X-MAPS WITH FLEXIBLE TILES

Abstract

Various exemplary embodiments relate to a method and server for processing X-Maps with flexible tiles including receiving measurements of at least one characteristic of a transmission, determining two or more geographic areas of similar transmission conditions, and storing indications of the determined geographic areas and indications of at least one characteristic of at least one transmission in each area.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to co-pending application U.S. application Ser. No. ______, filed on ______, Attorney Docket Number ALC 3913, “______,” which is hereby incorporated by reference for all purposes as if fully set forth herein. This application is related to PCT Patent Application No. EP 2670186, filed on Jun. 1, 2012, which is hereby incorporated by reference for all purposes as if fully set forth herein.

TECHNICAL FIELD

Various exemplary embodiments disclosed herein relate generally to a method and apparatus for generating and disseminating information useful for determining or predicting wireless or cellular network conditions at physical locations based on the conditions recorded at those locations.

BACKGROUND

A “Self-Optimizing Network” (SON) or “Geo-aware Self-Optimizing Networks” (Geo-SON, sometimes referred to in the art as Geo-Aware SON) is a communications network, particularly a cellular network, that can optimize the performance of network elements by adjusting physical equipment settings to account for the static and semi-static changes of spatial radio propagation conditions that result from real-world effects of buildings, growing trees, and increased or decreased demand over time. Optimization of network performance can be enhanced if there is increased information about the spatial radio propagation conditions in a given area and their effect on network efficiency.

The expansion of the smartphone and tablet markets has led to an explosion of Location-Based Services (LBS), including services to identify the location of a person or object, sometimes in relation to a specific location (e.g. an ATM machine or another user), based on the location of a transmitting device located on or near the person or object.

Raw measurements of radio propagation information used to spatially map transmission characteristics to a location is obtained by evaluating (e.g., averaging) measurements from drive tests (e.g. war driving, or recording radio signals while driving in a GPS-equipped vehicle), or from geo-located data recorded from the base stations of distributed communications networks (e.g., cells) and user equipment (UE) devices such as smartphones. The transmission data recorded by current systems includes quality indicators such as received signal strength and received interference. In some situations, UE devices in both idle and connected mode may record data, for example, in connection with 3GPP TS 37.320 “Radio measurement collection for Minimization of Drive Tests (MDT); Overall description; Stage 2”. Various other measurements are specified in the respective 3GPP standards (e.g., 3GPP TS 36.214 “E-UTRA; Physical Layer—Measurements”, and 3GPP TS 36.314 “E-UTRA; Layer 2—Measurements”) and their updates. In some situations, UEs in both, idle and connected mode may be tagged (located) to be typically resident within a geographic area. Some approaches have incorporated network equipment and UE data by gathering spatially resolved UE signal measurements—using location information and transmission data from the mobile user equipment—and linking the data with network side information.

Due in part to the rapid expansion of available data from UE devices, positioning systems designed to support LBS services have developed at a rapid pace, with developments focused on increasing the reliability of the location data collected from and reported to a device. For example, average results might be improved by using filters on raw measurements to reduce noise effects and measurements from rare conditions. These developments have benefitted data consumers of user location information by improving location data such as the accuracy of map positions reported to a user of location based services. Although these services are highly useful for user-level applications related to locating specific devices in near real-time, they lack capabilities useful for smart network management functions.

For example, existing radio propagation mapping services typically sub-divide a terrain/area/city into a rectangular grid (X-Maps). “X” stands for a measure (e.g. Density of population, Pilot Pollution, Received Signal Strength, Bit Error Rate, Throughput) that is represented in the grid. “Maps” in this context denotes a grid-based sub-division of an area into grid-elements (tiles). The grid layout is typically defined by a reference point and orientation, the outer rectangle, and the number of rows and columns of the grid.

Tiles in X-Maps are generally used to introduce spatial resolution of the measurements, driven by the assumption that radio conditions in the transmission path between sender and receiver are similar (i.e. following a certain statistical distribution) for similar locations and thus that averaging of radio paths to all locations inside the tile results in the overall radio condition of the tile. Thus, in the X-Map model one single value or one statistical measure (distribution) or one model-based description plot model parameter is determined and used to describe the property of interest for each grid element. For example, an X-Map model may assume that the path loss between a fixed sender antenna and receiver positions inside a grid element of the size 100 m×100 m (for similar receiver locations) may be described with a single value or a single random value with distribution or a single formula (e.g., Friis formula) with parameters. The assumption is recorded either as a similar value for single-valued parameters or as similar statistically distributed parameters. For example, the prior-art representation 100 in FIG. 1 assigns recorded information from user equipment (UE) 102 such as serving cells 108 and neighbor cells, 110, and recorded information from network equipment 104 such as traffic demand, or other data 114-n, to an X-map database 106 generated according to a grid defined prior to the recording of any radio information.

Grids are typically used to introduce spatial resolution to a property for which the generating model (i.e., a closed form formula for the whole area) is not known or is too complex. In a rectangular tile model, the choice of the size of the tiles in a grid (map) is driven by two divergent demands: larger tiles result in more input data and thus in faster convergence of averaged properties and more statistically significant calculated values. Smaller tiles better reflect spatial variability of radio conditions, but result in less input data for statistics and thus in slower convergence of the averaged properties.

Radio propagation conditions are impacted by and reflect the local environment between sender and receiver antennas. As noted above, current systems using grid-based maps assume that that radio conditions in the transmission path between sender and receiver are similar (i.e. following a certain statistical distribution), such that averaging of transmission records over a tile area leads to a sufficiently good representation of the whole grid element and thus accurate maps of radio propagation conditions. In reality, two environment aspects lead to very different radio propagation conditions. First, radio conditions change due to the topological peculiarities of the physical environment of a certain terrain like buildings, trees, hills, etc. These topological peculiarities cause multipath propagation conditions in the radio propagation path such as reflections and shadowing effects. Second, there may be large quantitative and qualitative differences in propagation conditions observed at locations physically very close to each other, namely Line Of Sight (LOS) vs. Non Line Of Sight (NLOS) and outdoor vs. indoor.

The rectangular grid structure of an X-Maps approach is not well-adapted to such real-world situations. For example, an inner city will have vastly changing radio conditions due to buildings as shading objects. However, radio conditions of note, for example, as shown in FIG. 4 with reference to buildings and other features creating unique radio propagation conditions 402, 404, 406, may not be marked with any particular tile, or may appear only in a small portion or to one side of a tile. Therefore, that a grid-based approach will not be suitable to model complex situations unless the grid tiles become very small, but this is not always feasible because the reduced amount of input measurements for each tile may not result in enough data for accurate results from averaging.

SUMMARY

In light of the present need for an adaptable system linking geographic information with information about wireless network conditions, a brief summary of various exemplary embodiments is presented. Some simplifications and omissions may be made in the following summary, which is intended to highlight and introduce some aspects of the various exemplary embodiments, but not to limit the scope of the invention. Detailed descriptions of a preferred exemplary embodiment adequate to allow those of ordinary skill in the art to make and use the inventive concepts will follow in later sections.

Various exemplary embodiments relate to a method performed by a Map Creator server for generating flexible tile maps related to conditions in a distributed communications network, the method including receiving, at the Map Creator server, measurements of at least one characteristic of a transmission; determining two or more geographic areas of distinctly similar transmission conditions; and storing indications of the determined geographic areas and indications of at least one characteristic of at least one transmission in each area. In some embodiments, the step of determining two or more geographic areas of distinctly similar transmission conditions includes clustering sub-cell areas of similar transmission measurements and mutually delimiting polygons based on sub-cell areas.

In some embodiments, the step of determining two or more geographic areas of distinctly similar transmission conditions includes calculating areas of similar radio paths in external ray-tracing simulations. In alternative embodiments the step of determining two or more geographic areas of distinctly similar transmission conditions includes evaluating PHY-layer measurements for similar transmission conditions. In other embodiments, the step of determining two or more geographic areas of distinctly similar transmission conditions includes filtering areas of distinctly similar transmission conditions according to similar propagation and environment conditions, and categorizing areas of distinctly similar transmission conditions according to similar multi-path propagation configuration. In some alternative embodiments indications of the determined geographic areas include a tessellation of arbitrarily formed polygons.

In some embodiments, at least a portion of at least two determined areas overlap a same physical space. In some alternative embodiments, indications of the determined geographic areas comprise indications of a layer value. In other embodiments, at least one characteristic of a transmission comprises an indication that no transmission was measured. In some embodiments, the at least one characteristic of a transmission is weighted. In various embodiments, indications of at least one characteristic of at least one transmission in each area include path loss.

Various exemplary embodiments relate to a method performed by a Map Creator server for generating flexible tile maps related to conditions in a distributed communications network, including receiving, at the Map Creator server, measurements of at least one characteristic of a transmission, dividing areas of the distributed communications network into a quadratic the grid, assigning the measurements to areas using at least one location characteristic of the transmission, generating indications of two or more irregularly shaped areas having one or more distinctly similar properties based on at least one characteristic of a transmission, and storing the indications of the two or more irregularly shaped areas and indications of at least one characteristic of at least one transmission in each area.

Various exemplary embodiments relate to a Map Creator server for processing geo-information related to conditions in a distributed communications network, the Map Creator server including a network interface and a processor in communication with the network interface, the processor being configured to receive, via the network interface, measurements of at least one characteristic of a transmission; determine two or more geographic areas of distinctly similar transmission conditions; and store indications of the determined geographic areas and indications of at least one characteristic of at least one transmission in each area. In alternative embodiments, the processor is further configured to cluster sub-cell areas of similar transmission measurements and mutually delimit polygons based on sub-cell areas.

In some alternative embodiments, the processor is further configured to calculate areas of similar radio paths in external ray-tracing simulations. In various embodiments the processor is further configured to evaluate PHY-layer measurements for similar transmission conditions. In other alternative embodiments, the processor is further configured to filter areas of distinctly similar transmission conditions according to similar propagation and environment conditions and categorize areas of distinctly similar transmission conditions according to similar multi-path propagation configuration. In various embodiments, indications of the determined geographic areas include a tessellation of arbitrarily formed polygons.

Various exemplary embodiments relate to a Map Creator server for generating flexible tile maps related to conditions in a distributed communications network, the Map Creator server including a network interface, and a processor in communication with the network interface, the processor being configured to receive, via the network interface, measurements of at least one characteristic of a transmission; divide areas of the distributed communications network into a quadratic tile grid; assign the measurements to areas using at least one location characteristic of the transmission; generate indications of two or more irregularly shaped areas having one or more distinctly similar properties based on at least one characteristic of a transmission; and store the indications of the two or more irregularly shaped areas and indications of at least one characteristic of at least one transmission in each area. In some alternative embodiments, the at least one characteristic of a transmission is weighted.

It should be apparent that, in this manner, various exemplary embodiments enable an adaptable system linking geographic information with information about wireless network conditions. In particular, by creating an information base for “Geo-aware Self-Optimizing Networks” (Geo-SON) that does not rely on a grid for sub-dividing the terrain/area into tiles, but instead sub-divides the coverage space depending on the measurements of actual radio propagation conditions.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to better understand various exemplary embodiments, reference is made to the accompanying drawings, wherein:

FIG. 1 illustrates an exemplary visualization of a prior art representation of cell-based data;

FIG. 2 illustrates exemplary visualizations of data generated by a Map Creator;

FIG. 3 illustrates an exemplary environment for a Map Creator;

FIG. 4 illustrates an exemplary visual representation of a geographic area described by an X-Map;

FIG. 5 illustrates an exemplary visual representation of a geographic area described by a flexible tile Map; and

FIG. 6 illustrates an exemplary hardware diagram for a Map Creator.

DETAILED DESCRIPTION

The description and drawings presented herein illustrate various principles. It will be appreciated that those skilled in the art will be able to devise various arrangements that, although not explicitly described or shown herein, embody these principles and are included within the scope of this disclosure. As used herein, the term, “or,” as used herein, refers to a non-exclusive or (i.e., and/or), unless otherwise indicated (e.g., “or else” or “or in the alternative”). Additionally, the various embodiments described herein are not necessarily mutually exclusive and may be combined to produce additional embodiments that incorporate the principles described herein. Further, while various exemplary embodiments are described with regard to a Geo-Information Service in a cellular network, it will be understood that the techniques and arrangements described herein may be implemented to facilitate network optimization in light of environmental properties in other types of systems that implement multiple types of data processing or data structure, such as Wi-Fi systems, heterogeneous cellular networks, homogeneous cellular networks, or other wireless systems and geographic network planning systems.

Accurate maps with radio propagation conditions between Base Station (BS) antenna locations and potential UE locations may enable network operators and others to implement smart network management functions such as SON by allowing prediction of the impact of changes to equipment system settings on the actual performance of the network, and thus achieve better SON efficiency. Information useful for smart network management may include maps describing radio propagation conditions beyond received signal strength, such as radio propagation conditions between serving base station antenna(s) and potential user locations as well as the same information for interfering cells (e.g. interference maps). Examples of such radio propagation conditions includes various types of static and semi-static radio path properties, such as overall path loss, multi-path properties and types, signal-to-interference conditions, and fading types; measurements from the physical layer of sending and receiving devices such as such as delay spread and impulse response measurements of pilot signals; and various types of static and semi-static cell and network performance related information that is required for optimization purposes such as user density, user activity level (average used data rates), and user mobility level.

Based on this type of tightly spatially resolved information, new optimization algorithms may overcome a rough and constricted cell-global view. For example, new algorithms may be configured with respect to performance targets of specific situations, such as (i) cell edge areas with high user density and (ii) relevant indoor locations. Having such information may allow, for example, an improvement in network performance by automatically accounting for geographical relationships among cells and between sender and receiver antennas when optimizing network parameters. Improved information about real radio propagation conditions may aid in network re-planning and small-cell deployment, and improve service quality and network capacity in already-deployed networks by optimizing the parameters of existing equipment. For example, a spatially resolved representation of cell radio properties and cell usage data is required to implement some Self-Optimizing Networks (SON) functions such as automated tilt optimization, handover parameter optimization, and cell dimming from green networking. Typically, to be useful the granularity of the spatial resolution of the input data for such optimization tasks must be significantly below cell size.

In view of the foregoing, it would be desirable to create an information base for “Geo-aware Self-Optimizing Networks” (Geo-SON) that does not rely on a grid for sub-dividing the terrain/area into tiles, but instead sub-divides the coverage space depending on the measurements of actual radio propagation conditions. A “Map Creator” function, using raw data, such as from drive tests, the ongoing communication sessions of user equipment devices and/or network devices, and/or idle mode data from UE devices, estimates areas of similar radio conditions and environment context and outputs a geographic area subdivided into sub-areas of similar radio and environment conditions, including data of the radio parameters of interest for each sub-area. In other words, each sub-area has similar radio and environment conditions within it, and is distinct from its neighboring sub-areas in terms of their radio and environment conditions. Put yet another way, the area description of a tile is the spatial tag for the persistence of a certain property. In most circumstances, the properties of flexible tiles are similar in the sense of being uniform or nearly uniform within each flexible tile, however, properties of different flexible tiles can differ significantly, and should differ for neighboring tiles.

The abbreviation “ETSSA” may be used for “environment type specific areas” and “environment type specific sub-areas”.

Referring now to the drawings, in which like numerals refer to like components or steps, there are disclosed broad aspects of various exemplary embodiments.

In addition to the flexible tile representations of data described herein, the Map Creator may generate other types of geographic representations of spatially resolved radio conditions. Examples of visualizations that may be generated by a Map Creator are shown in FIG. 2, such as xMaps 202, xMaps with flexible tiles 204 (described in detail below), cell shape polygons 206, Antenna deployment parameters 208, environment topology or radio propagations and shading elements 210, and network usage (not shown).

Various components of a Map Creator and system elements for data collection, retention, processing, distribution, and related components are shown in FIG. 3. The architecture shown includes an autonomous geo-information system functioning as part of a cellular access network. The system includes architecture for network internal generation, management, and use of spatially resolved data. Furthermore, the architecture includes the functions of data gathering, generation of relevant mapping or other geo-information from raw measurements, storage in a mapping service or store, and use of the spatially resolved data for applications such as smart network management. Geo-information may be used in network management functions and other geo-aware functions including service optimization and user services.

Data is initially acquired from one or more network devices such as base stations (BS) 302 and/or user equipment (UE) devices 304. In a typical arrangement, a network instance, e.g., a radio network controller associated with BS 302 collects raw measurements from cell schedulers and from UEs following standard measurement procedures. Measurements may include radio link properties of the BS 302 and UE 304, and a recorded location of the UE that was involved in the measurement at the moment the measurement was taken. In some circumstances, the recorded location might include a time stamp of the position estimation, which may be compared with a time stamp associated with the time the transmission measurements at the UE 304 or BS 302 were taken. Raw transmission measurements may include radio link parameters, such as received signal strength, signal-to-interference conditions, and more advanced PHY-layer measurements such as delay spread or impulse response measurements of pilot signals.

Although the term “cell” is typically used interchangeably with BS 302, BS 302 may have two or more different sectors (BSa, BSb, and BSc), each of which would be recognized as a different cell. Data is acquired when measurements are taken which may be sole measurement events, or may be multiple events processed on a user 304 or network device 302. For example, data measurements may be recorded and reported every time a measurement of interest is taken, or data may be collected on the device and occasionally uploaded in bulk to a raw measurements database 310, for example, in connection with MDT.

In some embodiments, Raw Measurements database 310, Maps Creator 312, and Maps DB 314 form a centralized component to gather spatially resolved measurements from ongoing data sessions in the access part of the network and estimate coverage maps from that data. In some embodiments, data acquired from network devices 302, such as, for example, connection-based or off-line (MDT) measurements, may be sent 308 to a raw measurements database 310 through the network operator's network (not shown). In some embodiments, data acquired from network devices 302 may be sent 308 to a raw measurements database 310 through a network for transferring data between devices (not shown). Data acquired from user devices 304, for example, user equipment session measurements, may be sent 306 to a raw measurements database 310 through a network for transferring data between devices (not shown). Raw measurements database 310 may be resident on a single device such as a server, or may be distributed amongst several interconnected devices. Raw data uploaded to the database 310 may be a single measurement, a set of single measurements, a single set of processed measurements, or multiple sets of processed measurements.

Measurements of interest may be acquired for any purpose, including explicitly for the purpose of GIS data collection or for geo-view generation (for example, locating the position of a user device or other location-based function) or may be dual- or multiple-use measurements of network conditions based upon technical processes that are used to establish radio link transmissions, such as measurements of channel state information. For example, the position of a user's mobile device 304 may be reported as the result of a user request, or the automatic function of a mobile device application. In another example, measurement information may be collected during ongoing communication procedures such as data transmission, for instance, as channel state information used to realize a radio transmission (which in some instances may be available from the PHY-layer of system entities such as BS 302 and user equipment 304.

Various devices may be sources of measurements, such as user devices 304 or cellular network operator devices, including, for example, at a base station 302 (e.g., GSM GTS, UMTS, and LTE eNodeB). For example, network device 302 may be a Base Station (BS), wireless network access point, or any other device suited for administering radio communication over a wireless telecommunications network. In another example, user equipment 304 may include electronic devices capable of wireless network communications, such as a server, blade, PC, laptop, tablet, tracking device, public service automobile (for example, Car2Go or ZipCar), drone (i.e., autonomously flying device), automotive system, or mobile phone, or any device that may wirelessly exchange information with another networked device. Raw measurements, either singly or in series, may include user position (geographical location), current received signal strength (RSSI), cellular events, and WLAN measurements. Information such as signal strength collected by a receiving device 304 may be reported for one or multiple transmitting devices, for example, for serving and/or detected cells or base stations.

Raw measurements 310 are sent to a Map Creator 312 for processing. Map Creator 312 may include one or more functions to process data. Map Creator 312 may be resident on a single device such as a server, or may be distributed amongst several interconnected devices. Map Creator 312 sends output to a Maps store or database 314. Maps DB 314 may be co-resident with a Maps service for serving data, for example, Coverage Info 316, to Applications such as a Smart Network Management function 318. Maps DB 314 and a Maps service may both reside on a single device such as a server, on separate interconnected devices, or may be distributed amongst two or more interconnected devices.

Raw measurements may be evaluated flow-based or periodically (including an aging mechanism for old and outdated measurements) such that the output data contains current radio properties for the area. For example, one or more functions of Map Creator 312 may update the Maps DB 314 periodically, for example, on a set schedule, or when map generation is re-executed in response to a certain amount of new measurement data. In some embodiments, functions of Map Creator 312 will weight data so as to take new data into account and age out or discount old data.

Raw measurements data 310 may be sent to each function of Map Creator 312 as required for the function, for example, raw data 310 may be requested by Map Creator 312 on a separate schedule when data is required for a recent time segment than when a Map Creator function requires all raw data ever collected. In some embodiments, raw measurements data may be bulk-processed by Map Creator 312 before being used by one, several, or all functions. Once data processing is complete, each function will send the output data to the Maps DB 314. In some embodiments, Map Creator 312 may send function outputs in bulk to Maps DB 314. In some embodiments, each function may send data to the store separately. Each function may output data in different formats or in the same format.

In one embodiment of the invention, raw data measurements are collected inside base stations 302 (for example, a NodeB), and cell-based calculations performed at the base station or collected and sent directly to Map Creator 312 or through Raw Measurements Database 310. In this embodiment, UE 304 measurements together with related positioning information may be gathered by the base station via radio resource control (RRC) measurement configuration and reporting (e.g., 3GPP TS 36.331 and TS 37.320).

Map Creator 312 may have multiple functions for output of different evaluations or calculations of raw data. Raw measurements 310 are evaluated in a spatially resolved manner. In some embodiments, raw measurements are additionally evaluated in a temporally resolved manner. Combinations of evaluation types are additionally possible, for example, 1) spatially resolved properties within or for a distinct time and 2) a timely resolved property for a distinct flexible environment tile. The spatially resolved representation of the radio propagation conditions of the local environment refers to a descriptive representation of a distinct local cell environment.

Functions of the invention output by Map Creator 312 typically output data delineating flexible tiles described by the elements “environment area delineation” and “environment property description.” A visualization of the output of an exemplary function of Map Creator 312 is shown in FIG. 5. An area of interest (terrain/city) is sub-divided into non-regular shapes which are denoted as flexible tiles. The flexible the description is stored together with the property description that describes that flexible tile.

FIG. 5 depicts an example for the sub-division of a city into polygons of similar radio conditions. Measurements taken from antennas 502, 504, 506, will have different properties depending on the location of the measurement in relation to the antennas and static or semi-static radio path properties. Alternative functions 312 are possible to determine flexible tile areas from raw measurements, including clustering from raw measurements (e.g., K-Means Clustering, Support Vector Machines), calculating areas of similar radio paths in external ray-tracing simulations, evaluating PHY-layer measurements for similar radio conditions, and also a two stage approach where X-Map tile information is initially pre-processed to add or alter data associated with the tile, and then environments delineated and environment type specific sub-area shapes defined to create flexible tiles.

In an exemplary function, sub-cell areas of similar radio link properties are identified, for example, by means of clustering similar measurements, and polygons are used to describe and mutually delimit identified sub-cell areas 500. In one such function, sub-cell areas may delimit indoor locations and outdoor areas. Sub-cell areas are preferably arranged seamlessly with respect to the original cell area. In some embodiments, a function of the Map Creator may output data using a 2.5D or 3D coordinate system to delimit area volume shapes in the maps representation, in order to represent more complex environment conditions than that typically described by two-dimensional tiles, e.g., delimiting space above and below bridges or multistory buildings. For example, in one embodiment an indoor coordinate system which uses semantics to identify rooms is used to describe areas of similar radio conditions. Most if not all optimization methods may be improved by applying environment and geo-context knowledge. In cases where the radio propagation effects of complex environmental conditions may be clustered and described in relation to the physical cause of the condition, calculated environment specific sub-cell areas may overlay and overlap each other.

Map Creator 312 may store various representations of mapping data in Maps DB 314. In some embodiments, data about radio path properties is recorded on a cell-basis, for example, a data point about the distance of separation from the originating cell antenna location at the time a signal was measured, or in another example, a data point about path loss. In some embodiments, data may be stored indicating the best server or BS for particular geographic locations.

The present invention is a departure from tiled maps or X-Maps in at least several respects. Rather than delineating areas based on static rectangles, areas are delineated by a tessellation of arbitrarily formed polygons in 2D or 2.5D or spaces in 3D where each shape is adapted based upon areas of similar or uniform environment conditions. Furthermore, some Map Creator functions may classify and cluster radio measurements according to similar propagation and environment conditions. For example, a clustering criterion may include the similarity of multi-path propagation configurations as measured with delay spread and impulse response functions from pilot symbols. Some Map Creator functions may classify and cluster radio measurements according to other cell performance and environment information.

The sub-division of a given area into tiles may be based, for example, on observations of the measurements made during the data collection phase. One or more properties of interest are stored for a certain region as set of the elements. A the includes information elements for (1) the shape of the tile (e.g., as a polygon) and the (2) one or more properties of interest that characterize the area of the tile. Areas for which a certain property may be arranged under one descriptive property at a given statistical quality are grouped as one area of similar properties. In another example, complex simulations, e.g., ray tracing simulations with city models, are performed to identify areas of similar properties. More than one property of interest may be represented. The same or different sub-divisions of the area into tiles may be used for each property of interest or set of properties of interest. What properties are used may depend on different Map Creator functions, or from different parameters input into the same function.

One exemplary representation of spatially resolved map data stored as a data set for a certain sub-area may include at least (1) the shape of the area (as a polygon or volume shape) and (2) a radio property for receiving signals inside this area. The data set may include additional related cell and cell usage information. Different radio properties may be measured using the same or different sub-divisions of an area. Different functions may be implemented in Map Creator 312 to calculate and record different radio properties or areas depending on the desired network optimization or other function.

In various cases the flexible, topology adapted tile structure generated by the Map Creator 312 provides the means for a more environment-specific representation of cell properties and cell context as a basis for an improved, context sensitive and policy controlled SON optimization. However, a descriptive representation of a certain environment is still used so that no closed-form models are required. For example, some exemplary functions may be suited to modeling areas where there exists significantly varying availability of measurements due to seldom visited locations, for example in rural areas or other isolated or difficult to reach locations. In one such exemplary function, radio path predictions may be based on measurements available from only a part of a distinct, homogeneous environment. Another exemplary function may be suited to situations where neighbor cell measurements are not be possible due to the signal strength of the serving cell in cell areas served by a so-called re-use one radio system. In one such exemplary function, tilt preset values for neighbor cells may be calculated to compensate for cell outages.

Based on spatially-resolved cellular conditions, SON cell and network performance may be better optimized by adapting to peculiarities that occur within a cell rather than using a restricted, cell global view on cell performance. For example, operator policy defined guidelines and objectives for Quality of Service (QoS) may be defined and applied to the environment specific to an individual cell or area or flexible tile area. An exemplary application that may be used by a network operator, in order to guarantee coverage within cell areas for outdoor locations, but to differentiate from indoor coverage where usually coverage cannot be guaranteed for all situations, may be to optimize antenna tilt settings to avoid coverage holes within the cell. Another exemplary application that could be used to optimize a network is to calculate user distribution in a given area over times of day and days of the week, for example, to differentiate usage patterns at night from business hours during the day. Another exemplary application could be to provide different QoS levels in business areas and residential areas. Another exemplary application includes searching best locations for the placement of small cells as part a network-re-planning campaign.

Another exemplary embodiment of the flexible the output of a Map Creator function provides Z-layering for area descriptions. The concept of Z-layering is known to those of skill in the art, and the concept of a Z-layer enforces the order of overlapping two-dimensional objects in a space. In this embodiment, overlapping areas are ranked such that the area of the highest layer for a given location will be delivered to a requestor when multiple layers are stored for that location. For example, an area A at z-layer 0 may have a certain basic property, and area B at z-layer 1 with a different property may partially or fully overlap with area A. When a request is made for the parameters for a certain location, if two or more defined areas, in this example, area A and area B, each encompass the requested location, the area definition and/or property which has the highest z-layer will be returned—in this case, area B.

An expanded exemplary embodiment of a Map Creator function whose output may be applied to antenna tilt optimization by applying environment and geo-context knowledge is further described as follows. An ETSSA (as explained above, “environment type specific areas” and “environment type specific sub-areas”), may reveal the same or similar radio propagation properties, for example, radio link properties. Typical output of a function would include data delineating unique sub-areas of a cell as irregularly shaped polygons outlining buildings or open areas as shown in FIG. 5.

In order to determine an ETSSA by mutual delineation of different environment areas, UE and Network device measurements are collected from the Raw Measurements database 310. Device measurements may include Key Performance Indicators (KPIs) from which each ETSSA may be constructed using an appropriate method such as a clustering algorithm. Other device measurements may include UE location, speed, or visible radio cells and their respective signal strength.

An alternative embodiment includes an optional pre-processing step, where raw measurements are processed as follows. Each cell area is divided into a quadratic tile grid such as an X-map. Using UE location information, radio measurements are associated with the corresponding the for each cell. From all measurements assigned to a tile (gathered from multiple UEs within a distinct time period) arithmetic mean values or other appropriate statistical representations of the measurements are calculated. These newly calculated statistical values are used to create values for that the or update older existing values for that the by an algorithmic filtering procedure. Preprocessing as described above may be carried out as a separate function or set of functions in the Map Creator for different network statuses like busy hours, night hours, weekends, and so on. Network equipment information, including KPIs such as cell status information, may be assigned to areas without additional calculation.

In another embodiment, environments are delineated and ETSSA shapes are defined using either raw data or the pre-processed data described above. For example, an algorithm such as a clustering algorithm may be applied to an appropriate set of measurement sizes in an n-dimensional parameter space to determine which tiles of the regular quadratic tile grid have the same or similar properties. Other algorithms may be applied such as filtering according to similar propagation and environment conditions and categorization according to similar multi-path propagation configuration determined by analyzing the delay spread and impulse response functions from pilot symbols. Those tiles are then combined to build an irregularly shaped ETSSA. Exemplary representations of such an irregularly shaped environment area are polygons in 2D, 2.5D or 3D shapes with their shape adapted to the area of similar radio conditions, or Thiessen polygons. To calculate exemplary Thiessen polygons, Voronoi center points are calculated so that the corresponding Voronoi diagram represents the ETSSAs of the whole cell coverage area. The set of Voronoi center points are stored with available environment conditions in a database. In one advantageous aspect of this exemplary representation, tiles in an ETSSA with few or no available measurements (e.g. tiles in an isolated area such as a deep forest) may be included into the optimization process by interpolation or extrapolation of ETSSA properties over the tiles. The resulting ETSSA structure may be refined as more measurements become available.

Overlaying ETSSAs constructed by different grouping criteria will occur. Different grouping criteria may result from different Map Creator functions, or from different parameters input into the same function. For example, different types of network optimization require different ETSSA types with distinct weightings of ETSSA properties and deduced metrics. For example, ETSSA type specific tilt optimization targeting residential areas close to the border of a small city covered by a serving cell may differ from optimizations targeted at an adjacent forest partly covered by the same cell. A preferred outcome of a Smart Network Management application is to achieve high spectral efficiency for the residential area to provide a maximum cell throughput, but maintain only a minimum signal to noise ratio within the forest for guaranteeing voice and emergency calls. For such policies the optimum tilt setting shall be found by the SON algorithm.

Because of the larger size of some ETSSAs, some radio performance values like path loss between a UE and BS antenna cannot be handled as a single constant value for the whole ETSSA but rather as a function that depends on the location, i.e., a spatially distributed function. For example, in a forest area where measurements are rare, regression analysis may be applied to analyze the reception condition for the whole ETSSA area by fitting a generic path loss to available, measurements. Sub-areas where no measurements are available are approximated by interpolating or extrapolating using the fitted path loss law, unless and until measurements become available over time. One example of a fitted, generic path loss law is:

PL=a*log(d)+b*d+c

where PL is the path loss over, for example, the whole forest area, d the distance between a UE and a BS antenna, and a, b, and c provide a characteristic description of a radio property for an ETSSA. In case of local peculiarities which prevent such a unique generic description of an ETSSA, the clustering algorithm may split the area into different ETSSAs, each with its own characteristic parameter set. The area of interest is then subdivided into ETSSAs using characteristics unique to the area, and/or sets of characteristics unique to individual sub-areas. The ETSSA-specific data calculated by the Map Creator 312 is stored in the Maps Database 314 and may be accessed by SON optimization entities such as Smart Network Management 318.

For example, an optimization algorithm may require input parameters such as performance indicators or metric components, each multiplied with a definable set of associated weighting factors. Using flexible tiles generated for ETSSAs, a specific set of weighting factors for each metric component may be applied per specific ETSSA type. In one embodiment, weighting factors and ETSSA types may be represented by a 2D matrix spanned by the different ETSSA types and the according vector of weighting factors. In this embodiment, the SON algorithm 318 may retrieve vector-described shapes for all ETSSAs of a target cell coverage area with associated radio propagation data 316 from the database 314 in order to determine statistically spatially resolved performance values within an ETSSA such as received signal strength, the signal to noise ratio SINR, the spectral efficiency and the mean data rate demand. Characteristic performance values may now be calculated for each ETSSA by applying ETSSA type-specific weighting factors to the respective metric components. From the characteristic performance values for each ETSSA a single performance value for a target cell may be calculated. The SON algorithm may maximize performance values by optimizing the tilt setting for the target cells.

As noted above, different environment types may be considered in the optimization process with a different weighting of environment type specific metric components. Moreover, using additional context information extracted from the measurements the introduction of further, specific environment types may be developed to manage local peculiarities within a cell area. Therefore, areas with different QoS and data rate demand needs such as business districts, train stations, shopping malls, etc. may be considered specifically in a refined optimization process, controlled by policies defined by a network operator using ETSSA type-specific weighting factors. The optimization process may also be tailored by operator defined preferences and constraints to handle all local peculiarities within any cell area.

FIG. 6 illustrates an exemplary hardware diagram for a Maps Creator 600. The exemplary Maps Creator 600 may correspond to the Maps Creator 312 FIG. 3. As shown, the Maps Creator 600 includes a processor 620, memory 630, user interface 640, network interface 650, and storage 660 interconnected via one or more system buses 610. It will be understood that FIG. 6 constitutes, in some respects, an abstraction and that the actual organization of the components of the Maps Creator 600 may be more complex than illustrated.

The processor 620 may be any hardware device capable of executing instructions stored in memory 630 or storage 660. As such, the processor may include a microprocessor, field programmable gate array (FPGA), application-specific integrated circuit (ASIC), or other similar devices.

The memory 630 may include various memories such as, for example L1, L2, or L3 cache or system memory. As such, the memory 630 may include static random access memory (SRAM), dynamic RAM (DRAM), flash memory, read only memory (ROM), or other similar memory devices.

The user interface 640 may include one or more devices for enabling communication with a user such as an administrator. For example, the user interface 640 may include a display, a mouse, and a keyboard for receiving user commands.

The network interface 650 may include one or more devices for enabling communication with other hardware devices. For example, the network interface 650 may include a network interface card (NIC) configured to communicate according to the Ethernet protocol. Additionally, the network interface 550 may implement a TCP/IP stack for communication according to the TCP/IP protocols. Various alternative or additional hardware or configurations for the network interface 650 will be apparent.

The storage 660 may include one or more machine-readable storage media such as read-only memory (ROM), random-access memory (RAM), magnetic disk storage media, optical storage media, flash-memory devices, or similar storage media. In various embodiments, the storage 660 may store instructions for execution by the processor 620 or data upon with the processor 620 may operate. For example, the storage 660 may store Map Creator instructions 662 for performing calculations of raw radio measurements according to the concepts described herein. The storage may also store Raw Measurement Data 664 and Map Data 666 for use by the processor executing the Map Creator instructions 662.

It will be apparent that various information described as stored in the storage 660 may be additionally or alternatively stored in the memory 630. For example, the Map Creator instructions may be stored, at least partially, in memory 630 for execution by the processor 620. As another example, in embodiments wherein the Map Creator 600 interfaces with an external application, the memory 630 may instead store Raw Measurement Data 664 that have been fetched for processing. In this respect, the memory 630 may also be considered to constitute a “storage device.” Various other arrangements will be apparent. Further, the memory 630 and storage 660 may both be considered to be “non-transitory machine-readable media.” As used herein, the term “non-transitory” will be understood to exclude transitory signals but to include all forms of storage, including both volatile and non-volatile memories.

While the Map Creator 600 is shown as including one of each described component, the various components may be duplicated in various embodiments. For example, the processor 620 may include multiple microprocessors that are configured to independently execute the methods described herein or are configured to perform steps or subroutines of the methods described herein such that the multiple processors cooperate to achieve the functionality described herein. In some embodiments, such as those wherein the Map Creator 600 is implemented in a cloud computing architecture, components may be physically distributed among different devices. For example, the processor 620 may include a first microprocessor in a first data center and a second microprocessor in a second data center. Various other arrangements will be apparent.

It should be apparent from the foregoing description that various exemplary embodiments of the invention may be implemented in hardware and/or firmware. Furthermore, various exemplary embodiments may be implemented as instructions stored on a machine-readable storage medium, which may be read and executed by at least one processor to perform the operations described in detail herein. A machine-readable storage medium may include any mechanism for storing information in a form readable by a machine, such as a personal or laptop computer, a server, or other computing device. Thus, a machine-readable storage medium may include read-only memory (ROM), random-access memory (RAM), magnetic disk storage media, optical storage media, flash-memory devices, and similar storage media.

It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative circuitry embodying the principals of the invention. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in machine readable media and so executed by a computer or processor, whether or not such computer or processor is explicitly shown.

Although the various exemplary embodiments have been described in detail with particular reference to certain exemplary aspects thereof, it should be understood that the invention is capable of other embodiments and its details are capable of modifications in various obvious respects. As is readily apparent to those skilled in the art, variations and modifications can be affected while remaining within the spirit and scope of the invention. Accordingly, the foregoing disclosure, description, and figures are for illustrative purposes only and do not in any way limit the invention, which is defined only by the claims.

Claims

1. A method performed by a Map Creator server for generating flexible tile maps related to conditions in a distributed communications network, the method comprising:

receiving, at the Map Creator server, measurements of at least one characteristic of a transmission;

determining two or more geographic areas of distinctly similar transmission conditions; and

storing indications of the determined geographic areas and indications of at least one characteristic of at least one transmission in each area.

2. The method of claim 1, wherein the step of determining two or more geographic areas of distinctly similar transmission conditions further comprises:

clustering sub-cell areas of similar transmission measurements; and

mutually delimiting polygons based on sub-cell areas.

3. The method of claim 1, wherein the step of determining two or more geographic areas of distinctly similar transmission conditions further comprises:

calculating areas of similar radio paths in external ray-tracing simulations.

4. The method of claim 1, wherein the step of determining two or more geographic areas of distinctly similar transmission conditions further comprises:

evaluating PHY-layer measurements for similar transmission conditions.

5. The method of claim 1, wherein the step of determining two or more geographic areas of distinctly similar transmission conditions further comprises:

filtering areas of distinctly similar transmission conditions according to similar propagation and environment conditions; and

categorizing areas of distinctly similar transmission conditions according to similar multi-path propagation configuration.

6. The method of claim 1, wherein indications of the determined geographic areas comprise a tessellation of arbitrarily formed polygons.

7. The method of claim 1, wherein at least a portion of at least two determined areas overlap a same physical space.

8. The method of claim 7, wherein indications of the determined geographic areas comprise indications of a layer value.

9. The method of claim 1, wherein at least one characteristic of a transmission comprises an indication that no transmission was measured.

10. The method of claim 1 wherein the at least one characteristic of a transmission is weighted.

11. The method of claim 1, wherein indications of at least one characteristic of at least one transmission in each area comprise path loss.

12. A method performed by a Map Creator server for generating flexible tile maps related to conditions in a distributed communications network, the method comprising:

receiving, at the Map Creator server, measurements of at least one characteristic of a transmission;

dividing areas of the distributed communications network into a quadratic tile grid;

assigning the measurements to areas using at least one location characteristic of the transmission;

generating indications of two or more irregularly shaped areas having one or more distinctly similar properties based on at least one characteristic of a transmission; and

storing the indications of the two or more irregularly shaped areas and indications of at least one characteristic of at least one transmission in each area.

13. A Map Creator server for processing geo-information related to conditions in a distributed communications network, the Map Creator server comprising:

a network interface; and

a processor in communication with the network interface, the processor being configured to:

receive, via the network interface, measurements of at least one characteristic of a transmission;

determine two or more geographic areas of distinctly similar transmission conditions; and

store indications of the determined geographic areas and indications of at least one characteristic of at least one transmission in each area.

14. The Map Creator server of claim 13, where the processor is further configured to:

cluster sub-cell areas of similar transmission measurements; and

mutually delimit polygons based on sub-cell areas.

15. The Map Creator server of claim 13, where the processor is further configured to:

calculate areas of similar radio paths in external ray-tracing simulations.

16. The Map Creator server of claim 13, where the processor is further configured to:

evaluate PHY-layer measurements for similar transmission conditions.

17. The Map Creator server of claim 13, where the processor is further configured to:

filter areas of distinctly similar transmission conditions according to similar propagation and environment conditions; and

categorize areas of distinctly similar transmission conditions according to similar multi-path propagation configuration.

18. The Map Creator server of claim 13, where indications of the determined geographic areas comprise a tessellation of arbitrarily formed polygons.

19. A Map Creator server for generating flexible tile maps related to conditions in a distributed communications network, the Map Creator server comprising:

a network interface; and

a processor in communication with the network interface, the processor being configured to:

receive, via the network interface, measurements of at least one characteristic of a transmission;

divide areas of the distributed communications network into a quadratic tile grid;

assign the measurements to areas using at least one location characteristic of the transmission;

generate indications of two or more irregularly shaped areas having one or more distinctly similar properties based on at least one characteristic of a transmission; and

store the indications of the two or more irregularly shaped areas and indications of at least one characteristic of at least one transmission in each area.

20. The Map Creator server of claim 19, where the at least one characteristic of a transmission is weighted.