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• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04W—WIRELESS COMMUNICATION NETWORKS
  • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
  • H04W16/22—Traffic simulation tools or models
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04W—WIRELESS COMMUNICATION NETWORKS
  • H04W24/00—Supervisory, monitoring or testing arrangements
  • H04W24/02—Arrangements for optimising operational condition
• • 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
  • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
  • H04W16/18—Network planning tools

Definitions

• a telecommunications network is established via a complex arrangement and configuration of many cell sites that are deployed across a geographical area.
• cell sites e.g., macro cells, microcells, and so on
• a specific geographical location such as a city, neighborhood, and so on.
• GSM Global System for Mobile
• CDMA/TDMA code/time division multiple access
• 3G/4G 3G/4G
• the devices can seek access to the telecommunications network for various services provided by the network, such as services that facilitate the transmission of data over the network and/or provide content to the devices.
• FIG. 1 is a block diagram illustrating a suitable computing environment within which to identify optimum network performance improvement solutions within a telecommunications network.
• FIG. 2 is a block diagram illustrating the components of the optimum capacity solution system.
• FIGS. 3A-3F are example of data accessed/received/collected by the historical data module.
• FIGS. 4A-4B illustrate scores generated for various sites in a geographic area.
• FIGS. 5A-5B are flow diagrams illustrating a process of identifying optimum network performance improvement solution at a location in a telecommunications network.
• FIGS. 6A-6B illustrate examples of generating clusters for a set of network improvement solutions records associated with a geographic area.
• An aim of a telecommunications service provider is to minimize customer experience degradation. This is typically achieved by deploying congestion management and/or network improvement solutions at one or more cell sites.
• congestion management and/or network improvement solutions have been suggested to address and resolve the degradation issues.
• different capacity planning solutions have been suggested to address and resolve the degradation issues.
• the process for identifying which capacity planning solutions to deploy to alleviate network congestion and/or improve capacity is more of a trial and error process. This results in inefficiencies as well as wasted costs as telecommunications service providers try (and fail) deploying sub-optimum capacity planning solutions that are not tailored to the particular location of network traffic usage and congestion.
• optimum capacity solution system an optimum capacity composite gain system and related method to identify optimum capacity planning solutions to improve telecommunications network performance for a particular location.
• One purpose of the optimum capacity solution system is to summarize complex, multi-dimensional indicators to support decision making by wireless telecommunication service providers on changes that may be needed to infrastructure repair, modification, planning and development.
• the optimum capacity solution system does this by analyzing data related to capacity planning solutions deployed at specific locations (e.g., historical data), learning from this data by creating clusters, and applying classification techniques to determine optimum capacity planning solutions capable of being deployed at a new location.
• a telecommunications service provider is able to efficiently and economically identify targeted solutions and locations to expand capacity of cell sites and improve customer experiences.
• the optimum capacity solution system uses historical data comprising capacity gain solutions and their associated gains at various locations to train a machine learning model.
• the trained machine learning model then, upon receiving a new location (e.g., latitude and longitude coordinates), recommends the top n (e.g., the top 3) solutions that should be deployed at the new location to improve telecommunications network performance.
• the machine learning model uses clustering techniques to perform the recommendations.
• the optimum capacity solution system builds/accesses a dataset comprising information of previously deployed capacity improvement solutions, such as gain, cost, location, duration, and solution type. It then performs clustering on the dataset per market for each solution to categorize solutions within an area.
• the optimum capacity solution system finds the nearest cluster and shows the top n solutions in the cluster.
• FIG. 1 is a block diagram illustrating a suitable computing environment 100 within which to select optimum network performance improvement solutions within a telecommunications network.
• One or more user devices 110 such as mobile devices or user equipment (UE) associated with users (such as mobile phones (e.g., smartphones), tablet computers, laptops, and so on), Internet of Things (IoT) devices, devices with sensors, and so on, receive and transmit data, stream content, and/or perform other communications or receive services over a telecommunications network 130 , which is accessed by the user device 110 over one or more cell sites 120 , 125 .
• UE user equipment
• IoT Internet of Things
• the user device 110 can access a telecommunication network 130 via a cell site at a geographical location that includes the cell site, in order to transmit and receive data (e.g., stream or upload multimedia content) from various entities, such as a content provider 140 , cloud data repository 145 , and/or other user devices 155 on the network 130 and via the cell site 120 .
• data e.g., stream or upload multimedia content
• entities such as a content provider 140 , cloud data repository 145 , and/or other user devices 155 on the network 130 and via the cell site 120 .
• the cell sites may include macro cell sites 120 , such as base stations, small cell sites 125 , such as picocells, microcells, or femtocells, and/or other network access component or sites (including IEEE 802.11 WLAN access points).
• the cell sites 120 , 125 can store data associated with their operations, including data associated with the number and types of connected users, data associated with the provision and/or utilization of a spectrum, radio band, frequency channel, and so on, provided by the cell sites 120 , 125 , and so on.
• the cell sites 120 , 125 can monitor their use, such as the provisioning or utilization of PRBs provided by a cell site physical layer in LTE network. For example, a cell site 120 having a channel bandwidth of 5 MHz that provides 25 available physical resource blocks through which data can be transmitted to/from the user device 110 .
• the telecommunications network 130 can monitor and/or measure the operations and transmission characteristics of the cell sites 120 , 125 and other network access components.
• the telecommunications network 130 can provide a network monitoring system, via a network resource controller (NRC) or network performance and monitoring controller, or other network control component, in order to measure and/or obtain the data associated with the utilization of cell sites 120 , 125 when data is transmitted within a telecommunications network.
• NRC network resource controller
• network performance and monitoring controller or other network control component
• the computing environment 100 includes an optimum capacity solution system 150 configured to monitor aspects of the network 130 based on, for example, data received from the network monitoring system.
• the optimum capacity solution system 150 can evaluate and select optimum network performance improvement solutions to be deployed at cell sites to improve their performance as described in detail below.
• FIG. 1 and the discussion herein provide a brief, general description of a suitable computing environment 100 in which the optimum capacity solution system 150 can be supported and implemented. Although not required, aspects of the optimum capacity solution system 150 are described in the general context of computer-executable instructions, such as routines executed by a computer, e.g., mobile device, a server computer, or personal computer.
• a computer e.g., mobile device, a server computer, or personal computer.
• the system can be practiced with other communications, data processing, or computer system configurations, including: Internet appliances, hand-held devices (including tablet computers and/or personal digital assistants (PDAs)), Internet of Things (IoT) devices, all manner of cellular or mobile phones, multi-processor systems, microprocessor-based or programmable consumer electronics, set-top boxes, network PCs, mini-computers, mainframe computers, and the like.
• PDAs personal digital assistants
• IoT Internet of Things
• computer host
• host computer and “mobile device” and “handset” are generally used interchangeably herein, and refer to any of the above devices and systems, as well as any data processor.
• aspects of the system can be embodied in a special purpose computing device or data processor that is specifically programmed, configured, or constructed to perform one or more of the computer-executable instructions explained in detail herein.
• aspects of the system can also be practiced in distributed computing environments where tasks or modules are performed by remote processing devices, which are linked through any communications network, such as a Local Area Network (LAN), Wide Area Network (WAN), or the Internet.
• LAN Local Area Network
• WAN Wide Area Network
• program modules can be located in both local and remote memory storage devices.
• aspects of the system can be stored or distributed on computer-readable media (e.g., physical and/or tangible non-transitory computer-readable storage media), including magnetically or optically readable computer discs, hard-wired or preprogrammed chips (e.g., EEPROM semiconductor chips), nanotechnology memory, or other data storage media.
• computer implemented instructions, data structures, screen displays, and other data under aspects of the system can be distributed over the Internet or over other networks (including wireless networks), on a propagated signal on a propagation medium (e.g., an electromagnetic wave(s), a sound wave, etc.) over a period of time, or they can be provided on any analog or digital network (packet switched, circuit switched, or other scheme).
• Portions of the system reside on a server computer, while corresponding portions reside on a client computer such as a mobile or portable device, and thus, while certain hardware platforms are described herein, aspects of the system are equally applicable to nodes on a network.
• the mobile device or portable device can represent the server portion, while the server can represent the client portion.
• the user device 110 and/or the cell sites 120 , 125 can include network communication components that enable the devices to communicate with remote servers or other portable electronic devices by transmitting and receiving wireless signals using a licensed, semi-licensed, or unlicensed spectrum over communications network, such as network 130 .
• the communication network 130 can be comprised of multiple networks, even multiple heterogeneous networks, such as one or more border networks, voice networks, broadband networks, service provider networks, Internet Service Provider (ISP) networks, and/or Public Switched Telephone Networks (PSTNs), interconnected via gateways operable to facilitate communications between and among the various networks.
• ISP Internet Service Provider
• PSTNs Public Switched Telephone Networks
• the telecommunications network 130 can also include third-party communications networks such as a Global System for Mobile (GSM) mobile communications network, a code/time division multiple access (CDMA/TDMA) mobile communications network, a 3rd or 4th generation (3G/4G) mobile communications network (e.g., General Packet Radio Service (GPRS/EGPRS)), Enhanced Data rates for GSM Evolution (EDGE), Universal Mobile Telecommunications System (UMTS), or Long Term Evolution (LTE) network), 5G mobile communications network, IEEE 802.11 (WiFi), or other communications networks.
• GSM Global System for Mobile
• CDMA/TDMA code/time division multiple access
• 3G/4G 3rd or 4th generation
• 3G/4G 3G/4G mobile communications network
• GPRS/EGPRS General Packet Radio Service
• EDGE Enhanced Data rates for GSM Evolution
• UMTS Universal Mobile Telecommunications System
• LTE Long Term Evolution
• WiFi IEEE 802.11
• FIG. 2 is a block diagram illustrating the components of the optimum capacity solution system 150 .
• the optimum capacity solution system 150 can include functional modules that are implemented with a combination of software (e.g., executable instructions, or computer code) and hardware (e.g., at least a memory and processor).
• a module is a processor-implemented module or set of code, and represents a computing device having a processor that is at least temporarily configured and/or programmed by executable instructions stored in memory to perform one or more of the specific functions described herein.
• the optimum capacity solution system 150 can include a historical data module 210 , a clustering module 220 , a ranking module 230 , and an optimum solution selection module 240 , each of which is discussed separately below.
• the historical data module 210 is configured and/or programmed to generate/access/receive/collect a set of network improvement solutions records for one or more locations over a period of time (e.g., daily, weekly, monthly, quarterly, yearly, etc.) (which can be stored in the capacity solution database 255 ).
• the set of network improvement solutions records comprises information about one or more of the following parameters: location (e.g., latitude/longitude), market(s) associated with the location, network performance improvement solution(s) deployed at the location, and an associated gain profile for the deployed network performance improvement solutions.
• the set of network improvement solutions records comprises information about one or more of the following solutions metrics associated with the deployed network performance improvement solutions: gain index, gain measures, time to deploy solution, lead time to deploy solution, cost to deploy solution, cost to maintain solution, total cost of solution, expected lifetime of solution, average median income, user demographics (e.g., age, income, crime statistics, occupation, education level, ethnicity, and so on), duration of gain to customers, change in customers after deploying solution, change in revenue after deploying solution, change in sales after deploying solution, traffic, number of users, Physical Resource Block (PRB) utilization, Channel Quality Indicator (CQI), throughput, carrier aggregation, advanced Quadrature Amplitude Modulation (QAM), duration of deploying the network performance improvement solution, lifetime of the network performance improvement solution, efficacy of the network performance improvement solution, location of the telecommunications network site, lease information of the telecommunications network site, duration of deployment of the network performance improvement solution, entitlements and permits required to deploy the network performance improvement solution, tower height, nearest available
• the term market is used to denote a geographic area, such as a portion (or all) of a city, town, state, country, other similar geographic construct (e.g., Pacific Northwest, Southeast, etc.), and so on. Each market can be associated with a group of locations (e.g., latitude/longitude pairs).
• the historical data module 210 collects capacity gain measure values for a set of key performance indicators (KPIs) for both before and after deployment of network improvement solutions at locations.
• KPIs include, but are not limited to traffic, number of users, PRB utilization, CQI, and throughput.
• the historical data module 210 collects three years of capacity gain measure values for the selected KPIs as well as three years of each solution's lead time, cost for each market, and location.
• FIGS. 3A-3F are example of data generated/accessed/received/collected by the historical data module 210 .
• the clustering module 220 is configured and/or programmed to generate market clusters for each solution to categorize solutions based on one or more solutions metrics.
• the clustering module 220 selects, for a market, a set of sites to consider for performing the clustering analysis. For example, the clustering module 220 selects the sites whose locations fall within the geographic boundaries of the market (e.g., North Seattle, Midtown New York City, Florida, etc.).
• the clustering module 220 can compute a score/priority for sites in the market using weights based on one or more of the following parameters: traffic, unique users, count of superphones (e.g., phone with certain characteristics such as release dates), revenue, throughput, duration of congestion, cost of lease/rent, median income, average household income, location of site, cost of hardware installed, age demographic distribution, and so on.
• the parameter values can be of different time granularities, such as weekly, monthly, quarterly, one time, and so on.
• FIGS. 4A and 4B illustrate scores generated for various sites in markets.
• the site scores can be used to rank the sites in the market, and the clustering module 220 can select the top n ranked sites when performing the clustering analysis discussed below.
• the clustering module 220 can score and rank sites in different markets using different parameter sets.
• the clustering module 220 After identifying the set of sites to consider for the clustering analysis, the clustering module 220 generates, for each market, clusters of network improvement solutions records for the market.
• the clustering module can use techniques, such as k-means clustering, to generate the clusters.
• the clusters can be generated based on a group of locations associated with the market and/or values of one or more of the solution metrics discussed above.
• the clustering module 220 can select a subset of the solution metrics discussed above based on solution metrics selection criteria, such as user selection, output optimization criteria (e.g., expected gain, length of solution to deploy, best solution, desired location characteristics, and so on), correlation between solution metric and efficacy of solution/gain measures, top n KPIs, and so on.
• solution metrics selection criteria such as user selection, output optimization criteria (e.g., expected gain, length of solution to deploy, best solution, desired location characteristics, and so on), correlation between solution metric and efficacy of solution/gain measures, top n KPI
• FIGS. 6A-6B illustrate examples of generating six clusters, along with several cluster attributes (e.g., gain, lead time, and best solution) for a set of network improvement solutions records associated with a geographic area 605 .
• cluster attributes e.g., gain, lead time, and best solution
• the ranking module 230 is configured and/or programmed to rank clusters and/or network performance improvement solutions in the generated clusters based on one or more of the following cluster ranking parameters: spectrum, duration, location, gain, cost to deploy solution, and so on.
• FIG. 6B illustrates a table 610 depicting a set of clusters associated with a geographic area, ranked according to their respective gains.
• the optimum solution selection and ranking module 240 is configured and/or programmed to select and recommend one (or more) network performance improvement solutions to deploy at particular locations/sites.
• network performance improvement solutions include, but are not limited to adding spectrum, removing spectrum, adding a proximate cell site, removing a proximate cell site, displacing a proximate cell site, adding or enhancing at least one technology capability, cell split, small cell deployment, sector addition, sector removal, sector capacity enhancement, cell on wheels addition, cell on wheel removal, tower addition, tower removal, hot spots addition, hot spots removal, capacity modification, and so on.
• the optimum solution selection and ranking module 240 receives a candidate location at which a user desires to deploy network performance improvement solution(s). For example, the optimum capacity solution system 150 can receive a user selection of a location via a user interface (e.g., a user can enter a location name, latitude/longitude, click on a location on a map, etc.). Upon receiving a desired location details, the optimum solution selection and ranking module 240 identifies a prioritized set of network performance improvement solutions capable of being deployed at the received location. The optimum solution selection and ranking module 240 identifies the prioritized set of network performance improvement solutions based on values of a set of prioritization parameters and the generated clusters.
• a user interface e.g., a user can enter a location name, latitude/longitude, click on a location on a map, etc.
• the set of prioritization parameters comprises one or more of the following: gain index, gain measures, time to deploy solution, lead time to deploy solution, cost to deploy solution, cost to maintain solution, total cost of solution, expected lifetime of solution, average median income, user demographics (e.g., age, income, crime statistics, occupation, education level, ethnicity, and so on), duration of gain to customers, change in customers after deploying solution, change in revenue after deploying solution, change in sales after deploying solution, traffic, number of users, Physical Resource Block (PRB) utilization, Channel Quality Indicator (CQI), throughput, carrier aggregation, advanced Quadrature Amplitude Modulation (QAM), duration of deploying the network performance improvement solution, lifetime of the network performance improvement solution, efficacy of the network performance improvement solution, location of the telecommunications network site, lease information of the telecommunications network site, duration of deployment of the network performance improvement solution, entitlements and permits required to deploy the network performance improvement solution, tower height, nearest available site, population served by the telecommunications network site, households served by
• the optimum solution selection and ranking module 240 identifies one or more candidate markets such that a group of locations associated with the candidate market are closest to the candidate location in light of the received prioritization parameters. Closeness between the group of locations associated with a candidate market and the candidate location can be determined based on closeness metrics, such as geographic distance, similarity between location characteristics, and so on. For example, similarity between location characteristics can be determined based on location similarity factors, such as demographics of users associated with a location, location type (e.g., urban, suburban, rural, etc.), legal regulations associated with the location, location coverage area, points of interest at the location, and so on.
• closeness metrics such as geographic distance, similarity between location characteristics, and so on.
• similarity between location characteristics can be determined based on location similarity factors, such as demographics of users associated with a location, location type (e.g., urban, suburban, rural, etc.), legal regulations associated with the location, location coverage area, points of interest at the location, and so on.
• the optimum capacity solution system 150 receives a candidate location selection from a user (e.g., when a user enters a latitude/longitude of a location and/or selects a desired location on a map).
• the optimum solution selection and ranking module 240 identifies one or more clusters based on the following characteristics: nearest site with previous solution, gains measured, time to deploy solution, cost of solution, best solution, and other measured KPIs.
• the optimum solution selection and ranking module 240 can predict based on the ranked solutions in the identified clusters (which are based on historical data), a prioritized set of network performance improvement solutions (e.g., comprising the top n solutions in an identified cluster) and their expected gain, time to deploy solution, and so on.
• a prioritized set of network performance improvement solutions e.g., comprising the top n solutions in an identified cluster
• the optimum solution selection and ranking module 240 computes a rank value for each network performance improvement solution in the prioritized set of network performance improvement solutions based on the values of the set of prioritization parameters. The optimum solution selection and ranking module 240 can then select and/or implement, at the candidate location, an optimum network performance improvement solution from the prioritized set of network performance improvement solutions based on the computed rank values.
• FIG. 5A is a flow diagram illustrating a process 500 of identifying optimum network performance improvement solution at a location in a telecommunications network.
• Process 500 begins at block 505 where it accesses a set of network improvement solutions records for multiple locations.
• Each record in the set of network improvement solutions records comprises information about a location, at least one network performance improvement solution deployed at the location, and an associated gain profile for the at least one network performance improvement solution.
• process 500 for each market in a set of markets, wherein each market is associated with a group of locations, process 500 : generates a cluster of network improvement solutions records for the market based on the group of locations associated with the market and solutions metric values associated with network performance improvement solutions deployed at each of the group of locations associated with the market (block 510 ); and ranks network performance improvement solutions in the cluster based on, for example, one or more of the following cluster ranking parameters: spectrum, duration, location, and cost to deploy solution (block 515 ). In some implementations, process 500 ranks the clusters for a market based on one or more metrics, such as gain, lead time, best solution, and so on.
• metrics such as gain, lead time, best solution, and so on.
• process 500 receives a candidate location to identify a prioritized set of network performance improvement solutions capable of being deployed at the candidate location.
• process 500 receives a set of prioritization parameters. Using the candidate location and the set of prioritization parameters, at block 535 , process 500 identifies a prioritized set of network performance improvement solutions capable of being deployed at the candidate location.
• process 500 computes a rank value for each network performance improvement solution in the prioritized set of network performance improvement solutions based on, for example, the values of the set of prioritization parameters.
• process 500 selects and/or implements, at the candidate location, an optimum network performance improvement solution selected from the prioritized set of network performance improvement solutions based on the computed rank values.
• FIG. 5B is a flow diagram illustrating a process 550 of identifying optimum network performance improvement solution at a location in a telecommunications network.
• the words “comprise,” “comprising,” and the like are to be construed in an inclusive sense, as opposed to an exclusive or exhaustive sense; that is to say, in the sense of “including, but not limited to.”
• the terms “connected,” “coupled,” or any variant thereof means any connection or coupling, either direct or indirect, between two or more elements; the coupling of connection between the elements can be physical, logical, or a combination thereof.
• the words “herein,” “above,” “below,” and words of similar import when used in this application, shall refer to this application as a whole and not to any particular portions of this application.
• words in the above Detailed Description using the singular or plural number can also include the plural or singular number respectively.
• the word “or,” in reference to a list of two or more items, covers all of the following interpretations of the word: any of the items in the list, all of the items in the list, and any combination of the items in the list.
• processes, message/data flows, or blocks are presented in a given order, alternative implementations can perform routines having blocks, or employ systems having blocks, in a different order, and some processes or blocks can be deleted, moved, added, subdivided, combined, and/or modified to provide alternative or subcombinations.
• Each of these processes, message/data flows, or blocks can be implemented in a variety of different ways.
• processes or blocks are at times shown as being performed in series, these processes or blocks can instead be performed in parallel, or can be performed at different times.
• any specific numbers noted herein are only examples: alternative implementations can employ differing values or ranges.

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