresponding predicted value memory space.The data structure that adopts can be form or Multidimensional numerical.In a preferred embodiment of the present invention, predict for the speech business that provides 12.2K, at first to the load capacity of sub-district 00～60 turn to light, weigh two grades, be time 1 and time 2 to time quantization, the data that can use the two-dimensional array of one 8 row to preserve caluclate table.Example is as shown in table 1:

Table 1

f0	f1	f2	f3	f4	f5	f6	t	Delta
									Gently	Gently	Gently	Gently	Gently	Gently	Gently	Time 1
Heavy	Gently	Gently	Gently	Gently	Gently	Gently	Time 1
									Gently	Heavy	Gently	Gently	Gently	Gently	Gently	Time 1
Heavy	Heavy	Gently	Gently	Gently	Gently	Gently	Time 1
									Gently	Gently	Heavy	Gently	Gently	Gently	Gently	Time 1
Heavy	Gently	Heavy	Gently	Gently	Gently	Gently	Time 1
									...	...	...	...	...	...	...	...
...	...	...	...	...	...	...	...
									Heavy	Heavy	Heavy	Heavy	Heavy	Heavy	Heavy	Time 2

Then enter step 130, in the starting stage, the predicted values of load increment initialize of result that utilizes other approach to obtain to storing in the data structure.Initial value can be the result who obtains from the result of other Pilot offices or emulation, and initial value does not need very accurate, and system can self adaptation adjust and revise in the operation phase.

After this enter step 140, enter the trial run stage, the predicted values of load increment of storing in the data structure is revised adaptively according to the ruuning situation of reality by system.After trial run is finished, the predicted values of load increment of storing in the data structure can intactly show the growth pattern of this cell load, and can embody the actual motion rule of this sub-district.According to a preferred embodiment of the present invention, can come the process of illustrative system with flow chart shown in Figure 3 according to actual conditions self adaptation correction predicted values of load increment.

As shown in the figure, at first, in step 200, system is by measuring the load value Load_old that obtained before inserting a new user.

Then enter step 210,, in data structure, search corresponding predicted values of load increment delta according to the value that the current various influencing factors of this type of service quantize.

After this enter step 220,, calculate the load value Load_newpre of system behind this user of access of prediction by formula Load_newpre=Load_old+delta.

Then in step 230, because the load value of prediction entirely accurate not necessarily, need to measure the load value Load_new behind actual this user of access.Relatively whether the difference of Load_new and Load-newpre in thresholding, if then directly enter step 200, carries out the adjustment of a new round, otherwise, enter step 240.

In step 240, utilize formula delta=Load_new-Load_old that predicted values of load increment delta is revised.In the use of reality, can adopt other modification method as the case may be, as getting methods such as confidential interval according to statistical law repeatedly.And then enter step 200, carry out new round adjustment.

After repeatedly reruning, can make that predicted values of load increment conforms to actual conditions in the data structure.

After the trial run correction, enter step 150 at last, promptly enter the actual motion stage.This moment, the correction of system closing self adaptation was directly carried out load estimation according to the predicted values of load increment of storing in the data structure.If when prediction result deviation the moving law big or sub-district changes, need reopen the self adaptation makeover process, revise predicted values of load increment.The algorithm of its load estimation can be: Load '=Load+delta.Wherein Load ' is the cell load predicted value behind the access user, and Load inserts the cell load measured value before the user, and delta searches the predicted values of load increment that data structure obtains according to the combination of cell load influencing factor quantized value under the current business.The prediction result deviation is meant that more greatly the incremental loading value of storing in the incremental loading value of actual measurement and the data structure differs bigger; The moving law of sub-district changes, and the user distribution rule that is meant current area or peripheral cell changes or the environment of sub-district changes, and as the place of original sub-district spaciousness, newly-built high building causes that the user distribution rule changes etc.

Though by reference some preferred embodiment of the present invention, the present invention is illustrated and describes, but those of ordinary skill in the art should be understood that, can do various changes to it in the form and details, and the spirit and scope of the present invention that do not depart from appended claims and limited.

Claims (10)

1. an adaptive code division multiple access system load predicting method is characterized in that, comprises following steps:

A determines to influence the factor of cell load prediction and quantize;

B sets up in order to preserving the data structure of described factors quantization value combination and predicted values of load increment, and to this data structure initialize;

C carries out self adaptation correction according to load estimation value, actual loading value and threshold value to the described predicted values of load increment in the described data structure in the trial run stage of described sub-district;

D uses the data in the revised described data structure that incremental loading is predicted in the described sub-district actual motion stage.

2. adaptive code division multiple access system load predicting method according to claim 1 is characterized in that the step of described self adaptation correction further comprises following substep:

C1 inserts the preceding former load value of measuring described sub-district of new user;

C2 finds corresponding described predicted values of load increment according to the quantized value combination of current described factor in described data structure;

C3 adds the above former load value with described predicted values of load increment, obtains inserting the load estimation value behind the described new user;

C4 measure to insert the actual loading value behind the described new user, and the difference of judging this actual loading value and described load estimation value if then enter step C1, otherwise enters step C5 whether in thresholding;

C5 enters step C1 according to the described predicted values of load increment of described actual loading value correction.

3. adaptive code division multiple access system load predicting method according to claim 2 is characterized in that among the described step C5, described predicted values of load increment is corrected for the difference of described actual loading value and described former load value.

4. adaptive code division multiple access system load predicting method according to claim 2 is characterized in that, among the described step C5, by the method for getting confidential interval according to statistical law repeatedly described predicted values of load increment is revised.

5. adaptive code division multiple access system load predicting method according to claim 1 is characterized in that, also comprises following steps:

In the described sub-district actual motion stage, when finding that load estimation result and the bigger or described sub-district of actual loading deviation ratio moving law change, according to reality operation situation the described predicted values of load increment in the described data structure is carried out the self adaptation correction once more.

6. adaptive code division multiple access system load predicting method according to claim 1 is characterized in that the initial value of described data structure is the runtime value of simulation result or other sub-districts.

7. adaptive code division multiple access system load predicting method according to claim 1 is characterized in that described factor comprises the loading condition of described sub-district, the loading condition of the peripheral cell of described sub-district, and time.

8. adaptive code division multiple access system load predicting method according to claim 1 is characterized in that, in described step B, each type of service is all set up related data structure.

9. adaptive code division multiple access system load predicting method according to claim 8 is characterized in that, described type of service is a kind of or its combination in any in speech business, data service, the video traffic.

10. adaptive code division multiple access system load predicting method according to claim 1 is characterized in that described data structure is form or array.

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