CN103338470B - Spectrum requirement Forecasting Methodology and device - Google Patents

Abstract

The present invention provides a spectrum demand prediction method and device. The method includes: predicting the traffic volume of users in the future time set in the area, and the transmission rate of services in different types of areas in the area in the traffic volume; according to the business volume Transmission rate, the total number of preset macro base stations, the distribution ratio of macro base stations in different types of areas in the area, and the prediction of different types of areas in the area: the number of macro base stations, the number of micro base stations and their corresponding service carrying capacity; according to the future The transmission rate of business in different types of areas in the area in the user's business volume within a certain period of time, the predicted number of different types of areas in the area: the number of macro base stations, the number of micro base stations, and their corresponding business carrying capacity, predict the area Spectrum used by users in the future. The embodiment of the present invention effectively solves the technical problem in the prior art that the accuracy of predicting the spectrum demand of the network is not high, and thus the spectrum resources of the network cannot be reasonably configured.

Description

Spectrum Demand Forecasting Method and Device

technical field

The present invention relates to the field of communication technology, in particular to a spectrum demand prediction method and device.

Background technique

With the rapid development of global mobile broadband communications, the data traffic of mobile communication users has increased dramatically, and frequency is the basic resource for the development of mobile communications, and its demand has also increased rapidly. In order to reasonably plan the use of spectrum resources of the country or telecom operators, the national radio administration or telecom operators need to predict the demand and usage of spectrum used in mobile communications in the future.

In the prior art, a typical mobile communication scenario is adopted, for example, in a city, the spectrum demand of the entire mobile communication network is predicted by monitoring the corresponding relationship between different traffic volumes and the required spectrum. However, the accuracy of the prediction results obtained by this prediction method is not high, and the spectrum resources of the network cannot be reasonably allocated.

Contents of the invention

The present invention provides a method and device for predicting spectrum demand, which are used to solve the technical problem that the accuracy of forecasting network spectrum demand in the prior art is not high, and thus the network spectrum resources cannot be rationally configured.

On the one hand, an embodiment of the present invention provides a spectrum demand prediction method, including:

Predict the traffic volume of users within the set future time in the area;

According to the traffic distribution of users in the historical time in the area, determine the transmission rate of services in different types of areas in the area in the user traffic in the future time;

According to the traffic transmission rates of different types of areas in the area in the user's traffic in the future time, the total number of preset macro base stations, and the allocation ratio of the macro base stations to different types of areas in the area, predict the The number of said macro base stations in different types of areas in said area;

Predict the number of micro base stations in different types of areas in the area according to the transmission rate of services in different types of areas in the area in the user's traffic in the future time and the number of macro base stations in different types of areas in the area ;

Predict the service carrying capacity of the macro base station and the service carrying capacity of the micro base station in different types of areas in the area according to the transmission rates of services in different types of areas in the area in the user's traffic in the future time ;

According to the transmission rate of services in different types of areas in the area in the user traffic in the future time, the predicted number of different types of areas in the area: the number of macro base stations, the number of micro base stations, the The service carrying capacity of the macro base station and the service carrying capacity of the micro base station are used to predict the frequency spectrum used by users in the area in the future.

On the other hand, an embodiment of the present invention provides a spectrum demand prediction device, including: a prediction module and a processing module;

The prediction module is used to predict the traffic volume of users in the future time set in the area;

The processing module is configured to determine the transmission rate of services in different types of areas in the area in the user's traffic in the future time according to the traffic distribution of users in the area in the past;

The prediction module is also used for:

According to the traffic transmission rates of different types of areas in the area in the user's traffic in the future time, the total number of preset macro base stations, and the allocation ratio of the macro base stations to different types of areas in the area, predict the The number of said macro base stations in different types of areas in said area;

Predict the number of micro base stations in different types of areas in the area according to the transmission rate of services in different types of areas in the area in the user's traffic in the future time and the number of macro base stations in different types of areas in the area ;

Predict the service carrying capacity of the macro base station and the service carrying capacity of the micro base station in different types of areas in the area according to the transmission rates of services in different types of areas in the area in the user's traffic in the future time ;

According to the transmission rate of services in different types of areas in the area in the user traffic in the future time, the predicted number of different types of areas in the area: the number of macro base stations, the number of micro base stations, the The service carrying capacity of the macro base station and the service carrying capacity of the micro base station are used to predict the frequency spectrum used by users in the area in the future.

The spectrum demand prediction method and device provided by the present invention obtain the transmission rate of the user's business in the future time set in different types of areas in the area; and in different types of areas in the area: the number of macro base stations, the number of micro base stations, the number of macro base stations The service carrying capacity of the base station and the service carrying capacity of the micro base station can finally predict the spectrum used by users in the future in the area, which improves the accuracy of the prediction, and then can reasonably configure the spectrum resources of the network.

Description of drawings

Fig. 1 is a flow chart of an embodiment of the spectrum demand prediction method provided by the present invention;

FIG. 2 is a flow chart of another embodiment of the spectrum demand prediction method provided by the present invention;

Fig. 3 is a schematic structural diagram of an embodiment of an apparatus for predicting spectrum demand provided by the present invention.

detailed description

The techniques described in this paper can be used for spectrum demand prediction of various communication networks, such as current 2G, 3G communication systems and next-generation communication systems, such as Global System for Mobile Communications (GSM, Global System for Mobile communications), Code Division Multiple Access (CDMA, CodeDivisionMultipleAccess) system , Time Division Multiple Access (TDMA, TimeDivisionMultipleAccess) system, Wideband Code Division Multiple Access (WCDMA, WidebandCodeDivisionMultipleAccessWireless), Frequency Division Multiple Access (FDMA, FrequencyDivisionMultipleAddressing) system, Orthogonal Frequency Division Multiple Access (OFDMA, OrthogonalFrequency-DivisionMultipleAccess) system, single carrier FDMA (SC-FDMA) system, General Packet Radio Service (GPRS, General Packet Radio Service) system, Long Term Evolution (LTE, Long Term Evolution) system, and other such communication systems.

Fig. 1 is a flowchart of an embodiment of a method for forecasting spectrum demand provided by the present invention. As shown in FIG. 1 , the execution subject of the following steps may be a network device, a server in the network, or a module, chip, etc. integrated on the network device or server. As shown in Figure 1, the spectrum demand forecasting method specifically includes:

S101. Forecasting the traffic volume of users within the set future time in the area;

Wherein, the predicted area may include one or more areas with different population densities, such as cities, suburbs, and rural areas. Forecasting the traffic volume generated by mobile communication services used by users in the future period set in this area, such as one month, one year, or ten years, can be counted through the historical traffic volume generated in this area to analyze its changing trend; As well as the population change trend in the region, the impact of the population change trend on the new business volume and other aspects are comprehensively predicted. After comprehensive consideration of the above factors, the number of users in the future time set in the area and the average business volume used by each user are determined, then the ratio of the number of users to the average business volume used by each user can be determined. The product is used as the predicted value of the user's business volume in the future time set in the area.

S102, according to the traffic distribution of the user in the area in the historical time, determine the transmission rate of the business of different types of areas in the area in the user traffic in the above-mentioned future time;

The area types in the predicted area may include the above-mentioned urban, suburban, rural and other areas. Through the analysis of the traffic distribution of historical users in this area, it can be known that in urban areas with a large population density, the deployment of mobile communication network equipment is relatively complete, and the traffic used by users in this area is relatively large; Its population density is relatively small, and the deployment of mobile communication network equipment is relatively rare, and the business volume generated in this area is relatively small; the traffic volume generated in rural areas is even less. According to the traffic distribution of users in the above historical time, the traffic of users in the future predicted in step 101 can be allocated according to the traffic distribution of different types of regions in this area, and the traffic of different types of regions in this area can be obtained Then, according to the time when these traffics are generated, the transmission rate of the business of different types of areas in the area in the user's traffic in the future time is obtained.

S103. According to the transmission rate of services in different types of areas in the area in the user's traffic in the future, the total number of preset macro base stations, and the distribution ratio of the macro base stations in different types of areas in the area, predict the different types of areas in the area. The number of macro base stations in a type area;

Wherein, the preset total number of macro base stations may be based on the development of the existing network in the predicted area, such as performing statistical analysis on the specific number of base station deployments in each region announced by operators each year, and predicting the number of macro base stations in the future. The number of macro base stations in the predicted area. According to the preset total number of macro base stations and the distribution ratios of the macro base stations in different types of areas in the area, the number of macro base stations allocated to different types of areas can be roughly obtained. However, in actual application scenarios, even between regions of the same type, due to their different areas and different numbers of users in the regions, there are large differences in the service volumes used by these users. In this case, further judgment should be made according to the transmission rate of services in different types of areas in the area in the user's traffic in the future. The transmission rate of the service directly reflects the total number of macro base stations required to carry it. Therefore, after determining the approximate number of macro base stations allocated to different types of areas through the preset total number of macro base stations and the allocation ratio of the macro base station in different types of areas in the area, according to the traffic volume of users in the future, the The transmission rate of services in different types of areas in the area adjusts the allocated number of macro base stations in each area to make the allocation more reasonable. For example, under normal circumstances, the business volume of urban users is larger than that of suburban and rural areas. Therefore, the allocation ratio of macro base stations in urban areas is higher than that of the latter two. In some special scenarios, such as a certain urban area is smaller than other urban areas, the overall number of users is small, and the transmission rate of user services is relatively small. Therefore, the allocation of macro base stations can be appropriately reduced on the basis of the original allocation ratio. number, so that the distribution of macro base stations among various types of regions is more reasonable.

S104. Predict the number of micro base stations in different types of areas in the area according to the transmission rates of services in different types of areas in the area in the user's traffic in the future and the number of macro base stations in different types of areas in the area;

Considering the uneven distribution of traffic in space, in different types of regions, there are "hot spot" areas that absorb most of the traffic, and "cold spot" areas that only carry a small amount of traffic. For a "hot spot" area, a fixed number of micro base stations may be set in the macro cell corresponding to each macro base station in the area. By multiplexing the frequency spectrum used by the macro base station with fixed bandwidth, the micro base station offloads the excessively high traffic density, thereby reducing the transmission rate of the service carried by the macro base station.

In this embodiment, it is judged whether the type of area is a "hot spot" area according to the transmission rate of the traffic of the user in the area in different types of areas in the future. If the transmission rate of services carried by this type of area is very high, approaching or exceeding the maximum service carrying capacity of the macro base station in this area, then this area is considered to be a "hot spot" area. According to the number of macro base stations in the hotspot area and the fixed number of micro base stations configured for each macro base station, the number of micro base stations in different types of areas in the area can be obtained.

S105. Predict the service carrying capacity of the macro base station and the service carrying capacity of the micro base station in different types of areas in the area according to the transmission rates of services in different types of areas in the area in the user's traffic in the future;

Wherein, the service carrying capacity refers to the maximum transmission rate that each macro base station or micro base station can provide for carrying services by using each MHz bandwidth, and the service carrying capacity is different under different network standards. The service carrying capacity is related to: the spectrum efficiency of the macro base station or the micro base station, the number of sectors included, the transmission rate of the service carried and the maximum carrying load. Among them, the spectrum efficiency is the spectrum efficiency value obtained according to the different network standard technologies adopted in the mobile communication network. This value will determine the service carrying capacity that operators can provide by using the spectrum with the same bandwidth. According to the transmission rate of services in different types of areas in the area in the user's traffic in the future, and each macro base station or micro base station itself: spectral efficiency, the number of sectors included, and the maximum bearer load, it can be predicted that in this area The service carrying capacity of the macro base station and the service carrying capacity of the micro base station in different types of areas.

S106, according to the transmission rate of services in different types of areas in the area in the user's business volume in the future, predict the different types of areas in the area: the number of macro base stations, the number of micro base stations, the service carrying capacity of macro base stations and The service carrying capacity of the micro base station, predicting the spectrum used by users in the future in the area;

Subtract the offload transmission rate of the micro base station from the predicted transmission rate of services in different types of areas in the area in the future to obtain the transmission rate of the macro base station in different types of areas in the area, and then, according to each macro base station The service carrying capacity of the station and the number of macro base stations can be used to obtain the spectrum used by users in the area in the future. Wherein, the transmission rate of the offloading of the micro base station can be obtained by the number of the micro base station, the service carrying capacity of the micro base station and the bandwidth allocated to the micro base station.

The spectrum demand prediction method provided by the present invention obtains the transmission rate of the user's business within the set future time in different types of areas in the area; and in different types of areas in the area: the number of macro base stations, the number of micro base stations, the number of macro base stations The service carrying capacity and the service carrying capacity of the micro base station can finally predict the spectrum used by users in the area in the future, which improves the accuracy of the prediction, and then can reasonably configure the spectrum resources of the network.

FIG. 2 is a flow chart of another embodiment of the spectrum demand prediction method provided by the present invention, which is a specific implementation manner of the embodiment shown in FIG. 1 . As shown in Figure 2, the method specifically includes:

S201 , predicting the traffic volume of users in the set future time in the area; for the specific execution process of this step, please refer to the corresponding content of step 101 . Since the service of the mobile communication network used by the user includes the voice service and the data service, so in this step, the user's service volume in the future time set in the prediction area also includes the voice service and the data service.

S202, according to T _v =M _v ×60×K (1), convert voice traffic M _v into equivalent data traffic T _v ; where K is a conversion coefficient;

In order to facilitate the unified processing of the predicted voice traffic and data traffic, the voice traffic can be converted into equivalent data traffic through formula (1); where, M _v is the number of minutes of voice calls, and the unit of K for Kbps.

S203. Determine the equivalent data traffic volume and the sum of the data traffic volume as the traffic volume of the user in the future time set in the area;

Convert the voice service volume M _v in the user's service volume in the predicted future time in the predicted area to the equivalent data service volume T _v , and then add the equivalent data service volume T _v to the original predicted service volume The sum of the data traffic volumes is determined as the traffic volume of users in the future time set in the area. In this way, the business volume can be uniformly processed in subsequent steps.

For step 102, according to the traffic distribution of users in the historical time in the area, determine the transmission rate of business in different types of areas in the area in the user traffic in the above-mentioned future time; steps 204 to 206 of this embodiment provide a a specific 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 downlink business during busy hours in the traffic; wherein, _Td is the data traffic volume, T _V is the equivalent data traffic volume, DLR is the proportion of the downlink traffic volume of the user in the total traffic volume in the historical time, BHR is the ratio of the busy hour business volume of the user in the historical time to the total traffic volume, and t is the duration of the busy time in the future;

Considering the characteristics of the predicted uneven distribution of user traffic in time and the asymmetry of uplink and downlink (these characteristics can be obtained after analyzing the traffic distribution of users in the historical time in the area), in actual application scenarios In , there will be a certain concentration in the distribution of business volume. Among them, the concentration degree of the traffic in time directly reflects the required frequency spectrum. In this embodiment, while considering the concentration of business volume in time, the distribution of uplink and downlink services in the total traffic volume is also considered, and finally the transmission rate TBH of the busy hour downlink business in the traffic volume is calculated by formula (2) . The definition of "busy hour" can be that the business volume generated during this period exceeds the set value, or a certain fixed time period in a day, etc.

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

Obtain the transmission rate TBH _m of the downlink service during busy hours in the future; wherein, TBH is the transmission rate of the downlink service during busy hours under all systems, m is the network standard adopted by the area, P _m is the proportion of users under the m network standard, U _m is the normalization parameter of the service volume used by each user under the m network standard;

Among them, m is the network system used in the forecast area, which can include the second-generation/third-generation/fourth-generation mobile communication technology (Second-Generation/Third-Generation/Fourth-Generation, 2G/3G/4G); P _m is the proportion of users under the m network standard to all users under the system in the area; U _m is the normalized parameter of the service volume used by each user under the m network standard, which is used to indicate that each user belonging to the m network The relative service usage of each user, for example: U _3G is usually set to 1, which means that each 3G user uses 1 unit of service, if U _2G and U _4G are a (less than 1) and b (greater than 1) respectively , it means that each 2G user uses a unit of service volume on average, and each 4G user uses b unit of service volume on average.

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

Obtain the transmission rate TBH _m,n of the corresponding downlink business in different types of areas in the area in the future; where n is the type of area, TBH _m is the transmission rate of the downlink business during busy hours under the m network standard, and P _n is The proportion of users in the n-type area under the m network standard, U _n is the normalization parameter of the traffic volume used by each user in the type n area under the m network standard;

Among them, P _n is specifically the proportion of the number of users in n-type regions under the m network standard to the number of users in all types of regions under the m network standard; U _n is the number of users in n-type regions under the m network standard. A normalized parameter of the service volume, which is used to represent the relative service usage per user in different regions under the same network standard. For example, U _s can be set to 1, which means that each suburban user uses 1 unit of business volume, if U _u and U _r are respectively c (greater than 1) and d (less than 1), it means that each urban area Users use c units of business volume, and each rural user uses d units of business volume on average.

S207. According to the transmission rate of services in different types of areas in the area in the user's traffic in the future, the total number of preset macro base stations, and the distribution ratio of macro base stations in different types of areas in the area, predict the traffic of different types of areas in the area. The number of macro base stations; for the specific execution process of this step, refer to the corresponding content of step 103.

S208, sorting the macro base stations in the same type of area according to the transmission rate of the business carried by them, and dividing them into j groups of macro base station groups with equal numbers;

Considering the uneven distribution of traffic in space, there are "hot spot" areas that absorb most of the traffic in different types of regions, and "cold spot" areas that only carry a small amount of traffic. In this step, the predicted The macro base stations in the same type of area are sorted according to the transmission rates of the services they bear, and are divided into j groups of macro base station groups with equal numbers. Then judge the relationship between the transmission rate of the service carried by each macro base station group and the threshold value through subsequent steps, and determine whether to configure micro base stations for each macro base station and the number of configurations.

S209, according to ${\mathrm{MSN}}_{\mathrm{no}}=\underset{i=1}{\overset{j}{\mathrm{\Σ}}}{\mathrm{MSN}}_{\mathrm{ni}}---\left(5\right)$

${\mathrm{<span\; class="notranslate">\; MSN</span>}}_{\mathrm{<span\; class="notranslate">\; ni</span>}}<span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}{\mathrm{<span\; class="notranslate">\; SN</span>}}_{\mathrm{<span\; class="notranslate">\; ni</span>}}<span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; MSPS</span>}<span\; class="notranslate">\; ,</span>& \frac{{\mathrm{<span\; class="notranslate">\; TBH</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; TR</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\mathrm{<span\; class="notranslate">\; SN</span>}}_{\mathrm{<span\; class="notranslate">\; ni</span>}}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; SD</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}}<span\; class="notranslate">\; \&Greater\; Equal;</span>{\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; MS</span>}}\\ \mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; ,</span>& \frac{{\mathrm{<span\; class="notranslate">\; TBH</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; TR</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\mathrm{<span\; class="notranslate">\; SN</span>}}_{\mathrm{<span\; class="notranslate">\; ni</span>}}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; SD</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; MS</span>}}\end{array}\right.<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 6</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>$

Predict the number MSN _n of micro base stations in the area of type n; wherein, i is an integer from 1 to j, MSN _ni is the number of micro base stations contained in the i-th group of macro base station groups in the area of type n, and SN _ni is the number of micro base stations whose type is The number of macro base stations contained in the i-th group of macro base station groups in the area of n, MSPS is the number of micro base stations contained in the macro cell of each macro base station in the area of type n, TBH _n is the corresponding number of micro base stations in the area of type n The transmission rate of the downlink service during busy hours, TR _i is the transmission rate of the downlink service during busy hours carried by the i-th group of macro base stations in the area of type n, among the transmission rates of the downlink services during busy hours corresponding to all areas of type n Ratio, SD _n is the site density of the macro base station in the area of n, G _MS is the transmission rate density threshold value of the busy hour downlink service required for setting up the micro base station at each macro base station;

The formulas (5) and (6) in this step are determined by judging whether the spatial distribution density of the transmission rate of the downlink service carried by each macro base station group during busy hours reaches the required busy time for setting up a micro base station for each macro base station. The transmission rate density threshold G _MS of the downlink service is used to determine whether to set up micro base stations in the macro cells of the macro base stations in the macro base station group, and the specific number of establishments. If the TBH _n is refined according to different network standards, the number MSN _m,n of micro base stations for regions of type n under different network standards m can also be obtained. Steps 208-209 may be regarded as a specific implementation manner of step 104.

S210. Predict the service carrying capacity of the macro base station and the service carrying capacity of the micro base station in different types of areas in the area according to the transmission rates of services in different types of areas in the area in the user's traffic in the future; the specific execution process of this step Refer to the corresponding content in step 105. As a further description of step 105, the present embodiment provides the following scheme:

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

MiT=FE _Mi ×ASN _Mi (8),

Calculate respectively n types of areas: the service carrying capacity MaT of the macro base station and the service carrying capacity MiT of the micro base station; where Ma is the attribute identifier of the macro base station, Mi is the attribute identifier of the micro base station, FE _Ma is the spectrum efficiency of the macro base station, ASN _Ma is the average number of sectors of the macro base station, FE _Mi is the spectrum efficiency of the micro base station, ASN _Mi is the average number of sectors of the micro base station, and L is the transmission rate of the downlink service during busy hours. The network system in this area can carry the business The ratio of the transmission rate to the maximum capability value.

S211. According to the transmission rate of services in different types of areas in the area in the user's traffic in the future, predict the number of different types of areas in the area: the number of macro base stations, the number of micro base stations, the business carrying capacity of macro base stations, and the number of micro base stations service carrying capacity, and predict the spectrum used by users in the area in the future; for the specific execution process of this step, please refer to the corresponding content of step 106. As a further description of step 106, the present embodiment provides the following scheme:

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

${\mathrm{<span\; class="notranslate">\; BW</span>}}_{\mathrm{<span\; class="notranslate">\; ni</span>}}<span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\frac{{\mathrm{<span\; class="notranslate">\; TBH</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; TR</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; MiT</span>}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; FU</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; MSN</span>}}_{\mathrm{<span\; class="notranslate">\; ni</span>}}}{\mathrm{<span\; class="notranslate">\; TT</span>}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; FU</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; SN</span>}}_{\mathrm{<span\; class="notranslate">\; ni</span>}}}& {\mathrm{<span\; class="notranslate">\; MSN</span>}}_{\mathrm{<span\; class="notranslate">\; ni</span>}}<span\; class="notranslate">\; ></span>\mathrm{<span\; class="notranslate">\; 0</span>}\\ \frac{{\mathrm{<span\; class="notranslate">\; TBH</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; TR</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; TT</span>}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; FU</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; SN</span>}}_{\mathrm{<span\; class="notranslate">\; ni</span>}}}& {\mathrm{<span\; class="notranslate">\; MSN</span>}}_{\mathrm{<span\; class="notranslate">\; ni</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}\end{array}\right.$

Predict the spectrum BW _n used by users in n types of areas in the area in the future; where j is the macro base station in the same type of area sorted according to the transmission rate of the downlink business during the busy time carried by them, and divide them into equal numbers of macro base stations The number of station groups, i is an integer from 1 to j, BW _ni is the frequency spectrum required by the i-th group of macro base station groups in an area of type n, and TBH _n is the transmission of downlink services during busy hours in an area of type n rate, TR _i is the ratio of the transmission rate of downlink services during busy hours in the area of type n to the transmission rate of downlink services during busy hours of group i, MiT is the service carrying capacity of the micro base station above, and B _i is the type of n The allocated bandwidth of the micro base stations included in the i-th group of macro base station groups in the region, FU _i is the spectrum utilization rate of the micro base stations contained in the i-th group of macro base station groups in the region of type n, MSN _ni is the type of n The number of micro base stations contained in the i-th macro base station group in the region, MaT is the service carrying capacity of the macro base station, FU _n is the spectrum utilization rate of the macro base station in the area of type n, SN _ni is the area of type n The number of macro base stations in the i-th macro base station group. Among them, the concept of FU _n arises because mobile communication systems usually use large-bandwidth spectrum and apply it to various geographical scenarios, which will cause some spectrum transmission performance in the entire spectrum to be poor, causing overall network performance to decline. Therefore, the parameter of spectrum utilization is introduced in this embodiment to represent the performance "discount" caused by the above factors.

Furthermore, in steps 207 to 211, further division can be continued according to the number of macro base stations and micro base stations under each network standard, and their corresponding service carrying capacity, and finally the users in the predicted area in the future will be classified as The frequency spectrum used under the m network standard in the region n.

The spectrum demand prediction method provided by the present invention obtains the transmission rate of the user's business within the set future time in different types of areas in the area; and in different types of areas in the area: the number of macro base stations, the number of micro base stations, the number of macro base stations The service carrying capacity and the service carrying capacity of the micro base station can finally predict the spectrum used by users in the area in the future, which improves the accuracy of the prediction, and then can reasonably configure the spectrum resources of the network.

Those of ordinary skill in the art can understand that all or part of the steps for implementing the above method embodiments can be completed by program instructions and related hardware. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiments; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk and other various media that can store program codes.

Fig. 3 is a schematic structural diagram of an embodiment of an apparatus for predicting spectrum demand provided by the present invention. As shown in Figure 3, the spectrum demand forecasting device can be a network device, a server in the network, or a module, chip, etc. integrated on the network device or server, and can perform the spectrum demand forecast in the embodiment shown in Figure 1 method steps. The spectrum demand prediction device includes: a prediction module 31 and a processing module 32, wherein:

Prediction module 31, for predicting the traffic volume of users within the future time set in the area;

The processing module 32 is used to determine the transmission rate of services in different types of areas in the area in the user's traffic in the future time according to the traffic distribution of users in the area at historical time;

The prediction module 31 is also used for:

According to the transmission rate of business in different types of areas in the area in the user's traffic in the future, the total number of preset macro base stations, and the distribution ratio of macro base stations in different types of areas in the area, predict the traffic of different types of areas in the area. the number of macro base stations;

Predict the number of micro base stations in different types of areas in the area according to the transmission rate of services in different types of areas in the area in the user's traffic in the future and the number of macro base stations in different types of areas in the area;

Predict the business carrying capacity of the macro base station and the service carrying capacity of the micro base station in different types of areas in the area according to the transmission rate of the business in different types of areas in the area in the user's traffic in the future;

According to the transmission rate of services in different types of areas in the area in the user's business volume in the future, the predicted number of different types of areas in the area: the number of macro base stations, the number of micro base stations, the business carrying capacity of macro base stations and the number of micro base stations Service carrying capacity, predicting the spectrum used by users in the area in the future.

Specifically, the spectrum demand prediction device shown in this embodiment realizes the prediction process of the spectrum required by users in a certain area in the future as follows:

The prediction module 31 predicts the traffic volume of users in the future time set in the area. For this process, refer to the corresponding content in step 101; the processing module 32 determines the traffic volume predicted by the prediction module 31 according to the traffic distribution situation of users in the historical time in the area. The transmission rate of the business of different types of regions in the user's business volume in the future time, this process can refer to the corresponding content of step 102; The transmission rate of services in different types of areas in the area, the total number of preset macro base stations, the distribution ratio of the macro base stations in different types of areas in the area, and the number of said macro base stations in different types of areas in the area are predicted. The process can be found in The corresponding content of step 103; then, prediction module 31 predicts the number of macro base stations in different types of areas in this area according to the transmission rate of the business of different types of areas in this area in the user's traffic in the future time, and the predicted number of macro base stations in this area. For the number of micro base stations in different types of areas, this process can refer to the corresponding content in step 104; then, the prediction module 31 predicts the different types of micro base stations in this area according to the transmission rate of the business in different types of areas in the area in the user's traffic in the future. For the service carrying capacity of the macro base station and the service carrying capacity of the micro base station in the area, the process can refer to the corresponding content in step 105; finally, the forecasting module 31 is based on the business volume of different types of areas in the area in the above-mentioned future time. The transmission rate is predicted for different types of areas in the area: the number of macro base stations, the number of micro base stations, the business carrying capacity of macro base stations and the service carrying capacity of micro base stations, and the spectrum used by users in the future is predicted in this area. This process Refer to the corresponding content in step 106.

The spectrum demand forecasting device provided by the present invention obtains the transmission rate of the user's business within the set future time in different types of areas in the area; and in different types of areas in the area: the number of macro base stations, the number of micro base stations, the number of macro base stations The service carrying capacity and the service carrying capacity of the micro base station can finally predict the spectrum used by users in the area in the future, which improves the accuracy of the prediction, and then can reasonably configure the spectrum resources of the network.

The present invention also provides a structural schematic diagram of another embodiment of the spectrum demand prediction device. The schematic structural diagram is a specific implementation manner of the embodiment shown in FIG. 3 , and the steps of the spectrum demand prediction method shown in FIG. 2 can be executed. On the basis of the structure and functions shown in Figure 3, the spectrum demand forecasting device also includes:

The processing module 32 is used for: according to _Tv = _Mv ×60×K, convert the voice traffic _{Mv into the equivalent data traffic Tv ;} wherein, K is the conversion coefficient; the equivalent data traffic and the data traffic The sum of the volume is determined as the user's business volume in the future time set in the area;

The processing module 32 is also used for $\mathrm{TBH}=\frac{(T_d+T_V)\&times;\mathrm{DLR}\&times;\mathrm{BHR}}{t}$

Obtain the transmission rate TBH of the downlink business during busy hours in the above traffic; wherein, T _d is the above data traffic, T _V is the above equivalent data traffic, DLR is the downlink traffic of the user in the above historical time in the total traffic , BHR is the ratio of the busy hour business volume of the user in the historical time to the total traffic volume, and t is the duration of the busy time in the future time;

According to TBH _m =TBH×P _m ×U _m

Obtain the transmission rate TBH _m of the downlink service during busy hours in the future; wherein, TBH is the transmission rate of the downlink service during busy hours under all standards, m is the network standard adopted in this area, and P _m is the user under the m network standard The proportion of U _m is the normalization parameter of the service volume used by each user under the m network standard;

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

Obtain the transmission rate TBH _m,n of the corresponding downlink business in different types of areas in this area in the future time; wherein, n is the type of area, and TBH _m is the transmission rate of the downlink business during busy hours under the m network standard, P _n For the proportion of users in the n-type area under the m network standard, U _n is the normalization parameter of each user using the traffic volume in the area where the type is n under the m network standard;

The prediction module 31 is used to sort the macro base stations in the same type of area according to the size of the transmission rate of the business carried, and divide them into j groups of macro base stations with equal numbers;

according to ${\mathrm{MSN}}_{\mathrm{no}}=\underset{i=1}{\overset{j}{\mathrm{\&Sigma;}}}{\mathrm{MSN}}_{\mathrm{ni}},$

${\mathrm{<span\; class="notranslate">\; MSN</span>}}_{\mathrm{<span\; class="notranslate">\; ni</span>}}<span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}{\mathrm{<span\; class="notranslate">\; SN</span>}}_{\mathrm{<span\; class="notranslate">\; ni</span>}}<span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; MSPS</span>}<span\; class="notranslate">\; ,</span>& \frac{{\mathrm{<span\; class="notranslate">\; TBH</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; TR</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\mathrm{<span\; class="notranslate">\; SN</span>}}_{\mathrm{<span\; class="notranslate">\; ni</span>}}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; SD</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}}<span\; class="notranslate">\; \&Greater\; Equal;</span>{\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; MS</span>}}\\ \mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; ,</span>& \frac{{\mathrm{<span\; class="notranslate">\; TBH</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; TR</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\mathrm{<span\; class="notranslate">\; SN</span>}}_{\mathrm{<span\; class="notranslate">\; ni</span>}}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; SD</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; MS</span>}}\end{array}\right.$

Predict the number MSN _n of micro base stations in the area of type n; wherein, i is an integer from 1 to j, MSN _ni is the number of micro base stations contained in the i-th group of macro base station groups in the area of type n, and SN _ni is the number of micro base stations whose type is The number of macro base stations contained in the i-th group of macro base station groups in the area of n, MSPS is the number of micro base stations contained in the macro cell of each macro base station in the area of type n, TBH _n is the corresponding number of micro base stations in the area of type n The transmission rate of the downlink service during busy hours, TR _i is the transmission rate of the downlink service during busy hours carried by the i-th group of macro base stations in the area of type n, among the transmission rates of the downlink services during busy hours corresponding to all areas of type n Ratio, SD _n is the site density of the macro base station in the area of n, G _MS is the transmission rate density threshold value of the busy hour downlink service required for setting up the micro base station at each macro base station;

The prediction module 31 is also used for:

According to MaT=FE _Ma ×ASN _Ma ×L,

MiT=FE _Mi ×ASN _Mi

Calculate respectively n types of areas: the service carrying capacity MaT of the macro base station and the service carrying capacity MiT of the micro base station; where Ma is the attribute identifier of the macro base station, Mi is the attribute identifier of the micro base station, FE _Ma is the spectrum efficiency of the macro base station, ASN _Ma is the average number of sectors of the macro base station, FE _Mi is the spectrum efficiency of the micro base station, ASN _Mi is the average number of sectors of the micro base station, and L is the transmission rate of downlink services during busy hours in n-type areas. The network system in this area The proportion of the maximum capacity value of the transmission rate that can carry services;

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

${\mathrm{<span\; class="notranslate">\; BW</span>}}_{\mathrm{<span\; class="notranslate">\; ni</span>}}<span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\frac{{\mathrm{<span\; class="notranslate">\; TBH</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; TR</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; MiT</span>}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; FU</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; MSN</span>}}_{\mathrm{<span\; class="notranslate">\; ni</span>}}}{\mathrm{<span\; class="notranslate">\; TT</span>}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; FU</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; SN</span>}}_{\mathrm{<span\; class="notranslate">\; ni</span>}}}& {\mathrm{<span\; class="notranslate">\; MSN</span>}}_{\mathrm{<span\; class="notranslate">\; ni</span>}}<span\; class="notranslate">\; ></span>\mathrm{<span\; class="notranslate">\; 0</span>}\\ \frac{{\mathrm{<span\; class="notranslate">\; TBH</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; TR</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; TT</span>}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; FU</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; SN</span>}}_{\mathrm{<span\; class="notranslate">\; ni</span>}}}& {\mathrm{<span\; class="notranslate">\; MSN</span>}}_{\mathrm{<span\; class="notranslate">\; ni</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}\end{array}\right.$

Predict the spectrum BW _n used by users in n types of areas in the area in the future; where j is the macro base station in the same type of area sorted according to the transmission rate of the downlink business during the busy time carried by them, and divide them into equal numbers of macro base stations The number of station groups, i is an integer from 1 to j, BW _ni is the frequency spectrum required by the i-th group of macro base station groups in an area of type n, and TBH _n is the transmission of downlink services during busy hours in an area of type n rate, TR _i is the ratio of the transmission rate of the downlink business during busy hours in the i-th group to the transmission rate of the downlink business during the busy time in the area of type n, MiT is the service carrying capacity of the micro base station, B _i is the macro base station of the i-th group The micro base stations included in the station group correspond to the allocated bandwidth, FU _i is the spectrum utilization rate of the micro base stations contained in the i-th group of macro base station groups, MSN _ni is the micro base station contained in the i-th group of macro base station groups in an area of type n The number of base stations, MaT is the service carrying capacity of the macro base station, FU _n is the spectrum utilization rate of the macro base station in the area of type n, and SN _ni is the number of macro base stations in the i-th group of macro base station groups in the area of type n.

Specifically, the spectrum demand prediction device shown in this embodiment realizes the prediction process of the spectrum required by users in a certain area in the future as follows.

The prediction module 31 predicts the traffic volume of users in the set future time in the area; for this process, refer to the corresponding content of step 201 .

For the voice traffic in the above traffic, the processing module 32 converts the voice traffic M _v into the equivalent data traffic T _v according to T _v =M _v ×60×K (1), and then converts the equivalent data traffic The sum of traffic volume and data traffic volume is determined as the traffic volume of users in the future time set in the above area, so as to uniformly process the predicted voice traffic volume and data traffic volume.

The processing module 32 determines the transmission rate of services in different types of regions in the area in the user's traffic in the future predicted by the prediction module 31 according to the traffic distribution of the historical time users in the area, specifically including:

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 downlink service during busy hours in the traffic; according to TBH _m = TBH×P _m ×U _m (3) Obtain the transmission rate TBH _m of the downlink service during busy hours in the future; according to TBH _m,n = TBH _m ×P _n ×U _n (4) Obtain the transmission rate TBH _m,n of the corresponding downlink services in different types of areas in the area in the future. For the execution process, please refer to the corresponding content of steps 204-206.

Prediction module 31 is based on the transmission rate of services in different types of areas in the area, the total number of preset macro base stations, and the allocation ratio of the macro base station in different types of areas in the area according to the user's traffic in the future obtained by processing module 32 , to predict the number of macro base stations in different types of areas in the area. For this process, refer to the corresponding content in step 103 .

The prediction module 31 predicts the number of micro base stations in different types of areas in the area according to the transmission rate of services in different types of areas in the area in the user's traffic in the future and the predicted number of macro base stations in different types of areas in the area, Specifically include:

Sorting the macro base stations in the same type of area according to the transmission rate of the business carried by them, and dividing them into j groups of macro base station groups with equal numbers;

according to ${\mathrm{MSN}}_{\mathrm{no}}=\underset{i=1}{\overset{j}{\mathrm{\&Sigma;}}}{\mathrm{MSN}}_{\mathrm{ni}}---\left(5\right)$

${\mathrm{<span\; class="notranslate">\; MSN</span>}}_{\mathrm{<span\; class="notranslate">\; ni</span>}}<span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}{\mathrm{<span\; class="notranslate">\; SN</span>}}_{\mathrm{<span\; class="notranslate">\; ni</span>}}<span\; class="notranslate">\; \&times;</span>\mathrm{<span\; class="notranslate">\; MSPS</span>}<span\; class="notranslate">\; ,</span>& \frac{{\mathrm{<span\; class="notranslate">\; TBH</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; TR</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\mathrm{<span\; class="notranslate">\; SN</span>}}_{\mathrm{<span\; class="notranslate">\; ni</span>}}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; SD</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}}<span\; class="notranslate">\; \&Greater\; Equal;</span>{\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; MS</span>}}\\ \mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; ,</span>& \frac{{\mathrm{<span\; class="notranslate">\; TBH</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; TR</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{{\mathrm{<span\; class="notranslate">\; SN</span>}}_{\mathrm{<span\; class="notranslate">\; ni</span>}}<span\; class="notranslate">\; /</span>{\mathrm{<span\; class="notranslate">\; SD</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}}<span\; class="notranslate">\; <</span>{\mathrm{<span\; class="notranslate">\; G</span>}}_{\mathrm{<span\; class="notranslate">\; MS</span>}}\end{array}\right.<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 6</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>$

Predict the number MSN _n of micro base stations in an area of type n; for details of this process, refer to the corresponding content in steps 208-209.

Next, the predicting module 31 predicts the service carrying capacity of the macro base station and the service carrying capacity of the micro base station in different types of areas in the area according to the transmission rates of services in different types of areas in the area in the user's traffic in the future, specifically including :

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

MiT=FE _Mi ×ASN _Mi (8),

For n types of regions, respectively calculate: the service carrying capacity MaT of the macro base station and the service carrying capacity MiT of the micro base station. For this process, refer to the corresponding content in step 210 .

Finally, the prediction module 31 predicts the number of macro base stations, the number of micro base stations, and the traffic of macro base stations in different types of areas in the area according to the transmission rates of services in different types of areas in the area in the user's traffic in the future. Carrying capacity and service carrying capacity of the micro base station, predicting the spectrum used by users in the area in the future, including:

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

${\mathrm{<span\; class="notranslate">\; BW</span>}}_{\mathrm{<span\; class="notranslate">\; ni</span>}}<span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\frac{{\mathrm{<span\; class="notranslate">\; TBH</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; TR</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; MiT</span>}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; B</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; FU</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; MSN</span>}}_{\mathrm{<span\; class="notranslate">\; ni</span>}}}{\mathrm{<span\; class="notranslate">\; TT</span>}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; FU</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; SN</span>}}_{\mathrm{<span\; class="notranslate">\; ni</span>}}}& {\mathrm{<span\; class="notranslate">\; MSN</span>}}_{\mathrm{<span\; class="notranslate">\; ni</span>}}<span\; class="notranslate">\; ></span>\mathrm{<span\; class="notranslate">\; 0</span>}\\ \frac{{\mathrm{<span\; class="notranslate">\; TBH</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; TR</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}}{\mathrm{<span\; class="notranslate">\; TT</span>}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; FU</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; \&times;</span>{\mathrm{<span\; class="notranslate">\; SN</span>}}_{\mathrm{<span\; class="notranslate">\; ni</span>}}}& {\mathrm{<span\; class="notranslate">\; MSN</span>}}_{\mathrm{<span\; class="notranslate">\; ni</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}\end{array}\right.$

Refer to the corresponding content of step 211 for the process of predicting the spectrum BW _n used by users in n types of areas in the area in the future.

Furthermore, in the process of implementing spectrum prediction, the device shown in this embodiment can further divide according to the number of macro base stations, the number of micro base stations under each network standard, and their corresponding service carrying capacity, and finally obtain the future prediction area Spectrum used by users under the m network standard in the area of type n within the time period.

The spectrum demand forecasting device provided by the present invention obtains the transmission rate of the user's business within the set future time in different types of areas in the area; and in different types of areas in the area: the number of macro base stations, the number of micro base stations, the number of macro base stations The service carrying capacity and the service carrying capacity of the micro base station can finally predict the spectrum used by users in the area in the future, which improves the accuracy of the prediction, and then can reasonably configure the spectrum resources of the network.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than limiting them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: It is still possible to modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the technical solutions of the various embodiments of the present invention. scope.

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.