CN103338470B - Spectrum requirement Forecasting Methodology and device - Google Patents

Abstract

The invention provides a kind of spectrum requirement Forecasting Methodology and device, method comprises: the traffic carrying capacity of user in the future time of setting in estimation range, and the transmission rate of the business of different areas in this region in this traffic carrying capacity; According to the allocation proportion of the transmission rate of this business, default macro base station total number, macro base station different areas in this region, in estimation range different areas: macro base station number, micro-number of base stations and each self-corresponding service bearer ability thereof; According to the transmission rate of the business of different areas in this region in the traffic carrying capacity of user in future time, predict different areas in this region of obtaining: macro base station number, micro-number of base stations and each self-corresponding service bearer ability thereof, predict the frequency spectrum that in this region, in future time, user uses.It is not high that the embodiment of the present invention efficiently solves prior art prediction network spectrum demand accuracy, and then can not carry out the technical problem of reasonably configuration to the frequency spectrum resource of network.

Description

Spectrum requirement Forecasting Methodology and device

Technical field

The present invention relates to communication technical field, particularly relate to a kind of spectrum requirement Forecasting Methodology and device.

Background technology

Along with the fast development of global mobile broadband communication, the data traffic of mobile communication subscriber acutely rises, and frequency is the basic resource of Mobile Communication Development, and its demand also increases thereupon fast.In order to the use of make rational planning for country or telecom operators' frequency spectrum resource, National Radio authorities or telecom operators need to be applied to the spectrum requirement amount of mobile communication to following a period of time and service condition is predicted.

In prior art, adopt a typical mobile communication scene, as predicted whole mobile communications network spectrum requirement by the corresponding relation of monitoring different business amount and required frequency spectrum in city.But the accuracy that predicts the outcome that this Forecasting Methodology obtains is not high, reasonably can not configure the frequency spectrum resource of network.

Summary of the invention

The invention provides a kind of spectrum requirement Forecasting Methodology and device, not high in order to solve prior art prediction network spectrum demand accuracy, and then the technical problem of reasonably configuration can not be carried out to the frequency spectrum resource of network.

On the one hand, the embodiment of the present invention provides a kind of spectrum requirement Forecasting Methodology, comprising:

The traffic carrying capacity of user in the future time of setting in estimation range;

According to the distribution of traffic situation of historical time user in described region, the transmission rate of the business of different areas in region described in the traffic carrying capacity determining user in described future time;

According to the allocation proportion of the transmission rate of the business of different areas in region described in the traffic carrying capacity of user in described future time, default macro base station total number, described macro base station different areas in this region, predict the described macro base station number of different areas in described region;

According to the described macro base station number of different areas in the transmission rate of the business of different areas in region described in the traffic carrying capacity of user in described future time, described region, predict micro-number of base stations of different areas in described region;

According to the transmission rate of the business of different areas in region described in the traffic carrying capacity of user in described future time, predict the service bearer ability of described macro base station in different areas in described region and the service bearer ability of described micro-base station;

According to the transmission rate of the business of different areas in region described in the traffic carrying capacity of user in described future time, predict different areas in the described region that obtains: the service bearer ability of described macro base station number, described micro-number of base stations, described macro base station and the service bearer ability of described micro-base station, predict the frequency spectrum that in described region, in future time, user uses.

On the other hand, the embodiment of the present invention provides a kind of spectrum requirement prediction unit, comprising: prediction module and processing module;

Described prediction module, for the traffic carrying capacity of user in the future time that sets in estimation range;

Described processing module, for the distribution of traffic situation according to historical time user in described region, the transmission rate of the business of different areas in region described in the traffic carrying capacity determining user in described future time;

Described prediction module, also for:

According to the allocation proportion of the transmission rate of the business of different areas in region described in the traffic carrying capacity of user in described future time, default macro base station total number, described macro base station different areas in this region, predict the described macro base station number of different areas in described region;

According to the described macro base station number of different areas in the transmission rate of the business of different areas in region described in the traffic carrying capacity of user in described future time, described region, predict micro-number of base stations of different areas in described region;

According to the transmission rate of the business of different areas in region described in the traffic carrying capacity of user in described future time, predict the service bearer ability of described macro base station in different areas in described region and the service bearer ability of described micro-base station;

According to the transmission rate of the business of different areas in region described in the traffic carrying capacity of user in described future time, predict different areas in the described region that obtains: the service bearer ability of described macro base station number, described micro-number of base stations, described macro base station and the service bearer ability of described micro-base station, predict the frequency spectrum that in described region, in future time, user uses.

Spectrum requirement Forecasting Methodology provided by the invention and device, by the transmission rate of the business of user in the future time of the setting of different areas in acquisition region; And in this region in different areas: the service bearer ability of macro base station number, micro-number of base stations, macro base station and the service bearer ability of micro-base station, the frequency spectrum that in this region of final prediction, in future time, user uses, improve the accuracy of prediction, and then reasonably can configure the frequency spectrum resource of network.

Accompanying drawing explanation

Fig. 1 is the flow chart of a spectrum requirement Forecasting Methodology provided by the invention embodiment;

Fig. 2 is the flow chart of another embodiment of spectrum requirement Forecasting Methodology provided by the invention;

Fig. 3 is the structural representation of a spectrum requirement prediction unit provided by the invention embodiment.

Embodiment

The techniques described herein may be used in the spectrum requirement prediction of various communication network, such as current 2G, 3G communication system and next generation communication system, such as global system for mobile communications (GSM, GlobalSystemforMobilecommunications), code division multiple access (CDMA, CodeDivisionMultipleAccess) system, time division multiple access (TDMA, TimeDivisionMultipleAccess) system, Wideband Code Division Multiple Access (WCDMA) (WCDMA, WidebandCodeDivisionMultipleAccessWireless), frequency division multiple access (FDMA, FrequencyDivisionMultipleAddressing) system, OFDM (OFDMA, OrthogonalFrequency-DivisionMultipleAccess) system, Single Carrier Frequency Division Multiple Access (SC-FDMA) system, GPRS (GPRS, GeneralPacketRadioService) system, Long Term Evolution (LTE, LongTermEvolution) system, and other these type of communication systems.

Fig. 1 is the flow chart of a spectrum requirement Forecasting Methodology provided by the invention embodiment.As shown in Figure 1, the executive agent of following steps can be the network equipment, server in network, or is integrated in the module, chip etc. on this network equipment or server.As shown in Figure 1, this spectrum requirement Forecasting Methodology specifically comprises:

S101, the traffic carrying capacity of user in the future time of setting in estimation range;

Wherein, the different area of one or more density of population such as city, suburb, rural area can be comprised in predicted region.To predict in this region in the future time of setting, as one month, 1 year, the during the decade user institute traffic carrying capacity that uses mobile communication business to produce, the historical traffic that can pass through to produce in this region was added up, and analyzes its variation tendency; And population trends in this region, this population trends carries out integrated forecasting to aspects such as the impacts of Added Business amount.As considering through above-mentioned each factor, determine the number of users in the future time of setting in region, and the traffic carrying capacity that average each user uses, then the predicted value of the traffic carrying capacity of user in the future time that the product of the traffic carrying capacity that this number of users and average each number of users can be used sets in this region.

S102, according to the distribution of traffic situation of historical time user in this region, determines the transmission rate of the business of different areas in this region in the traffic carrying capacity of user in above-mentioned future time;

Regional population in predicted region can comprise the areas such as above-mentioned city, suburb, rural area.By the distribution of traffic situation analysis to historical time user in this region, can be informed in the urban area that the density of population is larger, mobile communication network device is disposed comparatively perfect, and the traffic carrying capacity that in this area, user uses is also relatively many; And suburb because of its density of population relatively less, and mobile communication network device dispose relative rarity, this area produce traffic carrying capacity also relatively less; The traffic carrying capacity that rural area produces is then less.According to the distribution of traffic situation of above historical time user, can will predict in step 101 that the traffic carrying capacity of user in the future time that obtains is distributed according to the distribution of traffic situation of different areas in this region, obtain the traffic carrying capacity of different areas in this region, then according to the time that these traffic carrying capacitys produce, the transmission rate of the business of different areas in this region in the traffic carrying capacity of user in future time is obtained.

S103, according to the allocation proportion of the transmission rate of the business of different areas in this region in the traffic carrying capacity of user in future time, default macro base station total number, this macro base station different areas in this region, predict the described macro base station number of different areas in this region;

Wherein, this macro base station total number preset can be the development according to existing network in this estimation range, concrete number as the base station deployment of each department announced annual operator carries out statistical analysis, predicts the macro base station number of this estimation range in the future time that obtains.According to the allocation proportion of the macro base station total number preset, this macro base station different areas in this region, the macro base station number being assigned to variant type communities can be obtained roughly.And in practical application scene, even between the area of identical type, because its area is different, in area, number of users is different, also there is larger difference in the traffic carrying capacity causing these users to use.Also will do further judgement according to the transmission rate of the business of different areas in this region in the traffic carrying capacity of user in future time in this case, the transmission rate of business has directly reacted the macro base station total number carried required for it.Therefore, in the allocation proportion of the macro base station total number by presetting, this macro base station different areas in this region, after determining the general macro base station number that different areas is assigned with, the macro base station number of transmission rate to each department distributed according to the business of different areas in this region in the traffic carrying capacity of user in future time adjusts, more reasonable to make it distribute.Such as, under normal circumstances, the traffic carrying capacity of the user in city is comparatively large relative to the traffic carrying capacity in suburb and rural area, therefore, higher both the allocation proportion of macro base station in urban area is relatively rear.And under some special screnes, as less relative to other urban area areas in certain urban area, number of users entirety is less, the transmission rate of customer service is relatively little, therefore on the basis of original allocation proportion, suitably can reduce the distribution number of macro base station, more reasonable to make macro base station distribute between all types of area.

S104, according to the macro base station number of different areas in the transmission rate of the business of different areas in this region in the traffic carrying capacity of user in future time, this region, predicts micro-number of base stations of different areas in this region;

Consider the characteristic of traffic carrying capacity uneven distribution spatially, all there is the most traffic carrying capacity of " focus " region absorption in different areas, and " cold spot " region only carries the situation of a small amount of business.For " focus " region, micro-base station of fixed qty can be set in the macrocell that each macro base station is corresponding in this region.The frequency spectrum that this micro-base station is used by the macro base station of multiplexing fixed-bandwidth, shunts too high traffic densities, thus reduces the transmission rate of the bearer service of macro base station.

According to the size of the traffic carrying capacity of user in the future time transmission rate of the business of different areas in this region in the present embodiment, judge whether the type area is " focus " area.If the transmission rate of business that the type area carries is very large, close to or beyond the maximum traffic bearing capacity of the macro base station in this area, then think that this area is " focus " area.According to the macro base station number in this hot zones, and be fixing micro-number of base stations of each macro base station configuration, micro-number of base stations of different areas in this region can be obtained.

S105, according to the transmission rate of the business of different areas in this region in the traffic carrying capacity of user in future time, predicts the service bearer ability of macro base station in different areas in this region and the service bearer ability of micro-base station;

Wherein, service bearer ability refers to that each macro base station or micro-base station utilize the maximum transmission rate for bearer service that can provide of every MHz bandwidth, and under different network formats, this service bearer ability is different.This service bearer ability and macro base station or micro-base station: spectrum efficiency, the sector number comprised, the transmission rate situation of business carried are relevant with maximum bearing load.Wherein, spectrum efficiency is in mobile communications network, and according to the different network formats technology adopted, the spectrum efficiency numerical value obtained, this numerical value will determine the service bearer ability that the frequency spectrum of operator's use same band can provide.According to the transmission rate of the business of different areas in this region in the traffic carrying capacity of user in future time, and each macro base station or micro-base station itself: spectrum efficiency, the sector number comprised and maximum bearing load, the service bearer ability of macro base station and the service bearer ability of micro-base station in different areas in this region measurable.

S106, according to the transmission rate of the business of different areas in region described in the traffic carrying capacity of user in future time, predict different areas in this region of obtaining: the service bearer ability of macro base station number, micro-number of base stations, macro base station and the service bearer ability of micro-base station, predict the frequency spectrum that in this region, in future time, user uses;

The transmission rate deducting the shunting of micro-base station in the transmission rate of the business of different areas in this region in future time is obtained in prediction, the transmission rate of carrying is sent with the macro base station obtaining different areas in this region, then, according to the service bearer ability of each macro base station and the number of macro base station, obtain the frequency spectrum that in this region, in future time, user uses.Wherein, the transmission rate of micro-base station shunting obtains by the service bearer ability of micro-number of base stations, micro-base station and the bandwidth of distributing to micro-base station.

Spectrum requirement Forecasting Methodology provided by the invention, by the transmission rate of the business of user in the future time of the setting of different areas in acquisition region; And in this region in different areas: the service bearer ability of macro base station number, micro-number of base stations, macro base station and the service bearer ability of micro-base station, the frequency spectrum that in this region of final prediction, in future time, user uses, improve the accuracy of prediction, and then reasonably can configure the frequency spectrum resource of network.

Fig. 2 is the flow chart of another embodiment of spectrum requirement Forecasting Methodology provided by the invention, is a kind of concrete implementation of embodiment as shown in Figure 1.As shown in Figure 2, described method specifically comprises:

S201, the traffic carrying capacity of user in the future time of setting in estimation range; The concrete implementation of this step can see the corresponding contents of step 101.Because user uses mobile communications network business to comprise speech business and data service, therefore in this step, in estimation range setting future time in user traffic carrying capacity in also comprise voice services volume and data business volume.

S202, according to T _v=M _v× 60 × K(1), by voice services volume M _vbe converted to equivalent data traffic carrying capacity T _v; Wherein, K is conversion coefficient;

Conveniently to predicting that the voice services volume that obtains and data business volume unify process, voice services volume can be converted to equivalent data traffic carrying capacity by (1) formula; Wherein, M _vfor the message minute of speech business, the unit of K are Kbps.

S203, is defined as the traffic carrying capacity of user in the future time that sets in region by equivalent data traffic carrying capacity and data business volume sum;

Voice services volume M in the traffic carrying capacity of user in the future time that sets in the region that obtains will be predicted _vbe converted to equivalent data traffic carrying capacity T _v, then by this equivalent data traffic carrying capacity T _vthe traffic carrying capacity of user in the future time that sets in region is defined as with the data business volume sum in the traffic carrying capacity of former prediction.So, subsequent step can be enable to unify to process to this traffic carrying capacity.

For in step 102, according to the distribution of traffic situation of historical time user in region, determine the transmission rate of the business of different areas in this region in the traffic carrying capacity of user in above-mentioned future time; The present embodiment step 204 ~ 206 give a kind of concrete implementation.

S204, according to $\mathrm{TBH}=\frac{(T_d+T_V)\×\mathrm{DLR}\×\mathrm{BHR}}{t}---\left(2\right)$

Obtain the transmission rate TBH of the busy downlink business in traffic carrying capacity; Wherein, T _dfor data business volume, T _vfor the duration that equivalent data traffic carrying capacity, the DLR ratio of downlink traffic in total traffic that be historical time user, the BHR ratio of busy-hour traffic at total traffic that be historical time user, t are busy in future time;

Consider to predict the feature (these features can obtain with reference to after the distribution of traffic situation analysis to historical time user in this region) of the asymmetry of the traffic carrying capacity of user that obtains uneven distribution in time and up-downgoing, in practical application scene, can there is certain centrality in the distribution of traffic carrying capacity.Wherein, traffic carrying capacity intensity in time has then directly reacted required amount of frequency spectrum.The present embodiment, while considering traffic carrying capacity intensity in time, also contemplates the distribution situation of up-downgoing business in total traffic, eventually through the transmission rate TBH of the busy downlink business in (2) formula computing service amount.Definition for " busy " can be the traffic carrying capacity that produces this period beyond set point, or certain set time section etc. in a day.

S205, according to TBH _m=TBH × P _m× U _m(3)

Obtain the transmission rate TBH of busy downlink business in future time _m; Wherein, the network formats, the P that adopt for region of the TBH transmission rate that is busy downlink business under all standards, m _mfor accounting, the U of user under m network formats _mfor user each under m network formats uses the normalized parameter of traffic carrying capacity;

Wherein, the network formats that m adopts for this estimation range, can comprise the second generation/third generation/forth generation mobile communication technology (Second-Generation/Third-Generation/Fourth-Generation, 2G/3G/4G); P _mfor the accounting in the user under the user under m network formats in this region all standards; U _mfor user each under m network formats uses the normalized parameter of traffic carrying capacity, be used for representing each user's relative business use amount belonged in m network, such as: usually by U _3Gbe set to 1, represent that average each 3G subscription uses the traffic carrying capacity of 1 unit, if U _2Gand U _4Gbe respectively a(and be less than 1) and b(be greater than 1), then represent that average each 2G user uses the traffic carrying capacity of a unit, average each 4G user uses the traffic carrying capacity of b unit.

S206, according to TBH _m,n=TBH _m× P _n× U _n(4)

Obtain the transmission rate TBH of downlink business corresponding in different areas in this region in future time _m,n; Wherein, n is the type in area, TBH _mfor transmission rate, the P of the busy downlink business under m network formats _nfor accounting, the U of user in n type communities under m network formats _nfor the type under m network formats be n area in each user use the normalized parameter of traffic carrying capacity;

Wherein, P _nbe specially the accounting in number of users under m network formats in the n type communities number of users under this m network formats under all types area; U _nfor user each in n type communities under m network formats uses the normalized parameter of traffic carrying capacity, this normalized parameter is used for representing for the every user's relative business use amount belonged under consolidated network standard in different regions.Such as, can by U _sbe set to 1, represent that average each rural subscriber uses the traffic carrying capacity of 1 unit, if U _uand U _rbe respectively c(and be greater than 1) and d(be less than 1), then represent that average each city user uses the traffic carrying capacity of c unit, average each rural subscribers use the traffic carrying capacity of d unit.

S207, according to the allocation proportion of the transmission rate of the business of different areas in this region in the traffic carrying capacity of user in future time, default macro base station total number, macro base station different areas in the zone, the macro base station number of different areas in estimation range; The concrete implementation of this step can see the corresponding contents of step 103.

S208, sorts the size of the macro base station of identical type communities according to the transmission rate of the business carried, and is divided into the equal j group macro base station group of number;

Consider the characteristic of traffic carrying capacity uneven distribution spatially, the most traffic carrying capacity of " focus " region absorption is all there is in different areas, " cold spot " region only carries the situation of a small amount of business, to predict that the size of the macro base station of the identical type communities obtained according to the transmission rate of the business carried sorts in this step, and be divided into the equal j group macro base station group of number.Judge the transmission rate of business that each macro base station group carries and the magnitude relationship of threshold value by subsequent step again, determine whether as each macro base station configures the number of micro-base station and configuration.

S209, according to ${\mathrm{MSN}}_n=\underset{i=1}{\overset{j}{\mathrm{\Σ}}}{\mathrm{MSN}}_{\mathrm{ni}}---\left(5\right)$

${\mathrm{MSN}}_{\mathrm{ni}}=\left\{\begin{array}{cc}{\mathrm{SN}}_{\mathrm{ni}}\&times;\mathrm{MSPS},& \frac{{\mathrm{TBH}}_n\&times;{\mathrm{TR}}_i}{{\mathrm{SN}}_{\mathrm{ni}}/{\mathrm{SD}}_n}\&GreaterEqual;G_{\mathrm{MS}}\\ 0,& \frac{{\mathrm{TBH}}_n\&times;{\mathrm{TR}}_i}{{\mathrm{SN}}_{\mathrm{ni}}/{\mathrm{SD}}_n}<G_{\mathrm{MS}}\end{array}\right.---\left(6\right),$

Type of prediction is micro-number of base stations MSN in the area of n _n; Wherein, i is integer, the MSN of 1 to j _nifor type is the micro-number of base stations, the SN that comprise in i-th group of macro base station group in the area of n _nibe the micro-number of base stations, the TBH that comprise in the macrocell of each macro base station in the area of n for type be the macro base station number, the MSPS that comprise in i-th group of macro base station group in the area of n is type _nfor the transmission rate that type is busy downlink business corresponding in the area of n, TR _ifor ratio, SD that type is in the transmission rate of transmission rate busy downlink business of correspondence in the area that all types is n of the busy downlink business of i-th group of macro base station carrying in the area of n _nfor the site density that type is the macro base station in the area of n, G _mSfor setting up the transmission rate density threshold value of the busy downlink business needed for micro-base station at each macro base station;

(5) and (6) formula in this step is by judging whether the transmission rate distribution density spatially of the corresponding busy downlink business carried in each macro base station group is reached for the transmission rate density threshold value G that each macro base station sets up the busy downlink business needed for micro-base station _mS, determine whether to set up micro-base station in the macrocell of each macro base station in this macro base station group, and the concrete number set up.If by TBH _ndifference according to the network formats at place carries out refinement, and so can also obtain is micro-number of base stations MSN in the area of n for the type under different network formats m _m,n.Step 208 ~ 209 can be considered a kind of specific implementation of step 104.

S210, according to the transmission rate of the business of different areas in this region in the traffic carrying capacity of user in future time, predicts the service bearer ability of macro base station in different areas in this region and the service bearer ability of micro-base station; The concrete implementation of this step can see the corresponding contents of step 105.As further illustrating step 105, this gives following scheme:

According to MaT=FE _ma× ASN _ma× L(7)

MiT=FE _Mi×ASN _Mi（8），

Calculate n type communities respectively: the service bearer ability MaT of macro base station and the service bearer ability MiT of micro-base station; Wherein, Ma is the attribute-bit of macro base station, Mi is attribute-bit, the FE of micro-base station _mafor spectrum efficiency, the ASN of macro base station _mafor average sector number, the FE of macro base station _mifor spectrum efficiency, the ASN of micro-base station _mifor the average sector number of micro-base station, the L network system of transmission rate in this region that be busy downlink business energy bearer service transmission rate maximum capacity value in ratio.

S211, according to the transmission rate of the business of different areas in this region in the traffic carrying capacity of user in future time, predict different areas in the region that obtains: the service bearer ability of macro base station number, micro-number of base stations, macro base station and the service bearer ability of micro-base station, the frequency spectrum that in estimation range, in future time, user uses; The concrete implementation of this step can see the corresponding contents of step 106.As further illustrating step 106, this gives following scheme:

According to BW _n=MAX (BW _n1, BW _n2..., BW _nj),

${\mathrm{BW}}_{\mathrm{ni}}=\left\{\begin{array}{cc}\frac{{\mathrm{TBH}}_n\&times;{\mathrm{TR}}_i-\mathrm{MiT}\&times;B_i\&times;{\mathrm{FU}}_i\&times;{\mathrm{MSN}}_{\mathrm{ni}}}{\mathrm{MaT}\&times;{\mathrm{FU}}_n\&times;{\mathrm{SN}}_{\mathrm{ni}}}& {\mathrm{MSN}}_{\mathrm{ni}}>0\\ \frac{{\mathrm{TBH}}_n\&times;{\mathrm{TR}}_i}{\mathrm{MaT}\&times;{\mathrm{FU}}_n\&times;{\mathrm{SN}}_{\mathrm{ni}}}& {\mathrm{MSN}}_{\mathrm{ni}}=0\end{array}\right.$

The frequency spectrum BW that in the future time predicting n type communities in this region, user uses _n; Wherein, macro base station that j is identical type communities sorts according to the size of the transmission rate of the busy downlink business carried, and be divided into the number of the equal macro base station group of number, i be 1 to j integer, BW _nifor frequency spectrum, TBH needed for i-th group of macro base station group in the type area that is n _nfor type be n area in transmission rate, the TR of busy downlink business _ifor type be n area in busy downlink business transmission rate in the ratio of transmission rate of busy downlink business, the MiT of i-th group be service bearer ability, the B of above-mentioned micro-base station _ifor type is bandwidth, FU that the micro-base station correspondence comprised in i-th group of macro base station group in the area of n is distributed _ifor the availability of frequency spectrum, MSN that type is the micro-base station comprised in i-th group of macro base station group in the area of n _nifor the number that type is the micro-base station comprised in i-th group of macro base station group in the area of n, MaT is the service bearer ability of macro base station, FU _nfor type be n area in the availability of frequency spectrum, the SN of macro base station _nifor type be n area in the number of macro base station in i-th group of macro base station group.Wherein, because of FU _nthe generation of concept be that be applied to various geographic scenes, it is not good that this can cause there are some spectrum transmissions performances in whole frequency spectrum, causes the situation that overall network performance declines because mobile communication system uses the frequency spectrum of large bandwidth usually.So, introduce this parameter of the availability of frequency spectrum in the present embodiment, be used for characterizing the performance " discount " that above factor causes.

Further, in step 207 ~ 211, can also continue according to the macro base station number under each network formats, micro-number of base stations, and their the service bearer ability of correspondence does Further Division, finally obtain the frequency spectrum used under the m network formats of user in the area that type is n in future time in estimation range.

Spectrum requirement Forecasting Methodology provided by the invention, by the transmission rate of the business of user in the future time of the setting of different areas in acquisition region; And in this region in different areas: the service bearer ability of macro base station number, micro-number of base stations, macro base station and the service bearer ability of micro-base station, the frequency spectrum that in this region of final prediction, in future time, user uses, improve the accuracy of prediction, and then reasonably can configure the frequency spectrum resource of network.

One of ordinary skill in the art will appreciate that: all or part of step realizing above-mentioned each embodiment of the method can have been come by the hardware that program command is relevant.Aforesaid program can be stored in a computer read/write memory medium.This program, when performing, performs the step comprising above-mentioned each embodiment of the method; And aforesaid storage medium comprises: ROM, RAM, magnetic disc or CD etc. various can be program code stored medium.

Fig. 3 is the structural representation of a spectrum requirement prediction unit provided by the invention embodiment.As shown in Figure 3, this spectrum requirement prediction unit can be the network equipment, server in network, or is integrated in the module, chip etc. on this network equipment or server, and can perform the step as the spectrum requirement Forecasting Methodology in Fig. 1 embodiment.This spectrum requirement prediction unit comprises: prediction module 31 and processing module 32, wherein:

Prediction module 31, for the traffic carrying capacity of user in the future time that sets in estimation range;

Processing module 32, for the distribution of traffic situation according to historical time user in region, determines the transmission rate of the business of different areas in this region in the traffic carrying capacity of user in this future time;

This prediction module 31, also for:

According to the allocation proportion of the transmission rate of the business of different areas in this region in the traffic carrying capacity of user in future time, default macro base station total number, macro base station different areas in the zone, predict the described macro base station number of different areas in this region;

According to the macro base station number of different areas in the transmission rate of the business of different areas in this region in the traffic carrying capacity of user in future time, this region, predict micro-number of base stations of different areas in this region;

According to the transmission rate of the business of different areas in this region in the traffic carrying capacity of user in future time, predict the service bearer ability of macro base station in different areas in this region and the service bearer ability of micro-base station;

According to the transmission rate of the business of different areas in this region in the traffic carrying capacity of user in future time, predict different areas in this region of obtaining: the service bearer ability of macro base station number, micro-number of base stations, macro base station and the service bearer ability of micro-base station, predict the frequency spectrum that in this region, in future time, user uses.

Particularly, the realization of the prediction unit of spectrum requirement shown in the present embodiment to future time user in certain region to the forecasting process of required frequency spectrum is:

The traffic carrying capacity of user in the future time of setting in prediction module 31 estimation range, this process can see the corresponding contents of step 101; Processing module 32 is according to the distribution of traffic situation of historical time user in this region, determine that prediction module 31 predicts the transmission rate of the business of different areas in this region in the traffic carrying capacity of user in the future time that obtains, this process can see the corresponding contents of step 102; In the future time that prediction module 31 obtains according to processing module 32 user traffic carrying capacity in the allocation proportion of the transmission rate of the business of different areas, default macro base station total number, this macro base station different areas in this region in this region, predict the described macro base station number of different areas in this region, this process can see the corresponding contents of step 103; Then, prediction module 31 is according to the transmission rate of the business of different areas in this region in the traffic carrying capacity of user in future time, the macro base station number predicting different areas in this region of obtaining, predict micro-number of base stations of different areas in this region, this process can see the corresponding contents of step 104; Then, prediction module 31 is according to the transmission rate of the business of different areas in this region in the traffic carrying capacity of user in future time, predict the service bearer ability of macro base station in different areas in this region and the service bearer ability of micro-base station, this process can see the corresponding contents of step 105; Finally, prediction module 31 is according to the transmission rate of the business of different areas in this region in the traffic carrying capacity of user in above-mentioned future time, predict different areas in this region of obtaining: the service bearer ability of macro base station number, micro-number of base stations, macro base station and the service bearer ability of micro-base station, predict the frequency spectrum that in this region, in future time, user uses, this process can see the corresponding contents of step 106.

Spectrum requirement prediction unit provided by the invention, by the transmission rate of the business of user in the future time of the setting of different areas in acquisition region; And in this region in different areas: the service bearer ability of macro base station number, micro-number of base stations, macro base station and the service bearer ability of micro-base station, the frequency spectrum that in this region of final prediction, in future time, user uses, improve the accuracy of prediction, and then reasonably can configure the frequency spectrum resource of network.

Present invention also offers the structural representation of another embodiment of spectrum requirement prediction unit.This structural representation is a kind of concrete implementation of embodiment as shown in Figure 3, can perform the step of spectrum requirement Forecasting Methodology as shown in Figure 2.This spectrum requirement prediction unit also comprises on the basis of 26S Proteasome Structure and Function as shown in Figure 3:

Processing module 32, for: according to T _v=M _v× 60 × K, by voice services volume M _vbe converted to equivalent data traffic carrying capacity T _v; Wherein, K is conversion coefficient; Equivalent data traffic carrying capacity and data business volume sum are defined as the traffic carrying capacity of user in the future time that sets in region;

Processing module 32, also for basis $\mathrm{TBH}=\frac{(T_d+T_V)\&times;\mathrm{DLR}\&times;\mathrm{BHR}}{t}$

Obtain the transmission rate TBH of the busy downlink business in above-mentioned traffic carrying capacity; Wherein, T _dfor above-mentioned data business volume, T _vfor the duration that above-mentioned equivalent data traffic carrying capacity, the DLR ratio of downlink traffic in total traffic that be above-mentioned historical time user, the BHR ratio of busy-hour traffic at total traffic that be this historical time user, t are busy in this future time;

According to TBH _m=TBH × P _m× U _m

Obtain the transmission rate TBH of busy downlink business in future time _m; Wherein, the network formats, the P that adopt for this region of the TBH transmission rate that is busy downlink business described under all standards, m _mfor accounting, the U of user under this m network formats _mfor under this m network formats, each user uses the normalized parameter of traffic carrying capacity;

According to TBH _m,n=TBH _m× P _n× U _n

Obtain the transmission rate TBH of downlink business corresponding in different areas in this region in this future time _m,n; Wherein, n is the type in area, TBH _mfor transmission rate, the P of the busy downlink business under m network formats _nfor accounting, the U of user in n type communities under this m network formats _nfor the type under this m network formats be n area in each user use the normalized parameter of described traffic carrying capacity;

Prediction module 31, for the size of the macro base station of identical type communities according to the transmission rate of the business carried being sorted, and is divided into the equal j group macro base station group of number;

According to ${\mathrm{MSN}}_n=\underset{i=1}{\overset{j}{\mathrm{\&Sigma;}}}{\mathrm{MSN}}_{\mathrm{ni}},$

${\mathrm{MSN}}_{\mathrm{ni}}=\left\{\begin{array}{cc}{\mathrm{SN}}_{\mathrm{ni}}\&times;\mathrm{MSPS},& \frac{{\mathrm{TBH}}_n\&times;{\mathrm{TR}}_i}{{\mathrm{SN}}_{\mathrm{ni}}/{\mathrm{SD}}_n}\&GreaterEqual;G_{\mathrm{MS}}\\ 0,& \frac{{\mathrm{TBH}}_n\&times;{\mathrm{TR}}_i}{{\mathrm{SN}}_{\mathrm{ni}}/{\mathrm{SD}}_n}<G_{\mathrm{MS}}\end{array}\right.$

Type of prediction is micro-number of base stations MSN in the area of n _n; Wherein, i is integer, the MSN of 1 to j _nifor type is the micro-number of base stations, the SN that comprise in i-th group of macro base station group in the area of n _nibe the micro-number of base stations, the TBH that comprise in the macrocell of each macro base station in the area of n for type be the macro base station number, the MSPS that comprise in i-th group of macro base station group in the area of n is type _nfor the transmission rate that type is busy downlink business corresponding in the area of n, TR _ifor ratio, SD that type is in the transmission rate of transmission rate busy downlink business of correspondence in the area that all types is n of the busy downlink business of i-th group of macro base station carrying in the area of n _nfor the site density that type is the macro base station in the area of n, G _mSfor setting up the transmission rate density threshold value of the busy downlink business needed for micro-base station at each macro base station;

This prediction module 31, also for:

According to MaT=FE _ma× ASN _ma× L,

MiT=FE _Mi×ASN _Mi

Calculate n type communities respectively: the service bearer ability MaT of macro base station and the service bearer ability MiT of micro-base station; Wherein, Ma is the attribute-bit of macro base station, Mi is attribute-bit, the FE of micro-base station _mafor spectrum efficiency, the ASN of macro base station _mafor average sector number, the FE of macro base station _mifor spectrum efficiency, the ASN of micro-base station _mifor the transmission rate of the average sector number of micro-base station, the L busy downlink business that is n type communities in this region network system energy bearer service transmission rate maximum capacity value in ratio;

According to BW _n=MAX (BW _n1, BW _n2..., BW _nj),

${\mathrm{BW}}_{\mathrm{ni}}=\left\{\begin{array}{cc}\frac{{\mathrm{TBH}}_n\&times;{\mathrm{TR}}_i-\mathrm{MiT}\&times;B_i\&times;{\mathrm{FU}}_i\&times;{\mathrm{MSN}}_{\mathrm{ni}}}{\mathrm{MaT}\&times;{\mathrm{FU}}_n\&times;{\mathrm{SN}}_{\mathrm{ni}}}& {\mathrm{MSN}}_{\mathrm{ni}}>0\\ \frac{{\mathrm{TBH}}_n\&times;{\mathrm{TR}}_i}{\mathrm{MaT}\&times;{\mathrm{FU}}_n\&times;{\mathrm{SN}}_{\mathrm{ni}}}& {\mathrm{MSN}}_{\mathrm{ni}}=0\end{array}\right.$

The frequency spectrum BW that in the future time predicting n type communities in this region, user uses _n; Wherein, macro base station that j is identical type communities sorts according to the size of the transmission rate of the busy downlink business carried, and be divided into the number of the equal macro base station group of number, i be 1 to j integer, BW _nifor frequency spectrum, TBH needed for i-th group of macro base station group in the type area that is n _nfor type be n area in transmission rate, the TR of busy downlink business _ifor type be n area in busy downlink business transmission rate in the ratio of transmission rate of busy downlink business, the MiT of i-th group be service bearer ability, the B of micro-base station _ibe bandwidth, the FU that the micro-base station correspondence comprised in i-th group of macro base station group is distributed _ibe the availability of frequency spectrum, the MSN of the micro-base station comprised in i-th group of macro base station group _nifor the number that type is the micro-base station comprised in i-th group of macro base station group in the area of n, MaT is the service bearer ability of macro base station, FU _nfor type be n area in the availability of frequency spectrum, the SN of macro base station _nifor type be n area in the number of macro base station in i-th group of macro base station group.

Particularly, the prediction unit of spectrum requirement shown in the present embodiment realizes as follows to the forecasting process of future time user to required frequency spectrum in certain region.

The traffic carrying capacity of user in the future time of setting in prediction module 31 estimation range; This process can see the corresponding contents of step 201.

For voice services volume in above-mentioned traffic carrying capacity, processing module 32 is according to T _v=M _v× 60 × K(1), by this voice services volume M _vbe converted to equivalent data traffic carrying capacity T _v, then, equivalent data traffic carrying capacity and data business volume sum are defined as the traffic carrying capacity of user in the future time that sets in above-mentioned zone, to predicting that the voice services volume that obtains and data business volume unify process.

Processing module 32, according to the distribution of traffic situation of historical time user in this region, is determined that prediction module 31 predicts the transmission rate of the business of different areas in this region in the traffic carrying capacity of user in the future time that obtains, is specifically comprised:

According to $\mathrm{TBH}=\frac{(T_d+T_V)\&times;\mathrm{DLR}\&times;\mathrm{BHR}}{t}---\left(2\right)$ Obtain the transmission rate TBH of the busy downlink business in traffic carrying capacity; According to TBH _m=TBH × P _m× U _m(3) the transmission rate TBH of busy downlink business in future time is obtained _m; According to TBH _m,n=TBH _m× P _n× U _n(4) the transmission rate TBH of downlink business corresponding in different areas in this region in future time is obtained _m,n, this implementation can see the corresponding contents of step 204 ~ 206.

In the future time that prediction module 31 obtains according to processing module 32 user traffic carrying capacity in the allocation proportion of the transmission rate of the business of different areas, default macro base station total number, this macro base station different areas in this region in this region, predict the described macro base station number of different areas in this region, this process can see the corresponding contents of step 103.

Prediction module 31 is according to the transmission rate of the business of different areas in this region in the traffic carrying capacity of user in future time, the macro base station number predicting different areas in this region of obtaining, predict micro-number of base stations of different areas in this region, specifically comprise:

The size of the macro base station of identical type communities according to the transmission rate of the business carried is sorted, and is divided into the equal j group macro base station group of number;

According to ${\mathrm{MSN}}_n=\underset{i=1}{\overset{j}{\mathrm{\&Sigma;}}}{\mathrm{MSN}}_{\mathrm{ni}}---\left(5\right)$

${\mathrm{MSN}}_{\mathrm{ni}}=\left\{\begin{array}{cc}{\mathrm{SN}}_{\mathrm{ni}}\&times;\mathrm{MSPS},& \frac{{\mathrm{TBH}}_n\&times;{\mathrm{TR}}_i}{{\mathrm{SN}}_{\mathrm{ni}}/{\mathrm{SD}}_n}\&GreaterEqual;G_{\mathrm{MS}}\\ 0,& \frac{{\mathrm{TBH}}_n\&times;{\mathrm{TR}}_i}{{\mathrm{SN}}_{\mathrm{ni}}/{\mathrm{SD}}_n}<G_{\mathrm{MS}}\end{array}\right.---\left(6\right),$

Type of prediction is micro-number of base stations MSN in the area of n _n; This process specifically can see the corresponding contents of step 208 ~ 209.

Then, prediction module 31, according to the transmission rate of the business of different areas in this region in the traffic carrying capacity of user in future time, is predicted the service bearer ability of macro base station in different areas in this region and the service bearer ability of micro-base station, is specifically comprised:

According to MaT=FE _ma× ASN _ma× L(7)

MiT=FE _Mi×ASN _Mi（8），

Calculate n type communities respectively: the service bearer ability MaT of macro base station and the service bearer ability MiT of micro-base station, this process can see the corresponding contents of step 210.

Finally, prediction module 31 is according to the transmission rate of the business of different areas in this region in the traffic carrying capacity of user in above-mentioned future time, predict different areas in this region of obtaining: the service bearer ability of macro base station number, micro-number of base stations, macro base station and the service bearer ability of micro-base station, predict the frequency spectrum that in this region, in future time, user uses, specifically comprise:

According to BW _n=MAX (BW _n1, BW _n2..., BW _nj),

${\mathrm{BW}}_{\mathrm{ni}}=\left\{\begin{array}{cc}\frac{{\mathrm{TBH}}_n\&times;{\mathrm{TR}}_i-\mathrm{MiT}\&times;B_i\&times;{\mathrm{FU}}_i\&times;{\mathrm{MSN}}_{\mathrm{ni}}}{\mathrm{MaT}\&times;{\mathrm{FU}}_n\&times;{\mathrm{SN}}_{\mathrm{ni}}}& {\mathrm{MSN}}_{\mathrm{ni}}>0\\ \frac{{\mathrm{TBH}}_n\&times;{\mathrm{TR}}_i}{\mathrm{MaT}\&times;{\mathrm{FU}}_n\&times;{\mathrm{SN}}_{\mathrm{ni}}}& {\mathrm{MSN}}_{\mathrm{ni}}=0\end{array}\right.$

The frequency spectrum BW that in the future time predicting n type communities in this region, user uses _n, this process can see the corresponding contents of step 211.

Further, the present embodiment shown device is in the process realizing spectrum prediction, can also according to the macro base station number under each network formats, micro-number of base stations, and their the service bearer ability of correspondence does Further Division, finally obtain the frequency spectrum used under the m network formats of user in the area that type is n in future time in estimation range.

Spectrum requirement prediction unit provided by the invention, by the transmission rate of the business of user in the future time of the setting of different areas in acquisition region; And in this region in different areas: the service bearer ability of macro base station number, micro-number of base stations, macro base station and the service bearer ability of micro-base station, the frequency spectrum that in this region of final prediction, in future time, user uses, improve the accuracy of prediction, and then reasonably can configure the frequency spectrum resource of network.

Last it is noted that above each embodiment is only in order to illustrate technical scheme of the present invention, be not intended to limit; Although with reference to foregoing embodiments to invention has been detailed description, those of ordinary skill in the art is to be understood that: it still can be modified to the technical scheme described in foregoing embodiments, or carries out equivalent replacement to wherein some or all of technical characteristic; And these amendments or replacement, do not make the essence of appropriate technical solution depart from the scope of various embodiments of the present invention technical scheme.

Claims (12)

1. a spectrum requirement Forecasting Methodology, is characterized in that, comprising:

The traffic carrying capacity of user in the future time of setting in estimation range;

According to the distribution of traffic situation of historical time user in described region, the transmission rate of the business of different areas in region described in the traffic carrying capacity determining user in described future time;

According to the allocation proportion of the transmission rate of the business of different areas in region described in the traffic carrying capacity of user in described future time, default macro base station total number, described macro base station different areas in this region, predict the described macro base station number of different areas in described region;

According to the described macro base station number of different areas in the transmission rate of the business of different areas in region described in the traffic carrying capacity of user in described future time, described region, predict micro-number of base stations of different areas in described region;

According to the transmission rate of the business of different areas in region described in the traffic carrying capacity of user in described future time, predict the service bearer ability of described macro base station in different areas in described region and the service bearer ability of described micro-base station;

According to the transmission rate of the business of different areas in region described in the traffic carrying capacity of user in described future time, predict different areas in the described region that obtains: the service bearer ability of described macro base station number, described micro-number of base stations, described macro base station and the service bearer ability of described micro-base station, predict the frequency spectrum that in described region, in future time, user uses.

2. method according to claim 1, is characterized in that, in the future time of setting in described region, the traffic carrying capacity of user comprises voice services volume and data business volume, and in the future time of setting in described estimation range, the traffic carrying capacity of user comprises:

According to T _v=M _v× 60 × K, by described voice services volume M _vbe converted to equivalent data traffic carrying capacity T _v; Wherein, described K is conversion coefficient;

Described equivalent data traffic carrying capacity and described data business volume sum are defined as the traffic carrying capacity of user in the future time that sets in described region.

3. method according to claim 2, is characterized in that, the described traffic carrying capacity according to user in the described regional historical time, and the transmission rate of the business of different areas in region described in the traffic carrying capacity determining user in described future time, comprising:

According to $TBH=\frac{(T_d+T_v)\&times;DLR\&times;BHR}{t}$

Obtain the transmission rate TBH of the busy downlink business in described traffic carrying capacity; Wherein, T _dfor described data business volume, T _vfor described equivalent data traffic carrying capacity, DLR be the ratio of downlink traffic in total traffic of described historical time user, the BHR ratio of busy-hour traffic at total traffic that be described historical time user, the t duration that is busy in described future time;

According to TBH _m=TBH × P _m× U _m

Obtain the transmission rate TBH of busy downlink business in described future time _m; Wherein, the network formats, the P that adopt for described region of the TBH transmission rate that is busy downlink business described under all standards, m _mfor accounting, the U of user under described m network formats _mfor under described m network formats, each user uses the normalized parameter of traffic carrying capacity;

According to TBH _m,n=TBH _m× P _n× U _n

Obtain the transmission rate TBH of downlink business corresponding in different areas in described region in described future time _m,n; Wherein, n is the type in area, TBH _mfor transmission rate, the P of the busy downlink business under described m network formats _nfor accounting, the U of user in n type communities described under described m network formats _nfor the described type under described m network formats be n area in each user use the normalized parameter of described traffic carrying capacity.

4. method according to claim 3, it is characterized in that, according to the described macro base station number of different areas in the transmission rate of the business of different areas in region described in the traffic carrying capacity of user in described future time, described region, predict micro-number of base stations of different areas in described region, comprising:

The size of the macro base station of identical type communities according to the transmission rate of the business carried is sorted, and is divided into the equal j group macro base station group of number;

According to ${\mathrm{MSN}}_n=\underset{i=1}{\overset{j}{\&Sigma;}}{\mathrm{MSN}}_{ni},$

${\mathrm{MSN}}_{ni}=\left\{\begin{array}{cc}{\mathrm{SN}}_{ni}\&times;MSPS,& \frac{{\mathrm{TBH}}_n\&times;{\mathrm{TR}}_i}{{\mathrm{SN}}_{ni}/{\mathrm{SD}}_n}\&GreaterEqual;G_{MS}\\ 0,& \frac{{\mathrm{TBH}}_n\&times;{\mathrm{TR}}_i}{{\mathrm{SN}}_{ni}/{\mathrm{SD}}_n}<G_{MS}\end{array}\right.$

Type of prediction is micro-number of base stations MSN in the area of n _n; Wherein, i is integer, the MSN of 1 to j _nifor type is the micro-number of base stations, the SN that comprise in i-th group of macro base station group in the area of n _nibe the micro-number of base stations, the TBH that comprise in the macrocell of each macro base station in the area of n for type be the macro base station number, the MSPS that comprise in i-th group of macro base station group in the area of n is type _nfor the transmission rate that type is busy downlink business corresponding in the area of n, described TR _ifor ratio, SD that type is in the transmission rate of transmission rate busy downlink business of correspondence in the area that all described types are n of the described busy downlink business of i-th group of macro base station carrying in the area of n _nfor the site density that type is the macro base station in the area of n, G _mSfor setting up the transmission rate density threshold value of the busy downlink business needed for micro-base station at each macro base station.

5. method according to claim 4, it is characterized in that, the described transmission rate according to the business of different areas in region described in the traffic carrying capacity of user in described future time, predict the service bearer ability of described macro base station in different areas in described region and the service bearer ability of described micro-base station, comprising:

According to MaT=FE _ma× ASN _ma× L,

MiT＝FE _Mi×ASN _Mi

Calculate described n type communities respectively: the service bearer ability MaT of described macro base station and the service bearer ability MiT of described micro-base station; Wherein, Ma is the attribute-bit of described macro base station, Mi is attribute-bit, the FE of described micro-base station _mafor spectrum efficiency, the ASN of macro base station _mafor average sector number, the FE of macro base station _mifor spectrum efficiency, the ASN of micro-base station _mifor the average sector number of micro-base station, the L transmission rate that is the busy downlink business of described n type communities in this region network system energy bearer service transmission rate maximum capacity value in ratio.

6. method according to claim 5, it is characterized in that, the described transmission rate according to the business of different areas in region described in the traffic carrying capacity of user in described future time, predict different areas in the described region that obtains: the service bearer ability of described macro base station number, described micro-number of base stations, described macro base station and the service bearer ability of described micro-base station, predict the frequency spectrum that in described region, in future time, user uses, comprising:

According to BW _n=MAX (BW _n1, BW _n2..., BW _nj),

${\mathrm{BW}}_{ni}=\left\{\begin{array}{cc}\frac{{\mathrm{TBH}}_n\&times;{\mathrm{TR}}_i-MiT\&times;B_i\&times;{\mathrm{FU}}_i\&times;{\mathrm{MSN}}_{ni}}{MaT\&times;{\mathrm{FU}}_n\&times;{\mathrm{SN}}_{ni}}& {\mathrm{MSN}}_{ni}>0\\ \frac{{\mathrm{TBH}}_n\&times;{\mathrm{TR}}_i}{MaT\&times;{\mathrm{FU}}_n\&times;{\mathrm{SN}}_{ni}}& {\mathrm{MSN}}_{ni}=0\end{array}\right.$

The frequency spectrum BW that in the future time predicting described n type communities in described region, user uses _n; Wherein, macro base station that j is identical type communities sorts according to the size of the transmission rate of the busy downlink business carried, and be divided into the number of the equal macro base station group of number, i be 1 to j integer, BW _nifor frequency spectrum, TBH needed for i-th group of macro base station group in the type area that is n _nfor type be n area in transmission rate, the TR of busy downlink business _ifor type be n area in busy downlink business transmission rate in the ratio of transmission rate of busy downlink business, the MiT of i-th group be service bearer ability, the B of micro-base station _ibe bandwidth, the FU that the micro-base station correspondence comprised in i-th group of macro base station group is distributed _ibe the availability of frequency spectrum, the MSN of the micro-base station comprised in i-th group of macro base station group _nifor the number that type is the micro-base station comprised in i-th group of macro base station group in the area of n, MaT is the service bearer ability of macro base station, FU _nfor type be n area in the availability of frequency spectrum, the SN of macro base station _nifor type be n area in the number of macro base station in i-th group of macro base station group.

7. a spectrum requirement prediction unit, is characterized in that, comprising: prediction module and processing module;

Described prediction module, for the traffic carrying capacity of user in the future time that sets in estimation range;

Described processing module, for the distribution of traffic situation according to historical time user in described region, the transmission rate of the business of different areas in region described in the traffic carrying capacity determining user in described future time;

Described prediction module, also for:

According to the allocation proportion of the transmission rate of the business of different areas in region described in the traffic carrying capacity of user in described future time, default macro base station total number, described macro base station different areas in this region, predict the described macro base station number of different areas in described region;

According to the described macro base station number of different areas in the transmission rate of the business of different areas in region described in the traffic carrying capacity of user in described future time, described region, predict micro-number of base stations of different areas in described region;

According to the transmission rate of the business of different areas in region described in the traffic carrying capacity of user in described future time, predict the service bearer ability of described macro base station in different areas in described region and the service bearer ability of described micro-base station;

According to the transmission rate of the business of different areas in region described in the traffic carrying capacity of user in described future time, predict different areas in the described region that obtains: the service bearer ability of described macro base station number, described micro-number of base stations, described macro base station and the service bearer ability of described micro-base station, predict the frequency spectrum that in described region, in future time, user uses.

8. device according to claim 7, is characterized in that, in the future time of setting in described region, the traffic carrying capacity of user comprises voice services volume and data business volume;

Described prediction module specifically for:

According to T _v=M _v× 60 × K, by described voice services volume M _vbe converted to equivalent data traffic carrying capacity T _v; Wherein, described K is conversion coefficient;

Described equivalent data traffic carrying capacity and described data business volume sum are defined as the traffic carrying capacity of user in the future time that sets in described region.

9. device according to claim 8, it is characterized in that, described processing module is used for,

According to $TBH=\frac{(T_d+T_v)\&times;DLR\&times;BHR}{t}$

Obtain the transmission rate TBH of the busy downlink business in described traffic carrying capacity; Wherein, T _dfor described data business volume, T _vfor described equivalent data traffic carrying capacity, DLR be the ratio of downlink traffic in total traffic of described historical time user, the BHR ratio of busy-hour traffic at total traffic that be described historical time user, the t duration that is busy in described future time;

According to TBH _m=TBH × P _m× U _m

Obtain the transmission rate TBH of busy downlink business in described future time _m; Wherein, the network formats, the P that adopt for described region of the TBH transmission rate that is busy downlink business described under all standards, m _mfor accounting, the U of user under described m network formats _mfor under described m network formats, each user uses the normalized parameter of traffic carrying capacity;

According to TBH _m,n=TBH _m× P _n× U _n

Obtain the transmission rate TBH of downlink business corresponding in different areas in described region in described future time _m,n; Wherein, n is the type in area, TBH _mfor transmission rate, the P of the busy downlink business under described m network formats _nfor accounting, the U of user in n type communities described under described m network formats _nfor the described type under described m network formats be n area in each user use the normalized parameter of described traffic carrying capacity.

10. device according to claim 9, it is characterized in that, described prediction module is used for,

The size of the macro base station of identical type communities according to the transmission rate of the business carried is sorted, and is divided into the equal j group macro base station group of number;

According to ${\mathrm{MSN}}_n=\underset{i=1}{\overset{j}{\&Sigma;}}{\mathrm{MSN}}_{ni},$

${\mathrm{MSN}}_{ni}=\left\{\begin{array}{cc}{\mathrm{SN}}_{ni}\&times;MSPS,& \frac{{\mathrm{TBH}}_n\&times;{\mathrm{TR}}_i}{{\mathrm{SN}}_{ni}/{\mathrm{SD}}_n}\&GreaterEqual;G_{MS}\\ 0,& \frac{{\mathrm{TBH}}_n\&times;{\mathrm{TR}}_i}{{\mathrm{SN}}_{ni}/{\mathrm{SD}}_n}<G_{MS}\end{array}\right.$

Type of prediction is micro-number of base stations MSN in the area of n _n; Wherein, i is integer, the MSN of 1 to j _nifor type is the micro-number of base stations, the SN that comprise in i-th group of macro base station group in the area of n _nibe the micro-number of base stations, the TBH that comprise in the macrocell of each macro base station in the area of n for type be the macro base station number, the MSPS that comprise in i-th group of macro base station group in the area of n is type _nfor the transmission rate that type is busy downlink business corresponding in the area of n, described TR _ifor ratio, SD that type is in the transmission rate of transmission rate busy downlink business of correspondence in the area that all described types are n of the described busy downlink business of i-th group of macro base station carrying in the area of n _nfor the site density that type is the macro base station in the area of n, G _mSfor setting up the transmission rate density threshold value of the busy downlink business needed for micro-base station at each macro base station.

11. devices according to claim 10, it is characterized in that, described prediction module is used for,

According to MaT=FE _ma× ASN _ma× L,

MiT＝FE _Mi×ASN _Mi

Calculate described n type communities respectively: the service bearer ability MaT of described macro base station and the service bearer ability MiT of described micro-base station; Wherein, Ma is the attribute-bit of described macro base station, Mi is attribute-bit, the FE of described micro-base station _mafor spectrum efficiency, the ASN of macro base station _mafor average sector number, the FE of macro base station _mifor spectrum efficiency, the ASN of micro-base station _mifor the average sector number of micro-base station, the L transmission rate that is the busy downlink business of described n type communities in this region network system energy bearer service transmission rate maximum capacity value in ratio.

12., according to device described in claim 11, is characterized in that, described prediction module is used for,

According to BW _n=MAX (BW _n1, BW _n2..., BW _nj),

${\mathrm{BW}}_{ni}=\left\{\begin{array}{cc}\frac{{\mathrm{TBH}}_n\&times;{\mathrm{TR}}_i-MiT\&times;B_i\&times;{\mathrm{FU}}_i\&times;{\mathrm{MSN}}_{ni}}{MaT\&times;{\mathrm{FU}}_n\&times;{\mathrm{SN}}_{ni}}& {\mathrm{MSN}}_{ni}>0\\ \frac{{\mathrm{TBH}}_n\&times;{\mathrm{TR}}_i}{MaT\&times;{\mathrm{FU}}_n\&times;{\mathrm{SN}}_{ni}}& {\mathrm{MSN}}_{ni}=0\end{array}\right.$

The frequency spectrum BW that in the future time predicting described n type communities in described region, user uses _n; Wherein, macro base station that j is identical type communities sorts according to the size of the transmission rate of the busy downlink business carried, and be divided into the number of the equal macro base station group of number, i be 1 to j integer, BW _nifor frequency spectrum, TBH needed for i-th group of macro base station group in the type area that is n _nfor type be n area in transmission rate, the TR of busy downlink business _ifor type be n area in busy downlink business transmission rate in the ratio of transmission rate of busy downlink business, the MiT of i-th group be service bearer ability, the B of micro-base station _ibe bandwidth, the FU that the micro-base station correspondence comprised in i-th group of macro base station group is distributed _ibe the availability of frequency spectrum, the MSN of the micro-base station comprised in i-th group of macro base station group _nifor the number that type is the micro-base station comprised in i-th group of macro base station group in the area of n, MaT is the service bearer ability of macro base station, FU _nfor type be n area in the availability of frequency spectrum, the SN of macro base station _nifor type be n area in the number of macro base station in i-th group of macro base station group.