Classifications

• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04W—WIRELESS COMMUNICATION NETWORKS
  • H04W24/00—Supervisory, monitoring or testing arrangements
  • H04W24/08—Testing, supervising or monitoring using real traffic
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04W—WIRELESS COMMUNICATION NETWORKS
  • H04W24/00—Supervisory, monitoring or testing arrangements

Definitions

• This invention relates generally to data gathering systems and, in particular, to systems and related methods for simultaneously gathering data from multiple wireless communication networks.
• a typical cellular communication network essentially consists of a plurality of mobile subscriber units (MSUs), a plurality of cell sites with base station equipment, a plurality of base station controllers (BSCs), which may be associated with each base station, or may be centralized to provide control for a plurality of base stations, a mobile telephone switching office (MTSO or Mobile Switching Center (MSC)) and various local or networked databases which may include a home location register (HLR), visitor location register
• VLR voice resource control
• AuC authentication center
• EIR equipment identity register
• a typical cellular communication network is characterized by the concepts of frequency reuse and handoff.
• a frequency is reused at many s which are geographically separated from each other by a distance sufficient to ensure th the interference from other sites utilizing the same frequency is low enough to permit a quality signal from the primary serving site.
• Handoffs are the process of changing the serving site as a subscriber moves from the primary service area of one site to that of another.
• cellular systems are initially designed with a set of cell sites that pro partial overlapping RF coverage over a market area of interest.
• additional cell sites are constructed between the initial cell sites.
• Th coverage area of each cell site is reduced through a combination of antenna system desi and transmitter power reduction in order to provide limited overlap of individual covera areas while maintaining contiguous coverage.
• capacity within each cell is limited by the available spectrum and the number of frequency assignments can be assigned for that cell without violating the interference constraints of the commo air interface standard employed for the network.
• Capacity can also be reused through t use of "sectored" sites, in which a single site is equipped with antenna systems and transceivers to permit multiple cells to be created from a single site.
• a common sectori approach utilizes three sectors per site, each providing primary coverage in a different
• Common frequency reuse patterns range from a reuse pattern of twenty-one, in which t frequency assignments are reused over a pattern of seven tri-sectored sites in Frequency Division Multiple Access (FDMA) systems, to reuse patterns of one, in which the same frequency assignment is reused in every cell in Code Division Multiple Access (CDMA) systems.
• FDMA Frequency Division Multiple Access
• CDMA Code Division Multiple Access
• CAI common air interface
• FDMA Frequency Division Multiple Access
• Digital systems generally provide multiple communications channels within single frequency assignment.
• TDMA Time Division Multiple Access
• TDMA Time Division Multiple Access
• CDMA Code Division Multiple Access
• CDMA Code Division Multiple Access
• Channels that are transmitted from the base stations and received by MSUs are known as forward channels, while those that are transmitted from the MSU and received b the base station are known as reverse channels. Channels are further differentiated by thei function. Those that are generally used for signaling between the MSU and the base stati are known as control channels, while those that are generally used to carry voice or data signals are known as traffic channels. Generally, some limited forms of signaling are available when a call is in progress on a traffic channel to permit control of the call in progress or to support system requirements such as handoffs. In certain cellular systems, when an MSU is in an idle mode, it may select a forward control channel (FCC) to monitor for signaling information.
• FCC forward control channel
• FCC reverse control channel
• the protocols for the various common air interfaces determine which FCC-RCC pair is to be used.
• FCCs are used to send two types of messages. Overhead messages provide information to all MSU units monitoring the channel, and may include system and cell site identifiers, and information regarding the system configuration (e.g. neighbor lists).
• the FCC also provides information for specific users, including pages and short data messages. Absent any means of determining which cell is being monitored by a particular MSU, such messages would need to be broadcast over all the FCCs of all cells within a network in order to ensure that the MSU receives the message.
• LAs location areas
• Cellular systems ordinarily use a process known as registration in order to determine which LA serves a MSU.
• registration Generally, when an MSU is first turned on, it will initially monitor the strongest available FCC. It will then register in accordance with information contained within the FCC overhead data. This is accomplished by exchanging prescribed messages, including the subscriber identity, over the FCC-RCC pair.
• the VLR stores the information regarding the most current LA is then stored in the system VLR and the MSU. If the MSU later determines that the LA identifier included in the FCC no longer matches the data it has stored, it will initiate a location update that will repeat the registration process with the new LA identifier. Re-registration may also occur in response to system requests.
• subscriber messages are initially sent only to cells within the system which correspond to the LA information for the MSU that is stored in the VLR.
• the network sends a page from the base station to the MSU by broadcasting a paging message on the FCC of the cells within the LA. If received, the MSU responds by sending its identifying information once again to the network along with a message confirming that it received the page. The network then sends a traffic channel assignment to the MSU on the forward control
• the MSU when an MSU originates a call, the MSU initiates a signaling sequence which includes its identity and the called number using the RCC that corresponds to the monitored FCC. After verifying that the MSU corresponds to a valid subscriber record, the MSU is assigned to a traffic channel and the MTSO completes the call to the called
• Each will have certain licensed frequency assignments, or bands of licensed frequencies, that it is permitted to use within its network.
• Each will have a common air interface, generally an industry standard, but occasionally a proprietary system developed by a particular vendor and not subjected to an industry standards process.
• test equipment has been developed to test the operation of, and characterize the quality of, the individual networks.
• Test equipment has been developed that allow the simultaneous testing of multiple networks at a single location. When coupled with navigation and data recording and analysis capabilities, they permit characterizing the comparative quality of various networks over a given set of geographical points, one location at a time (generally referred to as a drive route, since the test equipment is ordinarily installed in a vehicle and driven throughout a market area.)
• such equipment typically is limited to gathering information from the portions of the networks that are in the vicinity of the test equipment. In major cellular system market area, this may mean the equipment is limited to gathering information fro a small subset of the active cells at any given moment in time.
• the data processing capabilities of such equipment since a purpo of the equipment is to test the operation and the quality of the wireless communication networks, the data processing capabilities of such equipment generally are not designed t gather data to make market share, usage comparisons, or user profiles for the different wireless communication networks.
• the present invention encompasses data gathering systems and related methods for gathering data from wireless communication networks. For a given geographic area, there may be several service providers operating wireless communication networks utilizing various types of common air interface standards.
• One data gathering system in accordance with the invention gathers data from each wireless communication network simultaneously.
• the system comprises a plurality of data gathering nodes deployed in a sampling network, and a control center that provides management of the data collection processes of each node, data collection from each of the nodes, error detection, management of the collected data, and overall administration of the network.
• a data gathering node may comprise multiple receivers, a minimum of one for each wireless communication network. Each receiver employs a sampling algorithm to gather data from cell sites surrounding the data gathering node. The data gathered from each data gathering node is periodically sent to a control center, where it is stored.
• a data mining application may be run on the gathered data to generate marketing and usage information for each of the wireless communication networks.
• Figure 1 is an illustrative block diagram of a data gathering system for wireless communication networks in accordance with a presently preferred embodiment of the invention.
• Figure 2 is a representation of a cell layout of a wireless communication network and shows the placement of data gathering nodes like the one in Figure 4.
• Figure 3 is a representation of a group of cell sites comprising a cell group from which a data gathering node of Figure 4 samples to gather data.
• Figure 4 is a block diagram of the presently preferred embodiment of one of the data gathering nodes of the system of Figure 1.
• Figure 5 is a representation of storage of the gathered data in a relational database.
• Figure 6 is a sample excerpt of a baselining period data file.
• Figure 7 is a sample excerpt of a data file from which marketing and usage characterization information is to be generated.
• Figure 8 is a comparison between actual subscribers and measured subscribers used in calculating the subscriber share gross-up coefficient.
• Figure 9 is a sort of the data found in Figure 7 to count the number of measured subscribers.
• Figure 10 shows the calculation of the subscriber share gross-up coefficients and the market share percentage of each wireless communication network.
• Figure 11 is a sample report containing information on subscriber share.
• Figure 12 is a sample exce ⁇ t of a second data file from which marketing and usage characterization information is to be generated.
• Figure 13 is a comparison of different subscribers between the data file shown in Figure 7 and a second data file shown in Figure 12 used to determine new and churning subscribers.
• Figure 14 shows the calculation of the new subscriber gross-up coefficients and the market share percentage of new subscribers for each wireless communication network.
• Figure 15 shows the calculation of the churning subscriber gross-up coefficients and the market share percentage of churning subscribers for each wireless communication network.
• Figure 16 is a sample report containing information on new subscribers.
• Figure 17 is a sample report containing information on churning subscribers.
• Figure 18 is a comparison of actual roamers to roamers measured during a baseline period used to calculate the roamer gross-up coefficient.
• Figure 19 is a data sort of the data file of Figure 7 counting the number of roamers.
• Figure 20 shows the calculation of the roamer gross-up coefficient and the share of roamers for each wireless communication network.
• Figure 21 is a sample report containing information on roamer share.
• Figure 22 is a comparison of actual calls and measured calls during a baseline period used to calculate the call share gross-up coefficient.
• Figure 23 is a data sort of the file in Figure 7 counting the number of traffic channel assignments made for each wireless communication network.
• Figure 24 shows the calculation of the call share gross-up coefficient and the call share for each wireless communication network.
• Figure 25 is a sample report containing information on call share.
• Figure 26 is a sample excerpt of a data file from which base subscriber profiling information is to be generated.
• Figure 27 is a data sort of the file shown in Figure 26 to identify the number of traffic channel assignments made to different subscribers.
• Figure 28 is a data sort of the file shown in Figure 27 arranging subscribers by number of traffic channel assignments.
• Figure 29 is a data sort of the file shown in Figure 28 dividing the data into quartiles and counting the number of subscribers for each wireless communication network in each quartile.
• Figure 30 shows the calculation of market share of base subscribers for each wireless communication network in each quartile.
• Figures 31 and 32 are sample excerpts of data files from which new and churning subscriber profiling information is to be generated.
• Figure 33 is a data sort of the files shown in Figures 31 and 32 identifying new and churning subscribers.
• Figure 34 is a data sort of the file in Figure 31 counting the number of traffic channel assignments made to new subscribers.
• Figure 35 is a data sort of Figure 34 arranging subscribers by number of traffic channel assignments.
• Figure 36 is a data sort of the file shown in Figure 35 dividing the data into quartiles and counting the number of new subscribers for each wireless communication network in each quartile.
• Figure 37 shows the calculation of market share of new subscribers for each wireless communication network for each quartile.
• Figure 38 shows a data sort of Figure 31 counting the number of traffic channel assignments made to churning subscribers.
• Figure 39 is a data sort of Figure 38 arranging churning subscribers by the number of traffic channel assignments made.
• Figure 40 is a data sort of the file shown in Figure 39 dividing the data into quartiles and counting the number of churning subscribers for each wireless communication network in each quartile.
• Figure 41 shows the calculation of market share of churning subscribers for each wireless communication network for each quartile.
• Figure 42 is a sample exce ⁇ t of a data file from which peak and non-peak call share information is to be generated.
• Figure 43 is a comparison between actual calls and measured calls during a baseline period used to calculate the peak/non-peak call share gross-up coefficient.
• Figure 44 is a data sort of the file in Figure 42 counting the number of traffic channel assignments made during peak and non-peak hours for each wireless communication network.
• Figure 45 shows the calculation of the peak/non-peak gross-up coefficient and the peak and non-peak call share for each wireless communication network.
• Figure 46 is a sample exce ⁇ t of a data file from which incoming and outgoing call information is to be generated.
• Figure 47 is a data sort of the file in Figure 46 identifying traffic channel assignments and pages for each wireless communication network.
• Figure 48 shows the processing of the file shown in Figure 48 to identify and count incoming and outgoing calls.
• Figure 49 shows the calculation of the ingoing/outgoing call share gross-up coefficient and the incoming and outgoing call share for each wireless communication network.
• the present invention encompasses data gathering systems and related methods for gathering data from wireless communication networks.
• the following description is presented to enable any person skilled in the art to make and use the invention, and is provided in the context of a particular application and its requirements.
• Various modifications to the preferred embodiment will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the invention.
• the present invention is not intended to be limited to the embodiment shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.
• FIG. 1 A block diagram of a presently preferred embodiment of a data gathering system for wireless communication networks is shown in Figure 1.
• the data gathering system comprises N data gathering nodes
• N is an integer greater than one and represents the number of data gathering nodes used to gather data from cell sites in the wireless communication networks.
• Each data gathering node is coupled to a control center (120).
• a sample wireless communication network is represented by a distribution of cell sites (210) throughout a geographic area.
• the density of all sites (210) is greater in areas of high wireless communication traffic.
• the placement of each data gathering node (220) is dependent on the cell layouts of the wireless communication networks. A survey may be conducted prior to selecting monitoring locations in order to provide useful information regarding the density of cell sites (210) and the approximate areas the cell sites serve.
• Data gathering nodes (220) are placed in locations that permit each data gathering node to monitor an approximately equal number of cell sites (210) in each network and, in aggregate, maximize the number of cell sites (210) of each network that are monitored. Typically, in areas with greater cell densities, data gathering nodes (220) are spaced more closely together and locations are selected which have a smaller area in which they can effectively monitor cell sites.
• cells within a wireless communication network are associated with location areas (230) which are defined by the network operator.
• at least one data gathering node (220) is placed within each location area (230) of each wireless communication network to "sample” or gather data from the group of cell sites surrounding the data gathering node (a "cell group”).
• Data gathering nodes (220) may also be deployed within each location area (230) to gather data from additional cell sites (210) depending on the quantity and density of cell sites (210) within a particular location area (230) in order to gain sufficient samples of subscriber messages.
• Factors affecting the cell sites (210) from which a data gathering node can gather data include the physical environment of the node (particularly its height above the local terrain and the physical structures in the immediate vicinity of its antenna network), the relative locations and orientations of the local cell site antennas, the transmit power of the cells, and the terrain and mo ⁇ hology between the node and cell locations.
• the data gathering nodes (220) that are initially placed based on the cell layouts and location areas are meant to remain fixed for an extended period of time, although some initial adjustment may be necessary to optimize the gathering of data from the most number of cell sites (210) by each data gathering node from each wireless communication network. Periodic adjustments may be required in response to ongoing changes in the monitored networks. When the data gathering nodes (220) are initially placed, particular attention is paid to ensure that data is gathered from all cell sites in high usage areas such as business centers, high traffic areas, or ai ⁇ orts and bridges.
• each node may have multiple receivers, each configured to monitor control channels of base stations of one of multiple cellular networks.
• each receiver Prior to performing monitoring operations, each receiver is programmed to undertake an initialization process in which it scans all of the appropriate control channels of the wireless communication network that it is monitoring.
• the local processor will create and maintain a record of all readable control channels, their corresponding cell identifiers (or other information that may permit distinguishing the control channel of once cell site from that of another), location area identifiers, and the approximate received signal strength of each. This information will be uploaded to the control center (120), which then determines which data gathering nodes (220) are responsible for monitoring which cell sites (210).
• the control center will develop a set of sampling plans based upon the system configuration data.
• groups of cells for each network will be designated as within the sampling plan of a data gathering node based upon the following objectives and constraints: (1) all cells within a group will be within the same location area,
• sampling plan will be consistent with the configuration of the data gathering node.
• the development of the sampling plan may be performed manually, or may be accomplished automatically using an optimization program or process that provides a optimal solution using assigned weighting factors for each objectives, while maintaining location area and data gathering node configuration constraints.
• FIG. 3 shows a grouping of cell sites of one cellular system surrounding a data gathering node (310) that is monitored by one of the receivers of node (310) in accordance with a presently preferred embodiment of the invention.
• each data gathering node will sample from cell groups for each wireless communication network in a similar manner.
• the data gathering node (310) samples signals transmitted from a base station in cell site (320) for a time period, and then samples signals transmitted from the base station in cell site (330) for a time period, followed by cell site (340), cell site (350), cell site (360), cell site (370), and finally cell site (380).
• the data gathering node (310) samples signals from base stations belonging to each wireless communication network.
• each data gathering node monitors signals transmitted from the base station in a new cell by switching to the corresponding channel assignment of the base station in the new cell.
• a data gathering node will gather data from several seconds to several minutes from each cell in the cell group (230). The time periods may be uniform for each cell, or may be weighted based upon historical rates of subscriber messages monitored from each cell site. Other factors affecting the sampling period include the wireless communication network from which data is being gathered, and the strength at the data gathering node of the signal from the cell being monitored.
• each data gathering node does not sample each cell in the group of cell sites, but rather only a single cell site.
• Each data gathering node comprises an antenna network (410) that comprises one or more antennas and an RF distribution network connecting the antenna(s) to the receivers (420), P receivers (420), where P is an integer greater than one and represents the number of forward control channels from which data is to be gathered, a local processor with capabilities similar to a personal computer (440), a local storage device such as a hard drive (445), and a modem
• the antenna network (410) is coupled to each receiver (420) for signal reception, and each receiver (420) includes a corresponding controller (430).
• the combination of receivers at each data gathering node (110) is capable of receiving and decoding traffic from any common air interface standard on which a particular service provider may be operating, including N/AMPS (Narrowband/Advanced Mobile Phone Service), TDMA (Time Division Multiple Access), CDMA (Code Division Multiple Access), GSM (Global System for Mobile telephones), and iDEN in the cellular, SMR and PCS frequency bands.
• N/AMPS Narrowband/Advanced Mobile Phone Service
• TDMA Time Division Multiple Access
• CDMA Code Division Multiple Access
• GSM Global System for Mobile telephones
• iDEN in the cellular, SMR and PCS frequency bands.
• Each receiver (420) is coupled to the local processor (440).
• Each controller (430) allows the local processor (440) to control its corresponding receiver (420) functions, such as channel selection, decode mode, and cell sampling patterns.
• Each receiver is capable of receiving a forward control channel (FCC), forward traffic channel (FTC), reverse control channel (RCC), and reverse traffic channel (RTC) signals of a cellular communication network, determining the approximate received signal strength for the channel, and either decoding the signaling data or determining the presence of traffic.
• FCC forward control channel
• FTC forward traffic channel
• RRCC reverse control channel
• RTC reverse traffic channel
• each receiver (420) monitors the forward control channels of a different wireless communication network, observing all messages broadcast by the base station sent to all MSUs in the cell site associated with the service provider of interest.
• each receiver (420) may monitor a reverse control channel, forward traffic channel, or reverse traffic channel of a different wireless communication network. Messages decoded from the reverse control channel can be processed to generate subscriber calling pattern information as well as subscriber identity information, as further described below.
• each receiver (420) selects the following messages to save and discards all other messages: (1) certain overhead messages that provide system and cell site identification, network quality information (e.g.
• Registration Confirmations which include the subscriber MIN being confirmed, time of day and date of the confirmation, base station identification, and frequency/carrier identifier
• Pages which include the subscriber MIN being paged
• Traffic channel assignments which include the subscriber MIN for which a channel is being assigned.
• the filtered messages are then sent to the local processor (440) which records the time of receipt of each message.
• the local processor (440) is comparable in capabilities to a desktop personal computer.
• the local processor (440) is equipped with a fault tolerant power supply of sufficient dimension and capabilities to assure immunity to short duration power interruptions and the ability to shut down and restart in the event of a prolonged power outage.
• the local processor (440) based upon data processing commands received from the control center (120), may further filter messages. Messages are accumulated in temporary memory (RAM) in block files associated with the forward control channels associated with each receiver. Certain fields in the messages, such as portions of the subscriber MIN, are encrypted into unique subscriber identifier numbers that correlate to a specific subscriber using standard public/private key algorithms such as DES or MDS.
• Portions of the MIN corresponding to the area code are not encrypted into the unique subscriber identifier in order to gather data on roamers.
• Each data gathering node (110) will use the same public key to encrypt the MIN to simplify key management.
• the corresponding private key is held in escrow, and is unavailable to be used to decrypt the unique subscriber identifiers, thus protecting the privacy of subscribers.
• subscriber MINS are encrypted into unique subscriber identifiers at the receivers (440) rather than the local processor (440). At no time is an unencrypted MIN stored on a permanent media vulnerable to security breaches.
• the block file associated with the forward control channel is closed, compressed using a standard compression format such as ZIP to minimize storage and transmission requirements, and sent to a local storage device (445) such as a hard drive where it is appended to a file corresponding to its associated wireless communication network.
• a new block file is created which corresponds to the next block time period and forward control channel.
• each receiver (420) monitors the reverse control channel as well as the forward control channel. Demodulation and decoding of the reverse control channel signal yields a message corresponding to the ESN of the MSU.
• the ESN is encrypted to produce a unique subscriber identifier rather than the MIN gathered from the forward control channel.
• the unique RF wave pattern (RF finge ⁇ rint) transmitted by each MSU on the reverse control channel is used to assign a different unique subscriber identifier to each RF finge ⁇ rint.
• the messages pertaining to the MINs gathered from the forward control channel are filtered out with the extraneous information rather than encrypted.
• control center (120) On a scheduled basis, all files are closed and prepared for uploading to the control center (120). A file header is created for each wireless communication network file that summarizes the statistics of the corresponding file, including peg counts for each type of message recorded and a summary of any apparent alarm conditions or system outages detected during the reporting period.
• the control center (120) initiates a dialup routine to connect with the local processor (440), which sends the data file containing data gathered since the previous dialup.
• the local processor (440) may initiate the communications session based upon previously downloaded instructions and schedules from the control center (120).
• the connection between the control center (120) and the local processor (440) can be made via the internet, an RF link, or a wide area network.
• the control center (120) may also initiate a dialup routine to connect with the local processor (440) at other times to transmit any revised settings for the receivers to the local processor (440), such as frequency selection or sampling patterns.
• the local processor may also initiate a dialup routine to connect with the local processor (440) at other times to transmit any revised settings for the receivers to the local processor (440), such as frequency selection or sampling patterns.
• the local processor may also initiate a dialup routine to connect with the local processor (440) at other times to transmit any revised settings for the receivers to the local processor (440), such as frequency selection or sampling patterns.
• the control center (120) initiates a dialup routine once a day, at which time the local processor (440) sends the data file containing data gathered since the previous dialup and the control center (120) transmits any revised settings for the receivers to the local processor (440).
• the dialup linkage occurs landline via a standard telephone modem (450), but in an alternative embodiment the dialup linkage may be wireless.
• each data file received from a data gathering node is decompressed and the data file is error checked to ensure data integrity. This includes examining the timestamps of the first and last event in the data file and confirming that the data file is composed of data gathered during the expected time period since the previous data upload. Second, messages are sampled from the data file and compared to the messages in the corresponding positions in the previous day's data to confirm that the messages are not identical to the previous day's data. In an alternative embodiment, instead of error checking the data file against only the prior day's data file, the data file can be error checked against several previous days of data collected to ensure that new data has been collected. In the preferred embodiment, the first message, last message, and eight messages at equal intervals between the first and last messages are sampled, for a total of ten sampled messages.
• the data file is also checked to ensure that receivers have been functioning properly and gathering data throughout the collection period.
• Data files collected from previous collection periods are used to generate a normal range of occurrences for each type of message over a given collection period.
• the control center 120
• the uploaded data files from all the data gathering nodes (110) have been error checked, they are processed to generate a single file of messages. Since data is gathered from multiple data gathering nodes simultaneously, there may be undesired duplicate entries for the same event. This is particularly the case when multiple data gathering nodes are monitoring cells within the same location area.
• the desired data is that a particular MSU received a page at a particular time
• redundant page signals attempting to locate the MSU are undesirable and eliminated.
• the elimination of redundant pages significantly reduces both the space required to store the gathered data and the query time required when aggregating and analyzing the data.
• control center (120) sorts through all of the data files received from each data gathering node (110) and identifies pages to the same subscriber that were received at different data gathering nodes (110) within a predetermined window of time and selects the page with the earliest time stamp. The control center (120) then eliminates all other pages to the same subscriber within the predetermined window of time.
• the pre-determined window of time may vary depending on the wireless communication network standard from which the data is gathered, and may be refined from time to time based on prior data that has been gathered.
• a representation of the storage format for gathered data is shown in Figure 5.
• a userinformation table (510) is created for each event containing fields on the event date and time, area code of the subscriber, cell site location, event type, market frequency band, and carrier. Information pertaining to the event type, cell site location, market, frequency band, and carrier fields is represented in the userinformation table (510) by an indexed numerical value, with a corresponding relational table for cross-referencing each indexed numerical value with a text description of its meaning.
• Each type of event is designated an index value in the userinformation table (510), with a related eventType table (520) cross-referencing the index value with the type of event (page, traffic channel assignment, registrations).
• the cell site location of the event is designated an index value, with a relational cellinformation table (530) cross- referencing the index value with a text description of the cell site locations.
• Market, carrier, and frequency band information is allocated a single field in the userinformation table (510) and is designated a single index value.
• This index value is designated by a marketFrequency table (540), which contains individual fields for market, carrier, and frequency band information.
• the market is designated an index value in the marketFrequency table (540), with a relational market table (550) cross-referencing each index value with a text description of the corresponding market.
• both the carrier and frequency band are each designated an index value in the marketFrequency table (540), with a relational Carrier table (550) and frequencyBand table (560) cross-referencing the index values with a text description of the corresponding carrier and frequency band.
• All new data files received from all data gathering nodes (110) are backed up and archived on a regularly scheduled basis using the standard commercial features of a database management system. Standard archiving procedures are used to back up to an offline storage media such as magnetic tape or CD-ROM, which are then stored in a secure, fireproof safe.
• a data mining application is run on the data file to aggregate and analyze the data to produce summary tables and reports. The format of a sample table is shown in Figure 5.
• a user table (570) identifies each unique subscriber identification number and keeps track of the date the unique subscriber identification number first appeared and the date it last appeared. The user table (570) is updated each time a new file is processed.
• the data mining application is run at the control center (120).
• the data mining application can access the control center remotely to aggregate and analyze the data file stored at the control center (120).
• the data mining application generates periodic reports (monthly in the preferred embodiment) containing marketing and usage characterization information useful to wireless communication network service providers, wireless equipment manufacturers, service retailers, consultants, and financial institutions. Reports and gathered data can be provided through web-based access (including real time displays of data being gathered), e-mail delivery, electronic data delivery, or hard print.
• such information comprises the share of subscribers relative to other wireless communication networks, the number of new subscribers, the number of churning subscribers (subscribers who have dropped their service), and the share of total calls made by subscribers of a wireless communication network relative to subscribers of other wireless communication networks, the number and share of roamers for each wireless communication network, profiling of the quality of new,- existing, and churning subscribers for each wireless communication network based upon the share of subscriber calls made, share of calls during peak and non-peak hours for each wireless communication network, and share of incoming and outgoing calls for each wireless communication network.
• the data mining application may generate other types of marketing and usage characterization information, such as subscriber usage characteristics with respect to items such as number, time and location of calls made and received for each service provider, and use the number of gross adds for a wireless communication network following media advertising or special promotional pricing plans as a basis to track the impact and effectiveness of such advertising or special promotions. It is recognized that the data mining application can process the data files to generate many other types of marketing and usage characterization information similar to those described herein.
• Figures 6 and 7 are sample exce ⁇ ts of information contained in the data files stored at the control center (120) for illustration of how the data mining application generates information on market share of subscribers for each wireless communication network, the number and share of new subscribers and churning subscribers, the number and share of roamers, and share of total calls made by subscribers. Only data for two wireless communication networks is shown, but a complete file will have data for all wireless communication networks.
• Figure 6 represents exce ⁇ ts of data files containing data taken during a baseline period. This baseline period raw data is compared to actual data received from service providers for the time period corresponding to the baseline period to determine how much data was missed by the data gathering system.
• Figure 7 is a sample exce ⁇ t of a data file at the control center (120) that the data mining application is to analyze to generate marketing and usage characterization information.
• the data mining application calculates the subscriber share for each wireless communication network by first generating a subscriber share gross-up coefficient that accounts for data that is missed by the data gathering nodes (120).
• the data mining application sorts through the baselining period data and generates a list of different subscriber identifiers that are contained in the baselining period data. These different subscriber identifiers are compared to a list of actual subscriber identifiers generated from information received from service providers to determine how many subscribers the data gathering nodes missed. The comparison of these two lists is shown in Figure 8.
• Figure 10 shows one method for calculating the subscriber share gross-up coefficient for each wireless communication network. The actual number of subscribers during the baseline period is divided by the number of measured subscribers during the baseline period.
• more comprehensive standard statistical methods can be used to calculate gross-up coefficients.
• the data gathering nodes will miss subscriber identifiers because not all of the cells in the wireless communication network are monitored simultaneously and continuously. As a result, for example, an event may have started and ended in a cell before being recorded. However, this base number of subscribers approximates the actual number of subscribers because as the number of recorded events increases, the number of unique subscriber identifier numbers grows asymptotically to the actual level. Although a particular event may be missed, the more data that is recorded in the future, the more likely future events involving the same unique subscriber identifiers may be recorded. Only one record is required to identify that a subscriber is active. Where the data collection period is a month, a sufficient number of events have been recorded to approach this asymptotic level. These additional factors may be taken into account when generating the gross-up coefficients for each wireless communication network.
• the data mining application sorts the data file and counts the number of different subscriber identifiers for each wireless communication network.
• Figure 9 is the result of such a sort of the file shown in Figure 7.
• the number of subscribers counted for each wireless communication network is multiplied by the corresponding subscriber share gross-up coefficient to generate an extrapolated number of subscribers for each wireless communication network.
• the market share of each wireless communication network is then generated by dividing the extrapolated number of subscribers for each wireless communication network by the total number of extrapolated subscribers for all wireless communication networks.
• Figure 10 shows the extrapolation of the number of subscribers counted for each wireless communication network shown in Figure 9 and the calculation of the subscriber share for each wireless communication network.
• a sample report containing information on subscriber share over a monthly period is shown in Figure 1 1.
• Figure 12 is a sample exce ⁇ t of data for a particular time period in which it is desired to determine the number of new and churning subscribers since a prior collection period.
• the data mining application calculates the number of new subscribers added by over a particular time period by each wireless communication network by comparing a list of unique subscriber identifiers of the particular time period to a list of unique subscriber identifiers of a prior time period and counting the number of unique subscriber identifiers that do not appear in the prior period.
• the number of churning subscribers for each wireless communication network who discontinued their service at the end of a prior time period is calculated by comparing a list of the unique subscriber identifiers of the particular time period to a list of unique subscriber identifiers of the prior time period and counting the number of unique subscriber identifiers that appear in the prior time period, but not the particular time period.
• Figure 13 shows this process for a list of unique subscriber identifiers sorted from a prior time period data file shown in Figure 7 and a list of unique subscriber identifiers from the particular time period shown in Figure 12.
• the number of new subscribers for each wireless communication network counted in Figure 13 is multiplied by a corresponding new subscriber gross-up coefficient to generate an extrapolated number of new subscribers for each wireless communication network.
• the new subscriber gross-up coefficient can be calculated in many ways. In the presently preferred embodiment, the subscriber share gross-up coefficient calculated previously is used since the number of new subscribers missed by the data gathering nodes is proportional to the number of subscribers missed.
• the share of new subscribers for each wireless communication network is calculated by dividing the extrapolated number of new subscribers for each wireless communication network by the total number of extrapolated subscribers for all wireless communication networks.
• Figure 14 shows the extrapolation of the number of new subscribers counted for each wireless communication network shown in Figure 13 and the calculation of the new subscriber share for each wireless communication network.
• Identifying the new subscribers for each wireless communication network can also be used to track subscribers who have left one service provider for another once Local
• Number Portability is mandated in the year 2001. Under Local Number Portability, subscribers can keep their MIN when switching from one service provider to another. To identify a subscriber who switched from one service provider to another, for each new subscriber, the data mining application sorts through the list of unique subscriber identifiers for each other service provider to see if the new unique subscriber identifier appears previously as a subscriber to a different service provider. If so. this subscriber is identified as a subscriber who has switched from another service provider. In this manner,.the data mining application can generate reports on the number of subscribers who switched from one service provider to another, and which service providers lost and gained subscribers from which other service providers.
• the ability to track new subscribers and new subscribers coming from other service providers can also be used to quantify the effectiveness of media campaigns by service providers, such as tracking the increase in subscribers per advertising dollar spent, relative effectiveness of television, radio, and print medium advertisements, and effectiveness of particular types of promotional plans, such as free air time or lack of roaming charges.
• Figure 15 shows the extrapolation of the number of churning subscribers counted for each wireless communication network shown in Figure 13 and the calculation of the churning subscriber share for each wireless communication network.
• Figures 16 and 17 show sample reports containing information on new and churning subscribers over a monthly period.
• the data mining application calculates the roamer share for each wireless communication network by first generating a roamer gross-up coefficient that accounts for data that is missed by the data gathering nodes (120).
• the data mining application sorts through the baseline period raw data shown in Figure 6 and generates a list of roamers by identifying area codes outside the wireless communication network for which data is being gathered. This list of roamers is compared to a list of actual roamers generated from information received from service providers to determine how many roamers the data gathering nodes missed. The comparison of these two lists is shown in Figure 18.
• a roamer gross-up coefficient is calculated by dividing the number of actual roamers by the measured number of roamers. The calculation of the roamer gross-up coefficient from the data in Figure 18 is shown in Figure 20.
• the data mining application calculates the number and share of roamers over a particular period for each wireless communication network by sorting the data file for the particular period and counting the number of roamers.
• Figure 19 shows the result of such a data sort of the data file shown in Figure 7.
• the number of roamers counted for each wireless communication network is multiplied by the corresponding roamer gross-up coefficient for each wireless communication network to generate an extrapolated number of roamers for each wireless communication network.
• the share of roamers for each wireless communication network is then generated by dividing the extrapolated number of subscribers for each wireless communication network by the total number of extrapolated roamers for all wireless communication networks.
• Figure 20 shows the extrapolation of the number of roamers counted for each wireless communication network shown in Figure 19 and the calculation of the roamer share for each wireless communication network.
• a sample report showing roamer activity over a monthly period is shown in Figure 21.
• the data mining application calculates the call share for each wireless communication network by first generating a call share gross-up factor that accounts for data that is missed by the data gathering nodes (120).
• the data mining application sorts through the baselining period raw data shown in Figure 6 and generates a list counting the number of traffic channel assignments made, which correspond to subscriber calls. This data sort is compared to a list of actual traffic channel assignments generated from data received from service providers to determine how many calls the data gathering nodes missed. The comparison of these two lists is shown in Figure 22.
• the call share gross-up coefficient is calculated by dividing the number of actual traffic channel assignments made by the measured number of traffic channel assignments made.
• the number of traffic channel assignments made for each wireless communication network is multiplied by the corresponding call share gross-up coefficient for each wireless communication network to generate an extrapolated number of traffic channel assignments for each wireless communication network.
• the share of calls made for each wireless communication network is generated by dividing the extrapolated number of traffic channel assignments made for each wireless communication network by the total number of extrapolated traffic channel assignments for all wireless communication networks.
• Figure 24 shows the extrapolation of the number of traffic channel assignments made for each wireless communication network and the calculation of call share for each wireless communication network.
• a sample report containing information on caller share over a monthly period is shown in Figure 25.
• the data mining application also processes the data files to generate information on the quality of the base, new, and churning subscribers for each wireless communication network based upon the number of calls each subscriber of each wireless communication network makes. Since service provider revenues are directly tied to the number of calls made, information regarding quality in addition to number of subscribers is highly desirable.
• Figure 26 is a sample exce ⁇ t of a data file stored at the control center (120) for illustration of how the data mining application generates information on the quality of base subscribers for each wireless communication network. Only data for two wireless communication networks is shown, but a complete file will have data for all wireless communication network.
• the data mining application calculates the quality of base subscribers over a particular period by sorting the data file for the particular period, identifying each different subscriber for each wireless communication network, and counting the number of traffic channel assignments made to each different subscriber.
• Figure 27 shows such a data sort of the file exce ⁇ t shown in Figure 26. This data is further sorted by number of traffic channel assignments to produce a list of subscribers with the highest number of traffic channel assignments listed first and the subscribers with the lowest number of traffic channel assignments listed last.
• Figure 28 shows such a data sort of the data found in Figure 27. In the presently preferred embodiment, this list is divided into quartiles based on the number of traffic channel assignments made. For each quartile, the number of subscribers for each wireless communication network is counted.
• Figure 29 shows such a data sort of the data found in Figure 28.
• the market share percentage of each wireless communication network is calculated by dividing the number of subscribers for each wireless communication network by the total number of subscribers for all wireless communication networks in the quartile.
• Figure 30 shows sample market share calculations for base subscribers for the data found in Figure 29.
• Figures 31 and 32 are sample exce ⁇ ts of data files stored at the control center (120) for illustration of how the data mining application generates information on the quality of new and churning subscribers of each wireless communication network.
• the data shown in Figure 32 is taken at a particular time period later than Figure 31.
• the data mining application first identifies which subscribers are new subscribers and which subscribers are churning using a similar process to that described earlier when determining the number and share of added and churning subscribers.
• the data mining application identifies the number of new subscribers added by over a particular time period by each wireless communication network by comparing a list of unique subscriber identifiers of the particular time period to a prior time period and counting the number of unique subscriber identifiers that do not appear in the prior period.
• the number of subscribers for each wireless communication network who discontinued their service at the end of a prior time period is approximated by comparing a list of the unique subscriber identifiers of the particular time period to the prior time period and counting the number of unique subscriber identifiers that appear in the prior time period, but not the present time period.
• Sample data exce ⁇ ted from a particular time period in which it is desired to calculate the number of new and dropped subscribers is shown in Figure 32.
• the data mining applications sorts through the raw data of Figure 32 and generates a list of different subscriber identifiers for each wireless communication network.
• the data mining application then sorts through the raw data of a prior time period, shown in Figure 31 , and generates a list of different subscribers for each wireless communication network. These lists are compared to identify the new subscribers appearing in the particular time period but not the prior time period and to identify the churning subscribers appearing in the prior time period, but not the particular time period. The comparison of these two lists is shown in Figure 33.
• the data mining application re-sorts the data in Figure 32, counting the number of traffic channel assignments made to the new subscribers identified from the data in Figure
• Figure 34 shows such a data sort of the data found in Figure 34. In the presently preferred embodiment, this list is divided into quartiles based on the number of traffic channel assignments made. For each quartile, the number of new subscribers for each wireless communication network is counted.
• Figure 36 shows such a data sort of the data found in Figure 35.
• the market share percentage of new subscribers for each wireless communication network is calculated by dividing the number of new subscribers for each wireless communication network by the total number of new subscribers for all wireless communication networks in the quartile.
• Figure 37 shows sample market share percentage calculations for new subscribers for the data found in Figure 36.
• the data mining application re-sorts the data in Figure 31 , counting the number of traffic channel assignments made to the churning subscribers identified from the data in Figure 33 for each wireless communication network. The results of this data sort are shown in Figure 38.
• This data is further sorted by number of traffic channel assignments to produce a list of churning subscribers with the highest number of traffic channel assignments listed first and the new subscribers with the lowest number of traffic channel assignments listed last.
• Figure 39 shows such a data sort of the data found in Figure 38. In the presently preferred embodiment, this list is divided into quartiles based on the number of traffic channel assignments made. For each quartile, the number of churning subscribers for each wireless communication network is counted.
• Figure 40 shows such a data sort of the data found in Figure 39.
• the market share percentage of churning subscribers for each wireless communication network is calculated by dividing the number of churning subscribers for each wireless communication network by the total number of churning subscribers for all wireless communication networks in the quartile.
• Figure 41 shows sample market share percentage calculations for churning subscribers for the data found in Figure 40.
• the data mining application calculates the peak and non-peak call share for each wireless communication network by first generating a peak/non-peak call share gross-up factor that accounts for data that is missed by the data gathering nodes (120). This process is the same as that performed in calculating the call share gross-up factor shown in Figure 24 since the proportion of calls missed during peak and non-peak periods is proportional to the number of calls missed.
• the data mining application sorts through the baselining period raw data shown in Figure 6 and generates a list counting the number of traffic channel assignments made, which correspond to subscriber calls. This data sort is compared to a list of actual traffic channel assignments generated from data received from service providers to determine how many calls the data gathering nodes missed. The comparison of these two lists is shown in Figure 43.
• the peak/non-peak call share gross-up coefficient is calculated by dividing the number of actual traffic channel assignments made by the measured number of traffic channel assignments made.
• the number of traffic channel assignments made during peak and non-peak hours for each wireless communication network is multiplied by the corresponding peak/non- peak call share gross-up coefficient for each wireless communication network to generate an extrapolated number of traffic channel assignments during peak and non-peak hours for each wireless communication network.
• the share of calls made during peak and non-peak hours for each wireless communication network is generated by dividing the extrapolated number of peak and non-peak traffic channel assignments made for each wireless communication network by the total number of extrapolated peak and non-peak traffic channel assignments for all wireless communication networks.
• Figure 42 is a sample exce ⁇ t of a data file from which peak and non-peak call information is to be generated.
• Figure 44 shows the data sort of Figure 42 counting the number of peak and non-peak traffic channel assignments made for each wireless communication network.
• Peak hours are defined to be between the hours of 07:00 and 19:00 whereas non-peak hours are defined to be between the hours of 19:00 and 07:00.
• Figure 45 shows the extrapolation of the number of traffic channel assignments made during peak and non-peak hours for each wireless communication network and the calculation of peak and non-peak call share for each wireless communication network. Also shown in Figure 45 is the calculation of peak and non-peak call share for each wireless communication network excluding all roamer
• the data mining application calculates the incoming and outgoing call share for each wireless communication network by first generating an incoming/outgoing call share gross-up coefficient that accounts for data that is missed by the data gathering nodes (120). This incoming/outgoing call share gross-up coefficient is equal to the value of the call share and peak/non-peak call share gross-up coefficient.
• Figure 46 is a sample excerpt of a data file from which incoming and outgoing call share information is to be generated.
• the data mining application sorts the data file shown in Figure 46 and identifies the traffic channel assignments and pages for each wireless communication network. To identify which traffic channel assignments are incoming calls and which are outgoing calls, the data mining application sorts the file shown in Figure 47, identifying which traffic channel assignments are immediately preceded within 2 to 4 seconds by a page to the same subscriber. These traffic channel assignments are classified as incoming calls, and all other traffic channel assignments are classified as outgoing. Any traffic channel assignments during the first three seconds of the file data gathering period are not considered because it is unknown whether there was a corresponding page to these traffic channel assignments. The results of this data sort identifying incoming and outgoing calls are shown in Figure 48.
• the number of incoming and outgoing call traffic channel assignments made during peak and non-peak hours for each wireless communication network is multiplied by the corresponding incoming/outgoing call share gross-up coefficient for each wireless communication network to generate an extrapolated number of incoming and outgoing call traffic channel assignments for each wireless communication network.
• the share of incoming and outgoing calls for each wireless communication network is generated by dividing the extrapolated number of incoming and outgoing calls for each wireless communication network by the total number of incoming and outgoing calls for all wireless communication networks.
• Figure 49 shows the extrapolation of the number of incoming and outgoing call traffic channel assignments for each wireless communication network and the calculation of incoming and outgoing call share for each wireless communication network. Also shown in Figure 49 is the calculation of incoming and outgoing call share for each wireless communication network excluding all roamer calls.
• Dual mode cell phones are often issued to subscribers which can operate on either the analog or digital networks.
• the forward control channel either assigns a digital or analog traffic channel, depending on network capacity at the time of the call.
• Dual mode users can be identified from the gathered data since traffic channel assignments for the same subscriber will be present in both the analog and digital wireless communication networks for a service provider.
• the data mining application can determine the share of analog and digital traffic channel assignments made to a subscriber by counting the number of analog and digital traffic channel assignments made and dividing each by the total number of traffic channel assignments made.
• the data gathered and the market share and usage characterization information generated by the data mining application of the present invention has many practical applications for wireless communication network service providers.
• the information allows service providers to (1) tailor their sales and marketing expenditures toward this customer base, (2) evaluate the success of pricing, promotions, and advertising programs introduced within a market, (3) quantitatively compare themselves to competitors, and (4) better understand the utilization patterns of the customer base within a region, such as at new cell site locations. On a regional or national level, comparisons can be made between competing service providers on a broader geographic basis encompassing several cities, or to track usage in new PCS markets or high subscriber growth cities.
• the area code and first three digits of subscriber MINS can be correlated to zip codes, thus allowing the number of existing, churning, and new subscribers in each zip code of a region for each service provider to be determined. This allows each service provider to identify areas in which they are weak and where additional sales and marketing efforts are required. Population demographics based on zip codes can be used to characterize and understand the demographics of the subscribers for each service provider, thus enabling service providers to design their sales and marketing efforts with particular demographic groups in mind.
• the service provider can decrypt the unique subscriber identifiers into subscriber
• the data gathered by the present invention on a particular service provider can then be utilized by that service provider in a variety of ways.
• a service provider can compare the gathered data to their internal records to verify proper billing and identify and track fraud.
• the present invention can also be used by service providers in conducting functionality tests of their systems, such as whether pages are being properly sent by a network.
• the data gathered from the present information can also be used by service providers to track individual subscriber usage patterns, such as time, day, and location of calls. Individual subscriber usage can also be tracked over a period of time to provide a profile and identify trends in usage. For example, usage can be tracked by season to identify seasonal trends, or drastic changes in usage by a subscriber can be identified. Tracking individual subscriber usage patterns allows service providers to identify or generate pricing plans most appropriate for an individual subscriber, thereby enhancing subscriber satisfaction and retention.

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