WO2017137089A1 - Établissement de profil d'équipement utilisateur pour une administration de réseau - Google Patents

Établissement de profil d'équipement utilisateur pour une administration de réseau Download PDF

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Publication number
WO2017137089A1
WO2017137089A1 PCT/EP2016/052963 EP2016052963W WO2017137089A1 WO 2017137089 A1 WO2017137089 A1 WO 2017137089A1 EP 2016052963 W EP2016052963 W EP 2016052963W WO 2017137089 A1 WO2017137089 A1 WO 2017137089A1
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Prior art keywords
user
information
network
profiles
profile
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English (en)
Inventor
Panagiotis SPAPIS
Alexandros KALOXYLOS
Chan Zhou
Sokratis BARMPOUNAKIS
Nancy ALONISTIOTI
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Huawei Technologies Duesseldorf GmbH
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Huawei Technologies Duesseldorf GmbH
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Priority to PCT/EP2016/052963 priority Critical patent/WO2017137089A1/fr
Publication of WO2017137089A1 publication Critical patent/WO2017137089A1/fr
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W8/00—Network data management
    • H04W8/18—Processing of user or subscriber data, e.g. subscribed services, user preferences or user profiles; Transfer of user or subscriber data
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition

Definitions

  • the present invention relates to a user profiling system for a communication network and a user profiling method for a communication network.
  • the present invention also relates to a computer-readable storage medium storing program code, the program code comprising instructions for carrying out such a method.
  • the number of wireless and mobile devices is expected to increase considerably. Along with it, a huge increase of mobile traffic will also take place.
  • the deployment of 5G cellular net- works targets to support this vast number of devices, while at the same time the existing 3GPP specifications will keep on supporting legacy cellular access networks (e.g., GSM, HSPA, LTE, LTE-A) as well as alternative radio access technologies (e.g., WiFi).
  • legacy cellular access networks e.g., GSM, HSPA, LTE, LTE-A
  • alternative radio access technologies e.g., WiFi
  • the new services (e.g., augmented reality, cloud services, car to car communication, etc.) lead to a paradigm shift of network access mechanisms since the users want connectivity everywhere (including indoor and outdoor environments, environments with ultra-dense or limited infrastructure, or where the environment is extremely crowded, etc.) with various mobilities (e.g., high speed trains, random indoor mobility, moving crowd, etc.).
  • connectivity everywhere including indoor and outdoor environments, environments with ultra-dense or limited infrastructure, or where the environment is extremely crowded, etc.
  • mobilities e.g., high speed trains, random indoor mobility, moving crowd, etc.
  • Context aware mechanisms for predicting the future behaviors of users exploit current infor- mation (i.e., current contextual information) so as to predict the user behavior including the user mobility, the service that the user will access and/or the duration of the service access.
  • current infor- mation i.e., current contextual information
  • the predictions are often not accurate, or they cannot make predictions for longer time periods.
  • the objective of the present invention is to provide a user profiling system and a user profiling method, wherein the user profiling system and the user profiling method overcome one or more of the above-mentioned problems of the prior art.
  • a first aspect of the invention provides a user profiling system for a communication network, comprising:
  • an information acquisition unit configured to acquire user network information for a plurality of users of the communication network
  • a profile generation unit configured to generate a plurality of user profiles for the plurality of users based on the acquired user network information
  • a profile determination unit configured to determine, based on a current characteristic of a specific user, from the plurality of user profiles one or more candidate user profiles for the specific user, and
  • the user profiling system of the first aspect can operate in two phases: In a first phase ("learning phase"), it can learn typical behaviors from user network information.
  • the user network information in the following is not limited to network information, but can include all information available about a user, their user equipment and their interaction with the network.
  • the user network information includes historic information, such as information about user-related network events in the past.
  • the system can apply the learned knowledge by determining one or more candidate user profiles for a user and an active user profile from the one or more candidate profiles.
  • This user profile may be associated with a typical behavior of the user, including typical kinds of network interaction of the user, e.g. a typical mobility level and/or a typical amount of data transfer.
  • the profile determination unit can be configured to determine a plurality of candidate user profiles.
  • the user profiles may comprise or be associated with input parameters or input parameter ranges that indicate when which one of the user profiles should be an active user profile.
  • a user profile may indicate a time range when it can be an active user profile.
  • the profile determination unit can be configured to always determine only one candidate user profile for a user.
  • the candidate user profile is the active user profile.
  • the profile determination unit can be configured to directly determine the active user profile. This may be preferable if frequent communications between the profile determination unit and other units such as the configuration unit are not an issue.
  • the configuration unit can configure a network component by directly setting one or more parameters of the network component.
  • the configuration unit can also be part of the network component that it is configuring.
  • the configuration unit can be a distribution unit that is configured to distribute the active user profile or an indication of the active user profile to components in the communication network.
  • the configuration unit can be implemented e.g. in base station, mobility management entity or even in a user equipment.
  • an overall list of user profiles can be built. For a specific user, a list of "enabled” user profiles can be determined that are suitable for this user. Subsequently, based e.g. on characteristics, such as an approximate location of the specific user and the date/time a list of "candidate" user profiles for this user can be determined. For example, if the user is at a shopping mall in the late afternoon, based on past information, the system may predict that the user is only making few and short phone calls whereas when the same user is at home he behaves as a heavy data user watching high definition videos for hours. However, the user behavior will typically still depend on further information such as a battery level of remaining credits on a prepaid account. Thus, based on further real time information such as battery level or remaining credits, the active user profile may be chosen.
  • the user behavior may change based on a large number of information, including but not limited to the user equipment type, the battery level of the user equipment, the charging status of the user account (e.g., remaining credits, etc.), the user overall user income, the user educational education level, etc. Due to the complexity and high dimensionality of this information, reducing the information to a selection from a set of predefined user profiles significantly reduces computational complexity.
  • the profiling system of the first aspect provides mechanisms that solve the abovementioned challenges and enable an efficient network management and control by automatically building user profiles which are associated with a predicted user behavior.
  • the proposed mechanism can take advantage of existing data mining mechanisms for identifying the most impacting parameters in the user behavior. Supervised and/or unsupervised learning mechanisms can be used for the identification of the user profiles.
  • a user profile can be associated with a predicted user behavior in the sense that it comprises a list of parameters that indicate a predicted user behavior. This can include: expected services to be accessed, the access rate, access duration, user mobility. These parameters can indicate different behavior based on the values of additional variables, including but not limited to the location/day/time as well as battery level and charging status.
  • the association between user profiles and expected user behaviors can be implemented e.g. using a table that associates different user profiles with different behavior parameters.
  • the behavior parameters can be individual values (e.g. an expected movement speed), value rang- es (e.g. from an expected minimum movement speed to an expected maximum movement speed) and probability distributions (e.g. a probability distribution that indicates which movement speeds are how likely).
  • the current characteristic of the specific user includes at least one of the following: a time, a location, a reception level, a battery level, and a credit level of the specific user.
  • the reception level and/or the battery level can be determined at the user equipment and then transmitted to the network.
  • the user network information may include all kinds of information that are exchanged among network entities, even heterogeneous ones so as to solve challenging networking problems such as management and control of the network resources.
  • the broad definition of network user information implies that all information types may be used for the optimization of the network management and control.
  • radio information e.g., RSS, RSRP, RSRQ, backhaul link capacity and quality, etc.
  • mobility information e.g., user speed, number of handovers, etc.
  • power/energy information e.g., battery consumption rate, battery level, etc.
  • user network information may refer to all information types that may be used for decision making, in addition to radio measurements such as Received Signal Strength - RSS, Signal to Interference Ratio.
  • a simple example of modelling user behavior depending on user network information could be the following:
  • Joe is stationary at the Office every weekday from 9:00 - 18:00, he performs long voice calls, and he does not access internet through his cell phone.
  • Joe in city center every Saturday from 10:00 - 16:00 is highly mobile, he performs short voice calls, and he does NOT access the Internet through his cell phone.
  • Such knowledge can enable the network to make decisions regarding the placement to the user in a specific radio access technology (RAT) or to a specific layer (e.g., macro cell or micro cell) and the interference cancelation methods to be used (e.g., CoMP or interference coordination, etc.).
  • RAT radio access technology
  • a specific layer e.g., macro cell or micro cell
  • interference cancelation methods e.g., CoMP or interference coordination, etc.
  • the profile determination unit and/or the network component is configured to select the active user profile from the one or more candidate user profiles of the user based on a further current characteristic of the specific user.
  • the one or more candidate user profiles can be transmitted e.g. to the network component which then uses an internal algorithm to determine the active user profile based on the values of additional variables such as day/time, location, battery level and charging (in terms of monetary credits) status.
  • the list of user profiles, in particular the candidate user profiles can comprise for each of the candidate user profiles an indication, e.g. a parameter range that indicates when this user profile can be the active user profile.
  • the further current characteristic can be one that changes more rapidly.
  • the user profiling system further comprises a distribution unit configured to distribute a subset of the plurality of user profiles in the communication network, wherein in particular the distribution unit is comprised in a dedicated unit, in particular a home subscriber server and/or a home location register.
  • the configuration unit can be the distribution unit, i.e., the configuration unit configures network components by passing user profile information to the network components.
  • the distribution unit distribute active user profiles (possibly including associated user behavior information for each user profile) and the configuration unit configures a network component based on the distributed active user profiles.
  • the distribution unit may be configured to distribute enabled user profiles of users that are known to be present in a certain geographical region to network components, in particular base stations, in that geographical region. This has the advantage that a reduced number of user profiles can be kept near to where the network components may require access to the user profiles.
  • the distribution unit can also be configured to distribute the subset of the plurality of user profiles, e.g. the active user profile for a specific user, to a user equipment of the user.
  • the information acquisition unit is configured to acquire the user network information from at least one of the following: a user equipment, a base station, a mobility server, a charging gateway, a data router, and a network database of the communication network.
  • the profile generation unit is further configured to determine, based on the acquired user network information, one or more enabled user profiles that are enabled for the specific user and the profile determination unit is configured to determine the one or more candidate user profiles from the one or more enabled user profiles of the specific user.
  • the profile generation unit and/or the profile determination unit may have access to or may comprise a database.
  • the database may be located at one or more central components of the communication network.
  • the user network information comprises at least one of the following:
  • a network measurement in particular at least one of the following: a received signal strength, a RSRP/RSRQ, a backhaul link capacity, a backhaul link quality, a packet loss, a connection delay, and an interface information of a user equipment, a mobility information, in particular a user movement speed and/or a number of handovers of a user equipment,
  • a service measurement information in particular at least one of the following: an accessed service type, an accessed service duration, an accessed service characteristic, a packet size, a packet transmission interval, a packet reception interval, an uplink bit rate, a downlink bit rate, a jitter, a packet loss, and a packet error rate of a base station or a user equipment,
  • a social information of the user in particular at least one of the following: an age, an employment, a profession, an education level, an income, and a gender of a user, a user contract information, in particular a contract ID and/or a contract expiration date of a user,
  • a credit level information in particular a credit model and/or an available credit level of a user
  • a user equipment information in particular at least one of the following: an available battery level, a maximum battery charging level, a device central processing unit description, a memory size, an operating system identifier, a screen size, a screen resolu- tion, a power information, a current CPU usage level, a current memory usage level, an information about one or more protocols supported by the user equipment, and an information about one or more physical interfaces of the user equipment.
  • Including one or more of this information in the user network information allows the user profiling system to create user profiles that accurately characterise different user behaviors.
  • One or more of the above user network information may be acquired locally at the user equipment and transmitted to the communication network so that it is available at components of the user profiling system that are distributed in the communication network.
  • the profile generation unit is configured to generate the plurality of user profiles by performing supervised and/or unsupervised learning on the acquired user network information
  • the profile generation unit is configured to determine one or more rele- vant features of the acquired user network information by analysing at least one of an Information Gain, a X 2 statistic, and a Mutual Information of the acquired user network information.
  • Determining relevant features using one or more of the above techniques has the advantage that the large number of dimensions of the acquired network user information is reduced, which makes the information easier to process and transmit within the communication network.
  • the profile generation unit can be configured to generate the plurality of user profiles using at least one of decision trees, Support Vector Machines, and clustering. These have been shown to be particularly efficient learning methods.
  • Unsupervised and supervised learning can also be combined.
  • the system can first perform unsupervised learning by clustering the users into a number of user groups. Sub- sequently, using techniques from supervised learning, certain enabled user profiles can be associated with predicted user behaviors.
  • the network component is configured to make a control function, in particular a radio resource man- agement action and/or a handover decision based on the active user profile, wherein in particular the network component is configured to decide how to execute the control function based on the active user profile.
  • the radio resource management action may comprise allocation of resources, scheduling and/or interference management.
  • the active user profile can be associated with one or more parameters about an expected user behavior.
  • a network component can be configured to make a control function based on these one or more parameters. This has the advantage that the network operation can be adjusted based on an expected user behavior.
  • the user profiling system further comprises a user equipment that is configured to select a cell and/or a radio access technology while it is in an idle mode.
  • the user profiling system can be configured to transmit an active user profile to a user equipment and the user equipment can be configured to select the cell and/or the radio access technology based on the transmitted active user profile.
  • the network can inform a user equipment about an active user profile and the user equipment can then make a cell/RAT decision, e.g. cell selection, based on the active user profile.
  • a cell/RAT decision e.g. cell selection
  • the active user profile may be passed to the user equipment during a previous attachment to the network.
  • the user profile is associated with a predicted behavior of a user, in particular a predicted behavior related to at least one of the following: a predicted mobility level, a predicted movement direction, a predicted type of accesses services, and a predicted service access duration.
  • a second aspect of the invention refers to a user profiling method for a communication network, the method comprising:
  • profiling methods according to the second aspect of the invention can be performed by the user profiling system according to the first aspect of the invention. Further features or implementations of the profiling method according to the second aspect of the invention can perform the functionality of the user profiling system according to the first aspect of the invention and its different implementation forms.
  • the method of the second aspect can comprise two phases of operation, the offline one and the online one.
  • the offline phase the required inputs are being gathered and processed, so as to extract the behavioral profiles.
  • the profiles are being distributed to the network components and used in online manner by combining online information with the profiles for more accurate prediction.
  • the predicted behavior can then be used for network management and control operations. Afterwards, the overall network performance can be evaluated and fine- tuned according to the effectiveness in the network performance.
  • the offline phase can comprise one or more of the following functions:
  • Storing the list of user profiles can involve writing a table that associates users with user profiles that are enabled for these users. It can further involve writing a table that associates user profiles with expected behaviors of these user profiles.
  • the online phase can include one or more the following two functionalities:
  • the distribution of the user profiles to different networking entities includes but are not limited to user equipments, Access Stratum and Non Access Stratum control entities, databases etc.
  • the method further comprises:
  • evaluating the performance of the communication network comprises evaluating whether a network decision that is based on active user profiles have been beneficial to the performance of the communication network.
  • a third aspect of the invention refers to a computer-readable storage medium storing program code, the program code comprising instructions for carrying out the profiling method of the second aspect or one of the implementations.
  • FIG. 1 is a block diagram illustrating a user profiling system in accordance with an embodiment of the present invention
  • FIG. 2 is a flow chart illustrating a user profiling method in accordance with a further embodiment of the present invention
  • FIG. 3 is a further flow chart illustrating phases and steps of a method in accordance with a further embodiment of the present invention.
  • FIG. 4 is a diagram illustrating an exemplary implementation of the process of acquiring and using the list of user profiles from the Profiling Engine in accordance with a further embodiment of the present invention
  • FIG. 5 is a diagram illustrating an exemplary implementation of the process of acquiring and using the list of user profiles from a networking component from the Profiling Engine in accordance with a further embodiment of the present invention
  • FIG. 6 is a diagram illustrating an exemplary implementation of the process of acquiring and using the list of user profiles from a networking component from the Profiling Engine in accordance with a further embodiment of the present invention; is a diagram illustrating an exemplary implementation of the process of acquiring the list of user profiles from eNB from the Profiling Engine in an
  • LTE/LTE-A network in accordance with a further embodiment of the present invention
  • FIG. 1 shows a user profiling system 100 for a communication network in accordance with an embodiment of the present invention.
  • the user profiling system 100 comprises an information acquisition unit 110, a profile generation unit 120, a profile determination unit 130 and a configuration unit 140.
  • the units 110-140 may be distributed over different locations of the communication network or one or more of the units 110-140 may be comprised in a profiling engine which may be located in one device, e.g. a component of the communication network.
  • the information acquisition unit 110 is configured to acquire user network information for a plurality of users of the communication network.
  • the information acquisition unit may be configured to derive the user network information from a logging unit (not shown in FIG. 1) that is configured to log user network information in a log file or a log database.
  • the profile generation unit 120 is configured to generate a plurality of user profiles for the plurality of users based on the acquired user network information.
  • the profile generation unit 120 may be configured to use machine learning techniques to generate the plurality of user profiles.
  • the profile determination unit 130 is configured to determine, based on a current characteristic of a specific user, from the plurality of user profiles one or more candidate user profiles for the specific user.
  • the configuration unit 140 is configured to configure a network component of the communication network based on an active user profile of the one or more candidate user profiles.
  • the configuration unit 140 can be localized in a processor of a network component that it is configuring.
  • FIG. 2 shows a user profiling method 200 for a communication network in accordance with an embodiment of the present invention.
  • the method comprises a first step 210 of acquiring user network information for a plurality of users of the communication network.
  • the first step 210 may be carried out repeatedly, e.g., in regular intervals, in order to retrieve newer user network information.
  • the method comprises a second step 220 of generating a plurality of user profiles for the plurality of users based on the acquired user network information.
  • the second step may be based on machine learning methods.
  • the second step 220 may be carried out repeatedly, e.g. each time when new user network information is available.
  • the method comprises a third step 230 of determining, based on a current characteristic of a specific user, from the plurality of user profiles one or more candidate user profiles. In embodiments, only one candidate user profile is determined, which is the active user profile.
  • the method comprises a fourth step 240 of making one or more network control function decisions related to a user equipment of the specific user based on an active user profile of the one or more candidate user profiles.
  • One exemplary use of the list of user profiles extracted from the Profiling Engine could be its application in Handover in an LTE/LTE-A network.
  • Some of the following examples describe how the user behavioral prediction in terms of accessed services, access service duration, user mobility, etc. could be applied in the LTE/LTE-A network.
  • the same mechanisms could be applied in other types of cellular networks such as GSM, UMTS, etc. or interworking networks of more than one type e.g., interworking GSM, UMTS, LTE/LTE-A, and WiFi networks, etc.
  • FIG. 3 shows a flow chart of the phases and steps of a further profiling method in accordance with a further embodiment of the present invention.
  • the method shown in FIG. 3 comprises an offline phase 302 and an online phase 304.
  • the offline phase involves gathering of user network information for generating the list of profiles. This significantly reduces the overhead in the network. Compared to context aware mechanisms available in the prior art that directly predict the user behavior, the proposed method does not require online interactions among several network components, thus making the operation of the proposed system more efficient. This characteristic makes the scheme scalable. Also, the grouping of the users based on user profiles has complexity gains by reducing the problem space, since it enables the network to predict the user behavior through user groups (corresponding to users with the same user profile) and not individually, which reduces significantly the problem space and thus handles the curse of dimensionality problem.
  • the offline phase 302 comprises first, second, third, fourth, fifth and eighth steps 310, 320, 330, 340, 350 and 380 as illustrated in FIG. 3.
  • user network information including personal context and user history data are acquired.
  • Step 310 can involve information gathering in a logically centralized entity which is responsi- ble for the processing and the analysis of the data.
  • the logically centralized entity can be referred to as Profiling Engine.
  • the information that it is collected from the Profiling Engine per user equipment may include, but is not limited to the following categories:
  • Network measurements comprising received signal strength, RSRP/RSRQ, backhaul link capacity and quality, packet loss, delays, and interface information.
  • Mobility Information comprising user speed and number of handovers.
  • Service measurements comprising accessed service type, accessed service duration, and accessed service characteristics (i.e., packet size, packet transmission interval, packet reception interval, uplink and downlink bit rate, acceptable jitter, acceptable packet loss, acceptable packet error rate, etc.)
  • Charging Information comprising charging model, and available credits.
  • User Equipment description information comprising available battery, maximum battery charging, device central processing unit description, memory, operating system, screen size, screen resolution, power/energy information (e.g., battery consumption rate, battery level, current CPU, current memory, information about protocols supported by the user equipment (e.g., Type of protocol, Required memory, Required CPU), and data describing the physical interfaces offered by a device (e.g., uplink rate, downlink rate, round trip delay, errors, packets sent, packets received, etc.).
  • power/energy information e.g., battery consumption rate, battery level, current CPU, current memory, information about protocols supported by the user equipment (e.g., Type of protocol, Required memory, Required CPU), and data describing the physical interfaces offered by a device (e.g., uplink rate, downlink rate, round trip delay, errors, packets sent, packets received, etc.).
  • the contextual information can be captured in vectors comprising the above information types.
  • each vector is uniquely characterized by the combination of the unique user identifier (e.g., IMSI) and the extensive time information; other information fields may be used for the unique characterization of each observation such as the location, the battery, the charging status, etc.
  • Each unique information vector can be referred to as "observation”.
  • the measurements may be represented by natural numbers, real positive numbers or event nominal values (e.g., for representing location, time, charging status, social information, etc.).
  • the most relevant parameters are identified. This can be performed e.g. using automated feature selection.
  • step 320 the most relevant parameters are identified by the Profiling Engine, so as to exclude information fields that do not contain relevant information so as to reduce the number of variables involved in the analysis.
  • This process is called dimensionality reduction and aims at simplifying the available dataset, by transformations that embed data in a space of significantly lower dimensions.
  • dimensionality reduction is achieved using feature selection schemes; the mechanisms that could be used for this process include but are not limited to Information Gain, X 2 statistic, and Mutual Information.
  • Such schemes attempt to quantify the influence of a feature to the description of a class and retain only the features which appear to contribute more knowledge to the classification task. All methods of this family are essentially heuristic and operate based on statistical information available in the dataset.
  • Information Gain computes the expected entropy reduction by the categorization of an instance Xi into class CXi due to the value of variable vj.
  • a prerequisite of these methods is that classification information is available in advance for all or at least some indicative observations of the dataset.
  • Other schemes available in the state of the art can also be used (e.g., Chi square).
  • Dimensionality reduction may be achieved by feature extraction schemes as well which aim at constructing combinations of the variables that reduce the dimensions of the dataset while still describing the data with sufficient accuracy (e.g., multidimensional scaling - MDS, Linear Discriminant Analysis, etc.).
  • a third step 330 user profiles (per location, subscription, time, battery level, charging level, mobility pattern, etc.) are created using data mining (using supervised and/or unsupervised learning). To this end, after concluding on the importance of each parameter, the reduced dataset can be used for the extraction of the creation of the profiles by the Profiling Engine.
  • clustering is the process of grouping a set of physical or abstract objects into classes of similar objects. Within a cluster, objects tend to be similar to one another and dissimilar with objects of other clusters. For the extraction of the profiles, in several schemes can be used including but not limited to partitioning methods, hierarchical methods, density-based methods, etc.
  • a list of profiles per user is created. In other words, for each user a list of user profiles that are enabled for this user is created.
  • the Profiling Engine can use the reduced dataset of step 320 with the previously mentioned techniques and similarities among the observations can be identified.
  • groups of obser- vations can be extracted containing several information fields, including the location, time, accessed service (or accessed service type), duration of the accessed service, device type (e.g., high end smartphone, etc.), device status (e.g., battery level, etc.), charging level (i.e., amount of remaining credits).
  • a group of observations will then indicate previous specific behavior (i.e., accessed service and duration) of users under specific conditions (e.g., location, time, device type, device status, charging level, etc.) (Table 1).
  • Table 1 presents a simple example where all different behaviors from all users are categorized in four different exemplary user profiles.
  • this profiling scheme contains all possible combinations, all potential behaviors are being captured, and all users belong to at least one profile for the situation combinations that they have been in the past.
  • user A when he is in a specific timeslot (e.g., 9:00-18:00 in Mondays) will have a certain behavioral profile, when he is located in a specific area (e.g., in the Stadium), under certain preconditions (e.g., based on battery status and/or charging status), but when he is in this area in a different timeslot or under other preconditions then his behavior is being captured by other behavioral profile related to the precondition values.
  • This implies that each user' s behavior is being captured by a list of profiles, which capture the user behavior under all the potential combinations of location, time, and preconditions.
  • Table 2 we capture that user A will behave as indicated by profiles A or C depending on a number of parameters (e.g., Location, Day, Time, Battery and Charging status).
  • the Profiling Engine can provide an accurate prediction of the user behavior, based on his personal past behavior when he is under specific of combinations of location, time, and preconditions.
  • Table 2 provides indicatively a list of potential user profiles of user A, for different locations and preconditions, (e.g. battery status and/or credit charging status). Note that in order to select the precise profile of a user, real time information may be needed to be collected. For example in Table 2, User A will behave as indicated by profile B instead of profile A, since all pre-conditions (e.g., certain location, day, time, and/or charging status) are the same, except from the battery level which has to be collected as information in real-time by a networking device.
  • pre-conditions e.g., certain location, day, time, and/or charging status
  • each user will have a behavioral profile for every combination of the locations, time, and preconditions.
  • This list of profiles is being stored locally either in the Profiling Engine, or in any other logically centralized entity that it is accessible from the other network elements (Step 350 in FIG. 3). From this point, for ease of presentation, we assume that this logically centralized entity resides together with the Profiling Engine and the profiling engine is responsible for the distribution of the profiles to the network devices. However, other logically centralized entities could undertake the role of storing and distributing such information (e.g., Home subscriber Server in LTE/LTE-A network, Home Location Register in GSM, etc.).
  • Table 2 Two dimensional exemplification of the enabled user profiles for user A
  • the user profiles may be associated with parameter ranges that indicate when a specific user profile should be the active user profile.
  • these parameter ranges are non- overlapping such that for each input parameter (e.g. time of the day) only one user profile can be the active user profile.
  • the user profiles are stored in a subscriber server. This may involve storing a table similar to Table 2 (with entries for a plurality of users). In other embodiments, more than one table may be used. For example, a first table can associate input information such as location, day, time and battery status with an associated profile, and a second table can associate a profile with an associated predicted behavior.
  • the first to fifth step 310-350 are carried out initially, before the system can be used for optimizing network performance. They can be repeated when further user network information is available, for example in regular intervals, such as once per day, week or month. Preferably, the collection can occur during hours of low network usage, e.g. during 3am to 4am.
  • the online phase 304 comprises a sixth and a seventh step 360, 370.
  • the profiles are distributed to one or more network components. This can occur "on demand".
  • a network component can request enabled user profiles for a specific user when the specific user is within reach of the network component.
  • step 360 can be carried out repeatedly.
  • the profiles are built and stored they have to be distributed to the network components.
  • a network device e.g., base station, user equipment, or mobility server
  • this device will ask for (via sending the respective message) this from the Profiling Engine and latter will provide the corresponding list of profiles for the location and the time under consideration.
  • the network device will contact the user equipment and/or other network devices for gathering real-time infor- mation regarding the preconditions for the user behavior.
  • These preconditions include but are not limited to battery level and charging status.
  • the task of collecting real-time information e.g., battery level, and/or charging status
  • real-time information e.g., battery level, and/or charging status
  • profile information can be integrated into radio resource management functions. For example, decisions about handovers can be based on an expected behavior that is associated with a certain user profile.
  • an overall network performance is evaluated. For example, it can be evaluated whether decisions that were based on integrating profile information in the seventh step 370 were actually beneficial to network performance.
  • the profiles can then be adjusted accordingly. For example, there may be users whose behavior is "erratic" in the sense that it turns out that it is not reliably possible to predict their behavior. In these cases, it may be preferable not to make network decisions based on an associated user profile. These users may be assigned a "null profile", which merely indicates that no profile-based decisions should be performed.
  • the eighth step 380 which may be considered part of the offline phase, can be carried out periodically, e.g. in regular time intervals.
  • the methods illustrated in FIGs 3 and 4 may be carried out by a profiling engine as shown in FIGs 5 to 10.
  • FIG. 4 shows an exemplary implementation of the process of acquiring and using the list of user profiles from a networking component from the profiling engine.
  • the network system shown in FIG. 4 comprises a UE A 402, a networking component A 404, a profiling engine 406 and a networking component B 408.
  • the profiling engine may comprise the information acquisition unit, the profile generation unit, the profile determination unit and/or the configuration unit.
  • these four units can be realized on a processor of the profiling engine.
  • the network system of FIG. 4 provides an example of the process of acquiring the user profile from a networking component from the Profiling Engine (see step 360 in FIG. 3).
  • a networking component for this example it is Networking Component A
  • UE A user equipment
  • the profiling engine identifies a list of enabled user profiles for user equipment A (corresponding to a user A) based on some characteristics (e.g. time, location, etc.).
  • the Networking Component A stores enabled user profiles of UE A (preferably including associated predicted behavior information) and when it wants to use it, it contacts other networking components for accessing to dynamic real-time information (see steps 440, 450, 460, 470, and 480).
  • the profiling engine provides a list of enabled profiles for user equipment A, for location X, and time Y.
  • the networking component A requests a real-time dynamic information for user equipment A.
  • a fifth step 450 the networking component B provides a real-time dynamic information for user equipment A.
  • the networking component A requests for UE real-time information (e.g. battery level).
  • UE real-time information e.g. battery level
  • the UE A provides UE real-time information (e.g. battery level).
  • step 480 the networking component A stores and uses a profile list.
  • Networking Component A communicates with Networking Component B for accessing real time information related to UE A.
  • Networking component A may communicate with UE A for accessing UE-related real time information which is available only in the UE A (e.g., battery level, etc.). Then the networking component A may use the profile (Step 490 in FIG. 4, see also step 370 in FIG. 3).
  • the processing of obtaining, storing, and using the user profiles may or may not be interconnected in terms of time and thus the Networking Component A may obtain the list of enabled user profiles of UE A, store it, and use it in a totally unrelated timeframe.
  • FIG. 5 illustrates an exemplary implementation of the process of acquiring and using the list of user profiles from a networking component from the Profiling Engine.
  • the system shown in FIG. 5 comprises a UEA 502, a networking component A 504, a profiling engine 506 and a networking component B 508.
  • the networking component A requests for a UE real-time information (e.g. battery level).
  • a UE real-time information e.g. battery level
  • the UE A provides UE real-time information (e.g. battery level).
  • the networking component A requests a list of profiles for user equipment A, for location X, and time Y, and for UE real-time information (e.g. battery level).
  • the networking component B provides real-time dynamic information for user equipment A (e.g. charging status).
  • the profiling engine requests real-time dynamic information for user equipment A (e.g. charging status).
  • the profiling engine identifies a list of profiles for user equipment A based on characteristics (e.g. time, location, etc.) and real-time information (e.g. charging status and/or battery level).
  • the profiling engine provides a user profile, in particular the active user profile, to the networking component A 504.
  • the networking component A 504 stores and uses the UE profile, e.g. to make a network function decision based on the active user profile.
  • FIG. 6 shows an exemplary implementation of the process of acquiring and using the list of user profiles from a networking component from the profiling engine.
  • the system shown in FIG. 6 comprises a UEA 602, a networking component A 604, a profiling engine 606 and a networking component B 608.
  • the networking component A requests a list or profiles for user equipment A, for location X, and time Y.
  • the profiling engine requests real-time dynamic information for user equipment A.
  • the networking component B provides real-time dynamic information for user equipment A.
  • the profiling engine identifies a list of possible profiles for user equipment A based on characteristics (e.g. time, location, etc.) and real-time information (e.g. charging status).
  • the profiling engine provides the networking component 604 with a list of candidate user profiles for UE A.
  • the networking component A requests for a UE real-time information (e.g. battery level).
  • a UE real-time information e.g. battery level
  • the UE A provides UE real-time information (e.g. battery level) to the networking component A 604.
  • UE real-time information e.g. battery level
  • the networking component A stores and uses a list of profiles for UE A 602.
  • the networking component A 604 may determine an active user profile from its list of candidate user profiles based on the real-time information provided from the UE A 602.
  • the eNB When the user equipment A (UE A) performs its initial connection to the network (e.g., via sending an attach request) to an eNB, the eNB communicates with the Mobility Management Entity (MME).
  • MME Mobility Management Entity
  • the MME communicates with the Profiling Engine to obtain the list of the UE profiles.
  • the Profiling Engine maintains the list of profiles for all the UEs in the network; however it could be any type of logically centralized entity that could provide the list of user profile (e.g., the HSS) .
  • the MME retrieves the list of profiles that fits to the characteristics of the attach request (e.g., location, time, etc.) and provides it to the eNB that has requested it. Then, the eNB stores the list of profiles locally. This process is summarized in FIG. 7.
  • FIG. 7 shows an exemplary implementation of the process of acquiring the list of user profiles from eNB from the Profiling Engine in an LTE/LTE-A network.
  • the system shown in FIG. 7 comprises a UEA 702, an eNB 704, a profiling engine HSS 706 and an MME 708.
  • a first step 710 the UE A attaches a request from the UE A.
  • the eNB forwards the attach request from the UE A.
  • the MME requests a list of enabled profiles for UE A from the profiling en- gine/HSS 706.
  • the MME 708 evaluates and stores the list of enabled profiles for UE A based on some current characteristics (e.g. time, location, etc.). Based on this current characteristic, the MME 708 can also determine a set of candidate user profiles for UE A.
  • some current characteristics e.g. time, location, etc.
  • the profiling engine/HSS 706 provides a list of profiles for UE A.
  • This can be the complete list of enabled user profiles for UE A, or it can be the (reduced) set of candidate user profiles that are selected based on the current characteristics.
  • This has the advantage that the eNB 704 is provided with a smaller list of relevant user profiles. The eNB 704 can then use further current characteristics to determine an active user profile from the list of candidate user profiles.
  • FIG. 8 shows an exemplary implementation of a handover process using a list of user profiles in an LTE/LTE-A network.
  • the network system shown in FIG. 8 comprises a UE A 802, an eNB 804 and an online charging server 809.
  • the UE A 802 moves to another eNB and performs a handover, with its handover re- quest it provides real time information related to its status (e.g., battery level, etc.). This real time information can be collected before the execution of a control function like a handover. To this end, the UE A 802 or other devices may periodically send the real time information. In other embodiments, the real time information may be sent asynchronously, e.g. whenever there is a significant deviation compared to the previously reported value.
  • the procedure can be carried out as follows:
  • the UE A 802 submits a handover request to the eNB 804 that it is currently connected to.
  • the handover request may comprise some realtime information from the UE A 802, e.g. a battery level of the UE A 802.
  • the eNB 804 requests the charging status of the UE A 802.
  • Charging status here refers not to a battery charging level, but rather to a credit level e.g. of a prepaid account.
  • the charging status can be requested from the Online Charging Server of the Policy and Charging Control.
  • the charging status may be provided from other nodes of the network.
  • the eNB 804 may communicate with other network components for acquiring other real-time information.
  • the online charging server 809 provides the charging status of the UE A 802 to the eNB 804.
  • the eNB 804 uses it, as well as any other real time information coming from the UE A 804 and/or other network components to determine, in fourth step 840, an active user profile of the UE A that is specific to conditions/constraints of time, location, battery level and/or charging, of the UE A 804. Choosing the active user profile can be based on a list of candidate user profiles for UE A 802 that has previously been stored at the eNB 804.
  • the information about the active user profile enables the eNB 804 to predict whether the UE A 802 is likely to move, or what type of service requests it will perform, the duration of each service access, etc. This information, combined with measurements for the link quality among the UE A 802 and candidate eNBs for handover, will enable the eNB 804 where the UE A 802 resides to decide to which of the neighboring eNBs the UE A 802 should be handed over to.
  • the eNB 804 can decide to which layer UE A 802 should be handed over.
  • Layer means macro-cell, micro-cell, small-cell, etc. Other layers could be included in such layered structure as well, depending on the functionalities of the base station.
  • Metrics that can be used to characterize the quality of the link between a UE and an eNB include but are not limited to the Received Signal Strength (RSS), Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), etc.
  • RSS Received Signal Strength
  • RSRP Reference Signal Received Power
  • RSRQ Reference Signal Received Quality
  • FIG. 9 shows an exemplary implementation of the process of acquiring the list of user profiles from UE from the Profiling Engine in an LTE/LTE-A network for a Cell Selection/- Reselection and/or a Call Admission Control processes.
  • the system shown in FIG. 9 comprises a UEA 902, an eNB 904, a profiling engine 906, an MME 908 and an online charging server 909.
  • exemplary uses of the multi-dimensional user profiles extracted from the Profiling Engine could be its application in Cell Selection/Reselection or Call Admission Control processes in an LTE/LTE-A network.
  • the same mechanisms could be applied in other types of cellu- lar networks such as GSM, UMTS, etc. or interworking networks of more than one type e.g., interworking GSM, UMTS, LTE/LTE-A, and WiFi networks, etc.
  • the UE A 902 When the UE A 902 performs a Tracking Area Update process it receives from the MME 908 (through the eNB 904) a list of candidate user profiles for the respective location, day, etc. Specifically, when the MME 908 receives a TAU request from the UE A 902, as in the case of the handover, it will retrieve the list of user profiles from the Profiling Engine 906. Additionally, as shown in FIG. 9, the MME 908 may interact with the Online Charging Server 909 (Online Charging Server of the Policy and Charging Control) to retrieve the charging status of the UE A 902. Then, MME 908 with the TAU response will also provide to the UE A 902 his list of profiles for the respective location.
  • the Online Charging Server 909 Online Charging Server of the Policy and Charging Control
  • the UE A 902 When the UE A 902 attempts to perform a Cell Selection/Reselection process or a Call Admission request then it will attempt to connect to the eNB or the cell which is most suitable according to the predicted behavior associated with its active user profile. For example, if the predicated behavior associated with its active user profile is that the UE A 902 will be moving with high speed, it will camp in the case of the Cell Selection/Reselection, or attempt to connect in the case of the Call Admission to a macro BS. Similarly if the UE A 902 is predicted to be static or slowly moving then it will attempt to connect to the most suitable micro-BS, small-cell.
  • the prediction of the user behavior is combined with metrics that are used to characterize the quality of the link between the UE A 902 and the eNB 904 and include but are not limited to the Received Signal Strength (RSS), Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), etc.
  • the UE A for the Cell Selection/Reselection or Call Admission Con- trol processes may obtain the list of user profiles profiles periodically, or on a demand basis linked to other processes (e.g., PLMN selection, attach request, etc.).
  • the eNB automatically obtains the list of enabled user profiles or the (reduced) set of candidate user profiles for UE A and stores it locally as illustrated in FIG. 9. Based on the determined active user profile the eNB 904 can redirect the UE A 902 to other eNBs 904 more suitable for his predicted behavior.
  • a first step 910 the UE A 902 sends a TAU request to eNB 902.
  • a second step 920 the eNB 904 forwards the TAU request from the UE A 902 to the MME 908.
  • a third step 930 the MME 908 requests a list of enabled user profiles for the UE A 902 from the profiling engine 906.
  • the profiling engine 906 provides a list of enabled user profiles for the UE A 902.
  • the profiling engine 906 may request a list of enabled user profiles for UE A 902 from a database.
  • a fifth step 950 the MME 908 requests the charging status of the UE A 909 from the online charging server 960.
  • the online charging server 909 provides the charging status of the UE A 902 to the MME 908.
  • the MME 908 evaluates and stores a list of candidate user profiles for UE A 902 based on the dynamic characteristics (e.g. location etc.). In particular, the MME 908 may choose from the list of enabled user profiles for UE A, those user profiles as candidate user profiles that match the dynamic characteristics. Furthermore, the MME 908 may choose an active user profile from the candidate user profiles. In other embodiments, the MME 908 may directly choose an active user profile from the enabled user profiles.
  • the MME 908 sends a TAU response for the UE A to the eNB 904, wherein the TAU response is based on the active user profile.
  • the TAU response may also comprise an indication of the active user profile and/or information about the predicted behavior associated with the active user profile.
  • a ninth step 990 the eNB 904 forwards the TAU response for the UE A 902 to the UE A 902.
  • FIG. 10 shows an exemplary implementation of the process of Call Admission Control using the list of user profiles in an LTE/LTE-A network.
  • the system shown in FIG. 10 comprises a UE A 1002, an eNB 1004, a profiling engine 1006, an MME 1008 and an online charging server 1009.
  • a first step 1010 the UE A 1002 sends an attach request to the eNB 1010.
  • the eNB 1004 forwards the attach request from the UE A 1002 to the MME 1008.
  • the MME 1008 requests a list of user profiles for UE A 1002.
  • the profiling engine 1006 provides the list of user profile for UE A 1002 to the MME 1008.
  • the list of user profiles can be a list of enabled user profiles that are enabled for UE A 1002.
  • the MME 1008 can send with the request one or more current characteristics of the UE A 1002, and the profiling engine 1006 can respond to the request with a list of candidate user profile that correspond to the one or more current characteristics.
  • the MME 1008 evaluates the list of user profiles for UE A 1002 based on dynamic characteristics (e.g. location, etc.).
  • the MME 1008 can determine from the list of enabled user profiles that were provided by the profiling engine 1006 a list of can- didate user profiles, based on the dynamic characteristics.
  • the MME 1008 can determine an active user profile based on the dynamic characteristics.
  • the MME 1008 provides a list of candidate user profiles for UE A 1002 to the eNB 1004. Alternatively, as indicated above, if the MME 1008 has already determined an active user profile, it may provide an indication of the active user profile to the eNB 1004.
  • the eNB 1008 requests the charging status of the UE A 1002 from the Online Charging Server 1009.
  • the Online Charging Server 1009 provides the charging status of the UE A 1002 to the eNB 1004.
  • the seventh and eighth step 1070, 1080 may be skipped if the MME 1008 has provided only one candidate user profile, i.e., the active user profile.
  • the eNB 1004 identifies and uses an active user profile for the call admission control. For example, the eNB 1004 can select an active user profile from the list of candidate user profiles that was provided by the MME 1008.
  • the eNB 1004 sends an attach response for UE A 1002 to the UE A 1002.
  • the attach response can comprise the selected active user profile.
  • the profiling engines 406, 506, 606, 706, 806, 906, 1006 act as profile determination units.
  • the profiling engines 406, 506, 606, 706, 806, 906, 1006 may furthermore also comprise functionalities of a profile generation unit and/or an information acquisition unit.
  • a profile generation unit and/or an information acquisition unit may be provided separately from the profiling engines 406, 506, 606, 706, 806, 906, 1006.
  • the networking components A 404, 504, 604, the networking components B 408, 508, 608, the user equipments 402, 502, 602, 702, 802, 902, 1002, the eNBs 704, 804, 904, 1004, and the MMEs 708, 908 and 1008 can be configured to configure themselves or other components in the network based on an active user profile. Thus, they act as configuration units.
  • embodiments of the invention relate to methods of using a UE profile to assist the network administration, including but not limited to handover optimization, cell selection and re-selection, call admission control, and using big data analytics.
  • the methods can be used, for example, in massive mobile broadband scenarios, in machine type communication scenarios etc.
  • Schemes to extract the user profiles of the users based on their past behavior may be based in supervised or unsupervised learning schemes, which will identify the user behavior in relation to several parameters including but not limited to date, time, location, device status and charging status.
  • Some benefits expected from some of the profiling systems and profiling methods introduced above include accurate user behavior prediction by taking into consideration previous user behaviors.
  • the user behavioral profile can be built by using the user previous behaviors, thus it is accurate under specific preconditions.
  • the user behavioral prediction becomes very accurate. For example, the proposed mechanisms infer that when the user is located in location A, he is highly mobile, and if his cell phone is fully charged then he will watch streaming videos, whereas when the same user is in location A, but his cell phone is low on battery he will only perform short calls (his mobility is not affected). This enables the network to associate the user with the respective more suitable eNB or even access network. This significantly benefits the system throughput, since the predicted user requirements are being handled by the most suitable network/technology.
  • a significant decrease in the number of handovers is expected, since the users are associated to the network according to their predicted mobility and thus low moving users may be associated to small-cells and high moving users to macro-BS.
  • the proper placement of the users in the network, and the reduction of the number of the handovers additionally reduces the experienced latency from the user as well.
  • a method to generate user profiles that comprises one or more systems that capture and analyze user activity in the network, and distribute them into network components.
  • method 1 is combined by the corresponding network components with real time information related to the current status of a UE and used by control functions to optimize the performance of the network.
  • Information from several network components including but not limited to channel conditions, charging status information, serving access node, mobility information, etc.
  • Service specific information from the UEs including but not limited to accessed services, duration of sessions, date, time, etc.
  • Device status information including but not limited to battery level, cpu load, memory load, etc.
  • Information related to the user including but not limited to age, income, contract type, user equipment type (e.g., smartphone, low end cell phone, etc.), user equipment information (e.g., radio access technology interfaces, screen size, CPU, memory, etc.), etc.
  • user equipment type e.g., smartphone, low end cell phone, etc.
  • user equipment information e.g., radio access technology interfaces, screen size, CPU, memory, etc.
  • the method of 1, further comprising the use of the output from method 4 and after using data mining techniques including but not limited to supervised and unsupervised learning produces user profiles based on characteristics including but not limited to:
  • type of services e.g., voice, data
  • the real time acquisition by network components of UE related information includes but is not limited to battery level and charging status.

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Abstract

L'invention concerne un système d'établissement de profil d'utilisateur (100) pour un réseau de communication, comprenant : une unité d'acquisition d'informations (110) configurée pour acquérir des informations de réseau d'utilisateur pour une pluralité d'utilisateurs du réseau de communication, une unité de génération de profil (120) configurée pour générer une pluralité de profils d'utilisateur pour la pluralité d'utilisateurs sur la base des informations de réseau d'utilisateur acquises, une unité de détermination de profil (130) configurée pour déterminer, sur la base d'une caractéristique courante d'un utilisateur spécifique, parmi la pluralité de profils d'utilisateur, un ou plusieurs profils d'utilisateur candidats pour l'utilisateur spécifique, et une unité de configuration (140) qui est configurée pour configurer un élément de réseau du réseau de communication sur la base d'un profil d'utilisateur actif du ou des profils d'utilisateur candidats.
PCT/EP2016/052963 2016-02-12 2016-02-12 Établissement de profil d'équipement utilisateur pour une administration de réseau Ceased WO2017137089A1 (fr)

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