Patent Publication Number: US-2023164160-A1

Title: Web page spectroscopy

Description:
RELATED APPLICATIONS 
     The subject patent application is a continuation of, and claims priority to each of, U.S. patent application Ser. No. 17/061,037, filed Oct. 1, 2020, and entitled “WEB PAGE SPECTROSCOPY,” which is a continuation of U.S. patent application Ser. No. 15/960,713 (now U.S. Pat. No. 10,834,112), filed Apr. 24, 2018, and entitled “WEB PAGE SPECTROSCOPY,” the entireties of which priority applications are hereby expressly incorporated by reference herein in their respective entireties. 
    
    
     TECHNICAL FIELD 
     The subject disclosure relates generally to communications systems, and, for example, to data evaluation and device management in communication networks. 
     BACKGROUND 
     In communication networks, various data is available related to communication devices and usage of the communication devices. Therefore, unique opportunities exist for application of the available data while maintaining anonymity of the data in order to address privacy concerns in communication networks. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various non-limiting embodiments are further described with reference to the accompanying drawings in which: 
         FIG.  1    illustrates an example, non-limiting, communications system for facilitating web page spectroscopy in accordance with one or more embodiments described herein; 
         FIG.  2    illustrates an example, non-limiting, system that employs automated learning to facilitate one or more of the disclosed aspects in accordance with one or more embodiments described herein; 
         FIG.  3    illustrates an example, non-limiting, flow diagram for machine/deep learning in accordance with one or more embodiments described herein; 
         FIG.  4    illustrates an example, non-limiting, system that identifies parameters of input data and implements one or more actions based on the identified parameters in accordance with one or more embodiments described herein; 
         FIG.  5    illustrates an example, non-limiting, chart of experimental results of detected tethering in accordance with one or more embodiments described herein; 
         FIG.  6    illustrates an example, non-limiting, chart of experimental results of detected not tethering in accordance with one or more embodiments described herein; 
         FIG.  7    illustrates an example, non-limiting, notched box plot in accordance with one or more embodiments described herein; 
         FIG.  8    illustrates a distribution of the notched box plot of  FIG.  7    in accordance with one or more embodiments described herein; 
         FIG.  9    illustrates example, non-limiting plots comparing flow concurrency between a tethered group and a non-tethered group in accordance with one or more embodiments described herein; 
         FIG.  10    illustrates example, non-limiting plots comparing uplink packet counts between a tethered group and a non-tethered group in accordance with one or more embodiments described herein; 
         FIG.  11    illustrates example, non-limiting plots comparing downlink packet counts between a tethered group and a non-tethered group in accordance with one or more embodiments described herein; 
         FIG.  12    illustrates example, non-limiting plots comparing uplink domain name system activity between a tethered group and a non-tethered group in accordance with one or more embodiments described herein; 
         FIG.  13    illustrates example, non-limiting plots comparing downlink domain name system activity between a tethered group and a non-tethered group in accordance with one or more embodiments described herein; 
         FIG.  14    illustrates example, non-limiting plots comparing uplink synchronize/acknowledge occurrences between a tethered group and a non-tethered group in accordance with one or more embodiments described herein; 
         FIG.  15    illustrates example, non-limiting plots comparing downlink synchronize/acknowledge occurrences between a tethered group and a non-tethered group in accordance with one or more embodiments described herein; 
         FIG.  16    illustrates an example, non-limiting, geo-fence within a defined geographic area in accordance with one or more embodiments described herein; 
         FIG.  17    illustrates an example, non-limiting, plot or a representation of airplane arrivals based on mined communication device data in accordance with one or more embodiments described herein; 
         FIG.  18    illustrates an example, non-limiting, plot or representation of cumulative airport arrival frequency by cell tower in accordance with one or more embodiments described herein; 
         FIG.  19    illustrates an example, non-limiting, method for determining behaviors associated with one or more communication packet flows in accordance with one or more embodiments described herein; 
         FIG.  20    illustrates an example, non-limiting, method for determining unauthorized tethering of a communication device in accordance with one or more embodiments described herein; 
         FIG.  21    illustrates an example block diagram of an example mobile handset operable to engage in a system architecture that facilitates wireless communications according to one or more embodiments described herein; and 
         FIG.  22    illustrates an example block diagram of an example computer operable to engage in a system architecture that facilitates wireless communications according to one or more embodiments described herein. 
     
    
    
     DETAILED DESCRIPTION 
     One or more embodiments are now described more fully hereinafter with reference to the accompanying drawings in which example embodiments are shown. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the various embodiments. However, the various embodiments can be practiced without these specific details (and without applying to any particular network environment or standard). 
     Discussed herein are various aspects that relate to web page spectroscopy. In an example, one or more communication network packets can be captured from respective electronic devices and input into a machine learner component that can be trained based on application of one or more machine learning techniques on the one or more communication network packets. Based on the training, various activities associated with the one or more electronic devices (and related users of the one or more electronic devices) can be identified and analyzed. Based on the identification and analysis, according to an implementation, unauthorized tethering/hot-spot from hacking/jail-breaking the mobile device can be determined and action can be taken to disrupt the unauthorized use. In an additional or alternative implementation, the identification and analysis can be utilized to provide useful data to the respective users of the one or more electronic devices. In an example, the useful data can include information relevant to the interests of the user, including targeted advertisements. 
     In an example, conventional identification of user activity through their respective electronic devices has relied upon Deep Packet Inspection (DPI) or agents installed on the device. DPI has become ineffective because encryption/SSL/HTTPS is being utilized for security and privacy issues. In addition, agents are expensive and prone to viruses. Therefore, the various aspects provided herein can be utilized to identify various subscribers (e.g., users) activities with their electronic devices without relying on DPI and/or device-local agents. Thus, the various aspects, can apply machine learning techniques in order to identify unauthorized tethering. 
     Advantages of the disclosed subject matter include the use of web page spectroscopy to determine habits of users of mobile devices (e.g., a subscriber community) without the invasion of privacy associated with DPI methods and/or the costly and virus/attack prone agent technologies. As an example, most web pages possess a relatively stable spectroscopic signature that can be utilized to identify the actual web site or action on the web site. As another advantage, more effective targeted marketing can be achieved when a subscriber&#39;s habits are understood. 
     In one embodiment, described herein is a system that can comprise a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations. The operations can comprise receiving first data that describes a first communication packet flow and second data that describes a second communication packet flow. The first communication packet flow and the second communication packet flow can be associated with a communication device of a communications network. The operations can also comprise training a model based on the first data and the second data, as a result of which the model is trained to detect respective behaviors represented by the first data and the second data. Further, the operations can comprise extracting a common parameter from third data that describes a third communication packet flow and fourth data that describes a fourth communication packet flow based on the model. 
     In an example, the common parameter can be associated with web page displays. Further to this example, the operations can comprise identifying overlapping web page displays of the web page displays that are associated with the communication device. The operations can also comprise determining that tethering is occurring at the communication device based on the overlapping web page displays. 
     Further to the above example, identifying the overlapping web page displays can comprise analyzing respective spectroscopic signatures associated with the web page displays. The respective spectroscopic signatures can indicate respective websites associated with the overlapping web page displays. Additionally, or alternatively, the respective spectroscopic signatures can indicate interactions associated with the overlapping web page displays. 
     In an example, the operations can also comprise determining an accuracy level of the tethering determined to be occurring at the communication device based on the overlapping web page displays. Further, the operations can comprise inputting the common parameter into the model and retraining the model based on the first data, the second data, the third data, the fourth data, the common parameter, and the accuracy level. 
     According to another example, receiving the first data and the second data can comprise receiving first domain name system traffic information and second domain name system traffic information. In another example, extracting the common parameter can comprise detecting respective stable spectroscopic signatures of the first data, the second data, the third data, and the fourth data. 
     In yet another example, receiving the first data and the second data can comprise receiving anonymous traffic of the communications network. Further to this example, training the model can comprise training the model to detect respective behaviors represented by the first data and the second data to at least a defined level of confidence. 
     According to another example, receiving the first data and the second data can comprise receiving a first header that identifies the first communication network packet flow and a second header that identifies the second communication network packet flow. 
     Receiving the first data and the second data can comprise receiving first metadata associated with the first communication network packet flow and second metadata associated with the second communication network packet flow. 
     Training the model can comprise training the model to detect respective fingerprints of third metadata associated with the third communication network packet flow and fourth metadata associated with the fourth communication network packet flow based on known fingerprints of the first metadata and the second metadata. 
     According to another embodiment, provided herein is a machine-readable storage medium that comprises executable instructions that, when executed by a processor of a network device of a wireless network, facilitate performance of operations. The operations can comprise receiving a group of communication packet flows that comprise respective identified usage parameters. The operations can also comprise, based on the group of communication packet flows, training a model to detect the respective identified usage parameters with a defined level of confidence. Further, the operations can also comprise, based on the model and based on the defined level of confidence, determining a usage parameter of a communication packet flow received by the network device. 
     In an example, the operations can further comprise detecting scripts executing concurrently in the communication packet flow received by the network device. The scripts can be associated with different web pages and indicate concurrent usage at a communication device of the communication network. 
     In further detail,  FIG.  1    illustrates an example, non-limiting, communications system  100  for facilitating web page spectroscopy in accordance with one or more embodiments described herein. 
     The communications system  100  can comprise one or more network devices (illustrated as a network device  102 ) and one or more user equipment or mobile devices (illustrated as a communication device  104 ). The network device  102  can be included in a group of network devices of a wireless network (e.g., the communications system  100 ). It is noted that although only a single network device and a single mobile device are illustrated, the communications system  100  can comprise a multitude of network devices and/or a multitude of mobile devices. 
     The network device  102  can comprise a receiver component  106 , a trainer component  108 , an extractor component  110 , a transmitter component  112 , at least one processor component  114 , at least one memory  116 , and/or at least one storage  118 . The communication device  104  can comprise a transmitter/receiver component  120 , at least one processor component  122 , at least one memory  124 , and/or at least one storage  126 . 
     The receiver component  106  can receive input data  128  that describes communication packet flows  130  transmitted, as output data, from the communication device  104  (e.g., via the transmitter/receiver component  120 ). The communication packet flows  130  can be communications from the communication device  104  to the network device  102 , to other network devices, and/or to other mobile devices in the communications system  100 . 
     The trainer component  108  can train a model  132  on the input data  128 . The model  132  (e.g., a detection model, a classification model, or another type of model) can be a model into which data can be fed. The model can be trained on data that comprises known content. For example, the model can be fed strictly data from known communication packet flows such that the model is trained to detect the behaviors consistently. Upon or after the model is trained to accurately predict the behaviors, the model can learn to detect the same behaviors or unknown behaviors with a high level of confidence. The trainer component  108  can train the model  132  to detect behavior of the data to a defined level of confidence. 
     Based on the defined level of confidence, the extractor component  110  can extract, from newly received data that describes communication packet flows newly transmitted by the communication device  104 , a common parameter associated with the newly received data. The common parameter can be output, by the transmitter component  112  as output data  134 . 
     According to an example, the receiver component  106  can receive first data (e.g., input data  128 ) that describes a first communication packet flow (e.g., included in the communication packet flows  130 ) and second data (e.g., input data  128 ) that describes a second communication packet flow (e.g., included in the communication packet flows  130 ). Using the first data and the second data, the trainer component  108  can train the model  132  to detect respective behaviors of the first data and the second data. For example, the first data and the second data can comprise known content (e.g., known behaviors) and the model  132  can be trained to detect the known content or known behaviors. Upon or after the model  132  has been trained on the first data and the second data, to a defined level of confidence, subsequent data from subsequent communication packet flows can be input into the model  132  to determine one or more behaviors associated with the subsequent data. 
     The receiver component  106  can receive input data  128  that comprises first data that describes a first communication packet flow and second data that describes a second communication packet flow. In an example, the first data can comprise first domain name system traffic information and the second data can comprise second domain name system traffic information. Further, the third data can comprise third domain name system traffic information and the fourth data can comprise fourth domain name system traffic information. In another example, the first data, the second data, the third data, and the fourth data can comprise anonymous communication network traffic. 
     In another example, the first data can comprise a first header that identifies the first communication network packet flow and the second data can comprise a second header that identifies the second communication network packet flow. Further to this example, the third data can comprise a third header that identifies the third communication network packet flow and the fourth data can comprise a fourth header that identifies the fourth communication network packet flow. 
     According to another example, the first data can comprise first metadata associated with the first communication packet flow and the second data can comprise second metadata associated with the second communication network packet flow. Further to this example, the third data can comprise third metadata associated with the third communication network packet flow and the fourth data can comprise fourth metadata associated with the fourth communication network packet flow. 
     According to some implementations, to determine the common parameter, the extractor component  110  can detect respective stable spectroscopic signatures of the first data, the second data, the third data, and the fourth data. 
     In accordance with another implementation, the trainer component  108  can train the model to detect respective fingerprints of third metadata associated with the third communication network packet flow and fourth metadata associated with the fourth communication network packet flow based on known fingerprints of the first metadata and the second metadata. 
     The respective one or more memories (e.g., the at least one memory  116 , the at least one memory  124 ) can be operatively coupled to the respective one or more processors (e.g., the at least one processor component  114 , the at least one processor component  122 ). The respective one or more memories (e.g., the at least one memory  116 , the at least one memory  124 ) can store protocols associated with facilitating machine learning and detection of behaviors based on communication packet flows in a communications network as discussed herein. Further, the respective one or more memories (e.g., the at least one memory  116 , the at least one memory  124 ) can facilitate action to control communication between the network device  102 , the communication device  104 , other network devices, and/or other mobile devices such that the communications system  100  can employ stored protocols and/or algorithms to achieve improved communications in a wireless network as described herein. 
     It should be appreciated that data store (e.g., memories) components described herein can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. By way of example and not limitation, nonvolatile memory can include Read Only Memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), electrically erasable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM), which acts as external cache memory. By way of example and not limitation, RAM is available in many forms such as Synchronous RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). Memory of the disclosed aspects are intended to comprise, without being limited to, these and other suitable types of memory. 
     The respective processors (e.g., the at least one processor component  114 , the at least one processor component  122 ) can facilitate improvements to achieve transmission diversity in a communication network. The processors (e.g., the at least one processor component  114 , the at least one processor component  122 ) can be processors dedicated to analyzing and/or generating information received, processors that control one or more components of the communications system  100 , and/or processors that both analyze and generate information received and control one or more components of the communications system  100 . 
       FIG.  2    illustrates an example, non-limiting, system  200  that employs automated learning to facilitate one or more of the disclosed aspects in accordance with one or more embodiments described herein. Repetitive description of like elements employed in other embodiments described herein is omitted for sake of brevity. The system  200  can comprise one or more of the components and/or functionality of the communications system  100  and vice versa. 
     As illustrated, the network device  102  can comprise a machine learning and reasoning component  202  that can be utilized to perform predictive machine learning and to automate one or more of the disclosed aspects. The machine learning and reasoning component  202  can employ automated learning and reasoning procedures (e.g., the use of explicitly and/or implicitly trained statistical classifiers) in connection with performing inference and/or probabilistic determinations and/or statistical-based determinations in accordance with one or more aspects described herein. 
     For example, the machine learning and reasoning component  202  can employ principles of probabilistic and decision theoretic inference. Additionally, or alternatively, the machine learning and reasoning component  202  can rely on predictive models constructed using machine learning and/or automated learning procedures. Logic-centric inference can also be employed separately or in conjunction with probabilistic methods. 
     The machine learning and reasoning component  202  can infer usage of the communications network by one or more communication devices (e.g., the communication device  104 ) by obtaining knowledge about websites visited, applications executing on (or through) the communication device  104 , programs being utilized by the communication device  104 , or combinations thereof. Based on this knowledge, the machine learning and reasoning component  202  can make an inference based on preferences of an anonymized user, activities of an anonymized user, usage of a communication device, network access by a communication device, or combinations thereof 
     As used herein, the term “inference” refers generally to the process of reasoning about or inferring states of a system, a component, a module, an environment, and/or devices from a set of observations as captured through events, reports, data and/or through other forms of communication. Inference can be employed to identify usage of a communication device (e.g., tethering at the communication device), actions and/or preferences of a user of a communication device, or can generate a probability distribution over states, for example. The inference can be probabilistic. For example, computation of a probability distribution over states of interest based on a consideration of data and/or events. The inference can also refer to techniques employed for composing higher-level events from a set of events and/or data. Such inference can result in the construction of new events and/or actions from a set of observed events and/or stored event data, whether or not the events are correlated in close temporal proximity, and whether the events and/or data come from one or several events and/or data sources. Various classification schemes and/or systems (e.g., support vector machines, neural networks, logic-centric production systems, Bayesian belief networks, fuzzy logic, data fusion engines, and so on) can be employed in connection with performing automatic and/or inferred action in connection with the disclosed aspects. 
     The various aspects (e.g., in connection with applying machine learning to detect off-plan tethering, unintended consequences resulting from actions of a communication device that can shorten a machine-learning cycle, and so forth) can employ various artificial intelligence-based schemes for carrying out various aspects thereof. For example, a process for determining if a particular communication device is utilized as a tethering point for other devices can be performed and/or whether an action of the communication device (e.g., entering/exiting airplane mode) indicates a pattern can be enabled through an automatic classifier system and process. 
     A classifier is a function that maps an input attribute vector, x=(x1, x2, x3, x4, xn), to a confidence that the input belongs to a class. In other words, f(x)=confidence(class). Such classification can employ a probabilistic and/or statistical-based analysis (e.g., factoring into the analysis utilities and costs) to provide a prognosis and/or or infer one or more actions that should be employed, based on the information obtained, and which actions should be automatically performed. In the case of actions, for example, attributes can be identification of usage of a communication device (e.g., tethering) and/or patterns indicating user preferences and the classes are criteria of the usage of the communication device (e.g., authorized or unauthorized) and/or targeted electronic messages provided based on the user preferences. 
     A Support Vector Machine (SVM) is an example of a classifier that can be employed. The SVM operates by finding a hypersurface in the space of possible inputs, which hypersurface attempts to split the triggering criteria from the non-triggering events. Intuitively, this makes the classification correct for testing data that can be similar, but not necessarily identical to training data. Other directed and undirected model classification approaches (e.g., naïve Bayes, Bayesian networks, decision trees, neural networks, fuzzy logic models, and probabilistic classification models) providing different patterns of independence can be employed. Classification as used herein, can be inclusive of statistical regression that is utilized to develop models of priority. 
     One or more aspects can employ classifiers that are explicitly trained (e.g., through a generic training data) as well as classifiers that are implicitly trained (e.g., by observing communication device behavior, by receiving extrinsic information, and so on). For example, SVM&#39;s can be configured through a learning or training phase within a classifier constructor and feature selection module. Thus, a classifier(s) can be used to automatically learn and perform a number of functions, including but not limited to determining according to predetermined criteria when to implement an action (e.g., disabling a tethering option, providing an electronic communication, and so on), which action to implement, what actions to group together, relationships between actions, and so forth. The criteria can include, but is not limited to, current information, historical information, and so forth. 
     Additionally, or alternatively, an implementation scheme (e.g., a rule, a policy, and so on) can be applied to control and/or regulate communication device behavior and resulting actions, privileges, and so forth. In some implementations, based upon a predefined criterion, the rules-based implementation can automatically and/or dynamically interpret communication device behavior. In response thereto, the rule-based implementation can automatically interpret and carry out functions associated with the communication device behavior by employing a predefined and/or programmed rule(s) based upon any desired criteria. 
       FIG.  3    illustrates an example, non-limiting, flow diagram  300  for machine/deep learning in accordance with one or more embodiments described herein. Repetitive description of like elements employed in other embodiments described herein is omitted for sake of brevity. The flow diagram  300  can be utilized and/or can comprise one or more of the components and/or functionality of the communications system  100 , the system  200 , and vice versa. 
     In various embodiments, the systems discussed herein can be classification computing systems associated with technologies such as, but not limited to, communication technologies, computing technologies, artificial intelligence technologies, object classification technologies, and/or other digital technologies. The systems can employ hardware and/or software to solve problems that are highly technical in nature (e.g., determine usage of a communication network, determine tethering associated with one or more communication devices in the communication network, determine preferences of a user of a communication device, and so on. For example, the systems can extract respective electronic data (e.g., usage parameters, fingerprint data, and so on) from a group of communication packets, analyze the respective electronic data, and classify patterns across electronic data of the respective electronic data, that are not abstract and that cannot be performed as a set of mental acts by a human. For example, the electronic data received can be complex data that have not previously been analyzed by the system, and which comprise interleaving data that are difficult (or impossible) to distinguish with the human mind or the human eye. Further, the number of communication packets received and associated data contained therein can be a large volume, which a human could not possibly automatically (e.g., within a matter of seconds or less) and consistently accurately process as discussed herein (e.g., identify data within the communication packets, extract the data, and perform classification of the extracted data). Further, in certain embodiments, some of the processes performed can be performed by one or more specialized computers (e.g., one or more specialized processing units, a specialized computer with a classification computing component, a specialized data classification model, etc.) to carry out defined tasks related to machine learning and data classification as discussed herein. 
     Machine Learning is a data-driven, artificial intelligence approach using algorithms that learn from and make determinations about the data provided to them. As illustrated, input data  128  can received (e.g., via the receiver component  106 ) and utilized as training data  302 , which can be retained in a data store (e.g., the at least one memory  116 , the at least one storage  118 ). In an example, the input data  128  can be positive packet flows and/or negative packet flows corresponding (respectively) to tethered/non-tethered subscriber activity. In another example, the input data can be Domain Name System (DNS) traffic after an “airplane mode” is turned off at the communication device (e.g., the communication device  104 ). 
     The training data  302  can comprise multiple data points x, which can undergo feature extraction  304  (e.g., via the extractor component  110 ). A predictor h(x) can be established and utilized to train a machine learning model  306  (e.g., the trainer component  108 , the machine learning and reasoning component  202 ) during the training phase. For example, during the training phase, for the input value x, there can be a corresponding output y, that is known in advance. For each example, the difference between the known, correct value y and the predicted value h(x) can be determined. Over time, the value of h(x) can be modified to result in more accurate predictions (e.g., a defined level of confidence). 
     Upon or after the initial training phase as more training data  302  is received (e., the input data  128 ), feature extraction  304  is performed and the predicted value h(x) can be processed by the machine learning model  306  (e.g., the model  132 ) to determine an output ŷ. In an example, the output ŷ can be a determination whether tethering is being performed or is not being performed. In another example, the output ŷ can include an identification of an application, a website, a location in a website, and/or other information associated with one or more communication devices. 
     The output ŷ can be compared with the output y, to determine a quality metric  308  (e.g., a defined level of confidence), such as through a feedback loop. Based on result of the comparison a machine learning algorithm  310  can be updated and the changes ŵ to the algorithm can be provided to the machine learning model  306  to further train the machine learning model  306 . 
       FIG.  4    illustrates an example, non-limiting, system  400  that identifies parameters of input data and implements one or more actions based on the identified parameters in accordance with one or more embodiments described herein. Repetitive description of like elements employed in other embodiments described herein is omitted for sake of brevity. The system  400  can comprise one or more of the components and/or functionality of the communications system  100 , the system  200 , the flow diagram  300 , and vice versa. 
     As illustrated, the network device  102  can comprise an identifier component  402 , a determiner component  404 , and a feedback component  406 . As discussed, the machine learning and reasoning component  202  can be utilized for predictive machine learning on the input data  128 . 
     The identifier component  402  can identify overlapping web page displays of the web page displays that are associated with the communication device  104 . For example, overlapping web page displays can indicate that more than one communication device is interacting with web pages. Further, the determiner component  404  can determine that tethering is occurring at the communication device based on the overlapping web pages displays. For example, through the communication device  104 , a first communication device can be accessing a first web page and a second communication device can be accessing a second web page (where one of the first communication device or the second communication device can be the communication device  104 ). 
     The identifier component  402  can identify the overlapping web page displays based on an analysis of respective spectroscopic signatures associated with the web page displays. In an example, the respective spectroscopic signatures can indicate respective websites associated with the overlapping web page displays. In another example, the respective spectroscopic signatures can indicate interactions associated with the overlapping web page displays. 
     Further, the network device  102  can comprise a feedback component  406  that can input newly received information and predicted information to the trainer component  108  and/or the machine learning and reasoning component  202 . For example, an accuracy level of the output data  134  can be determined (e.g., via the extractor component  110 , the machine learning and reasoning component  202 , or another system component). The output data  134  and related data (e.g., the third data, the fourth data) can be fedback to the model  132 , which can be retrained based on the newly received data. 
     As discussed herein, an implementation relates to detecting tethering performed by mobile subscribers whose plan does not include a tethering option. Authorized tethering, namely tethering that has been requested by the client and confirmed by look-up during session set-up, can be easy to detect (e.g., Access Point Name (APN) string contains “broadband”). Unauthorized tetherers jailbreak their phones to avoid the confirmation step by hiding their tethering to appear as a standard data connection (e.g., APN string is “nxtgenphone”). Current detection uses deep packet inspection (DPI) to look for incongruous activity (e.g., a first manufacturer operating system update request from second manufacturer phone, where the first manufacturer and the second manufacturer are competitors). Problems associated with current detection include that the approach is limited (e.g., need to explicitly look for specific incongruities), detection is conducted after the fact using historical data in a database, and increasing use of encryption HyperText Transfer Protocol Secure (HTTPS) is making DPI-based techniques null and void. 
     The various aspects provided herein can operate at scale and, thus, can handle nationwide packet rates of over 2.4 Terabytes per second (Tbps). Further, the disclosed aspects can operate at near real-time (e.g., can detect tethering as it is happening). In addition, the disclosed aspects can operate in the presence of encryption, namely HTTPS, and/or can operate without using DPI. 
     According to various experiments, a machine learning model achieved at least an eighty-seven percent accuracy level in identifying tethering using real data collected from 721 clients. It is noted that in accordance with one or more implementations described in this disclosure, users (e.g., clients) can opt-out of providing personal information, demographic information, location information, proprietary information, sensitive information, or the like in connection with data gathering aspects. Moreover, one or more implementations described herein may provide for anonymizing collected, received, and/or transmitted data. Further, a user can opt-out of providing information at any time, regardless of whether the user previously opted-in to providing the information. 
     For the experiment, the model was trained with the following seven “features” extracted from every packet: flows (e.g., the bi-direction channel/tunnel between the mobile subscriber and a web page); up/down link counts; packets; DNS lookups; TCP handshakes (SYN/SYNACK/ACK). These features were selected in order for the model to “learn” when the contents from multiple web pages were flowing to a SUB at approximately the same time. 
     For example,  FIG.  5    illustrates an example, non-limiting, chart  500  of experimental results of detected tethering in accordance with one or more embodiments described herein. In this example, tethering was detected based on the APN string containing “broadband.” The vertical or Y axis  502  represents the unique flows during a defined time period (e.g., three seconds). The horizontal or X axis  504  represents the number of SUBs. The chart  500  illustrates the result from 2,480 different SUBs being analyzed. 
     Further,  FIG.  6    illustrates an example, non-limiting, chart  600  of experimental results of detected not tethering in accordance with one or more embodiments described herein. In this example, tethering was detected based on the Access Point Name (APN) string containing “nxtgenphone.” The chart  600  illustrates the result of 3,841 different SUBs being analyzed. Unauthorized tethering is indicated by the three arrows at  602 . 
       FIG.  7    illustrates an example, non-limiting notched box plot  700  in accordance with one or more embodiments described herein.  FIG.  8    illustrates a distribution of the notched box plot of  FIG.  7    in accordance with one or more embodiments described herein.  FIGS.  7  and  8    are provided to explain further details of the disclosed aspects. 
     Box plots succinctly display the range of a set of data values in terms of their quartiles (the box or Inter-Quartile Region (IQR  802  of  FIG.  8   )), as well as variability beyond the quartiles (the whiskers or 1.5×IQR), and the outlying points beyond the whiskers. It is noted that if the notches of two plots do not overlap, this is ‘strong evidence’ that the two medians differ. As illustrated the example includes two groups (e.g., a first group  702  and a second group  704  plotted along a vertical range  706  (e.g., 0 to 100). 
     The median value 804 or second quartile (Q2) is the 50/50 point. At this point, half the data lies above and half below the median value 804. Q1 is the 25/75 point and Q3 is the 75/25 point. Although box plots make no assumptions about the underlying statistical distribution, it can be helpful to think about the box plot of a normal distribution  806 , as illustrated at the bottom of  FIG.  8   . 
       FIG.  9    illustrates example, non-limiting plots  900  comparing flow concurrency between a tethered group and a non-tethered group in accordance with one or more embodiments described herein. Illustrated on the vertical or Y axis  902  is the concurrent flows in a defined time window (e.g., a three second time window for this experiment). Illustrated on the horizontal or X axis  904  are data sets for flows. 
     A first group comprises non-tethered devices  906  comprising a quantity of 1,652 devices. A second group comprises tethered devices  908  comprising a quantity of  841  devices. As indicated by arrows  910  and  912 , there is little overlap between the two boxplots. The lack of overlap indicates that concurrent flows are a positive indicator of web page overlap. 
       FIG.  10    illustrates example, non-limiting plots  1000  comparing uplink packet counts between a tethered group and a non-tethered group in accordance with one or more embodiments described herein. The Y axis  1002  represents concurrent communication device (or mobile station (MS)) to network (NW) packets (e.g., MS-to-NW packets) over a three second window. The X axis  1004  represents the data sets for MS-to-NW packets. The plots  1000  are for the non-tethered devices  906  and the tethered devices  908 . 
       FIG.  11    illustrates n example, non-limiting plots  1100  comparing downlink packet counts between a tethered group and a non-tethered group in accordance with one or more embodiments described herein. The Y axis  1102  represents concurrent communication MS-to-NW packets over a three second window. The X axis  1104  represents the data sets for NW-to-MS packets. The plots  1100  are for the non-tethered devices  906  and the tethered devices  908 . 
       FIG.  12    illustrates example, non-limiting plots  1200  comparing uplink DNS activity between a tethered group and a non-tethered group in accordance with one or more embodiments described herein. The Y axis  1202  represents concurrent MS-to-NW DNS over a three second window. The X axis  1204  represents the data sets for MS-to-NW DNS. The plots  1200  are for the non-tethered devices  906  and the tethered devices  908 . 
       FIG.  13    illustrates example, non-limiting plots  1300  comparing downlink DNS activity between a tethered group and a non-tethered group in accordance with one or more embodiments described herein. The Y axis  1302  represents concurrent NW-to-MS DNS over a three second window. The X axis  1304  represents the data sets for MS-to-NW DNS. The plots  1300  are for the non-tethered devices  906  and the tethered devices  908 . 
       FIG.  14    illustrates example, non-limiting plot s 1400  comparing uplink synchronize/acknowledge (SYN/ACK) occurrences between a tethered group and a non-tethered group in accordance with one or more embodiments described herein. The Y axis  1402  represents concurrent MS-to-NW SYN/ACK over a three second window. The X axis  1404  represents the data sets for MS-to-NW SYN/ACK. The plots  1400  are for the non-tethered devices  906  and the tethered devices  908 . 
       FIG.  15    illustrates example, non-limiting plots  1500  comparing downlink SYN/ACK occurrences between a tethered group and a non-tethered group in accordance with one or more embodiments described herein. The Y axis  1502  represents concurrent MS-to-NW SYN/ACK over a three second window. The X axis  1504  represents the data sets for NW-to-MS SYN/ACK. The plots  1500  are for the non-tethered devices  906  and the tethered devices  908 . 
     According to another aspect, a state of a communication device can be utilized to shorten a machine-learning cycle. Various data can be statistically more likely to correspond to a particular action performed by a user on their communication device. In an example, the action can be enabling and/or disabling an “airplane-mode” of the communication device. It is noted that device actions (e.g., adjusting volume, pausing a video, enabling/disabling airplane-mode) are not transmitted over the network. However, these actions can be inferred with the assistance of additional information that is transmitted. For example, location information can provide valuable information. 
     Enabling/disabling airplane-mode can have the undesirable (or desirable if the goal is to understand network traffic patterns) side-effect of clearing the DNS cache. An empty DNS cache results in a significant burst of requests from both foreground and background applications. For example, passengers arriving at an airport can be anxious to reconnect with their communication devices. Thus, empty DNS caches and multiple disabling of airplane-mode can provide a wealth of training data. 
       FIG.  16    illustrates an example, non-limiting, geo-fence within a defined geographic area in accordance with one or more embodiments described herein. Illustrated is a screen shot  1600  of portion of an airport (e.g., Dulles International Airport). The geo-fence  1602  is represented by the circle. The geo-fence  1602  is a location where a common event occurs on a large scale. In the case of the airport, the geo-fence  1602  represents an area where multiple devices are exhibiting behavior of an empty DNS cache and activation of communication devices (e.g., airplane mode is turned off). Accordingly, the geo-fence  1602  is around an area where airplanes land and passengers are permitted to use their respective communication devices. For example, many passengers turn off airplane-mode immediately, or shortly after, the wheels of the airplane touch the runway. 
       FIG.  17    illustrates an example, non-limiting, plot  1700  or a representation of airplane arrivals based on mined communication device data in accordance with one or more embodiments described herein. The Y axis  1702  represents the quantity of communication devices and the X axis  1704  represents time. A first set of spikes in the data  1706  and a second set of spikes in the data  1708  represent linear growth (e.g., cumulative) corresponding to flight arrivals. 
       FIG.  18    illustrates an example, non-limiting, plot  1800  or representation of cumulative airport arrival frequency by cell tower in accordance with one or more embodiments described herein. The Y axis  1802  represents the quantity of communication devices and the X axis  1804  represents cell tower identifiers (not labeled for purposes of simplicity). Additionally, the most popular domains that are accessed upon or after (e.g., within a few minutes after) the plane lands can be determined, as well as other information. 
       FIG.  19    illustrates an example, non-limiting, method  1900  for determining behaviors associated with one or more communication packet flows in accordance with one or more embodiments described herein. At  1902 , a model can be trained using, as inputs to the model, identified communication packet flows that comprise respective fingerprint data. Training the model can comprise training the model to detect the respective fingerprint data with at least a defined level of confidence. In an example, the respective fingerprint data can comprise respective domain name system signatures that exhibit properties defined to be stable properties. In another example, the respective fingerprint data can comprise domain name system traffic of an application executing on mobile devices within the communications network. 
     Further, at  1904 , a pattern of use of a received communication packet flow received by the system can be identified based on detection of fingerprint data of the received communication packet flow data with at least the defined level of confidence. The fingerprint data can be detected based on the model. 
     As discussed herein, web page spectroscopy refers to using machine learning algorithms on network packet data (e.g., headers only) and/or meta-data describing the packet flows in order to identify various aspects about the web page itself. For example, web page content can be displayed in a highly concurrent manner by javascript executing many AJAX threads in parallel. This noticeable burst of concurrency creates a storm of activity on the network ranging from DNS lookups to TCP handshakes (SYN/SYNACK/ACK). Analysis of this “burst” has been equated herein with the field of spectroscopy and instead of light or chemical properties, the spectroscopy discussed herein analyzes network properties. The machine learning and training of the model can be facilitated by feeding actual anonymous network traffic as training data so that the model can continuously improve. In an example, spectroscopy can be utilized to identify overlapping web page displays which is a strong indicator of tethering 
       FIG.  20    illustrates an example, non-limiting, method  2000  for determining unauthorized tethering of a communication device in accordance with one or more embodiments described herein. At  2002 , one or more communication packet flows can be received. The communication packet flows can comprise respective fingerprint data. A model can be trained, at  2004 , based on identified communication packet flows. For example, the model can be trained to detect the respective fingerprint data with a defined level of confidence. Further, at  2006 , a pattern of use of the received communication packet flow can be identified. The identification can be based on detection of fingerprint data of the received communication packet flow based on the defined level of confidence. 
     At  2008 , conflicting uniform resource locators in the received communication packet flow can be identified. In an example, identifying the conflicting uniform resource locators can comprise analyzing the fingerprint data for respective domain name system traffic related to the conflicting uniform resource locators. In an example, the respective fingerprint data can comprise respective domain name system signatures that exhibit properties defined to be stable properties. Further, at  2010 , an unauthorized tethering of a communication device can be determined based on the conflicting uniform resource locators. 
     According to some implementations, the model can be retrained based on newly received data. For example, after the pattern of use is identified (e.g., at  1904  of  FIG.  19   , at  1906  of  FIG.  20   ), an accuracy of the pattern of use can be determined. The model can be retrained based on the identified communication packet flows and the received communication packet flow as a function of the accuracy of the pattern of use and at least the defined level of confidence. 
     The term “mobile device” can be interchangeable with (or include) a User Equipment (UE) or other terminology. Mobile device (or UE) refers to any type of wireless device that communicates with a radio network node in a cellular or mobile communication system. Examples of UEs include, but are not limited to, a target device, a Device to Device (D2D) UE, a machine type UE or a UE capable of Machine to Machine (M2M) communication, a Personal Digital Assistant (PDA), a tablet, a mobile terminal, a smart phone, a Laptop Embedded Equipment (LEE), a Laptop Mounted Equipment (LME), a Universal Serial Bus (USB) dongle, and so on. 
     As used herein, the term “network device” can be interchangeable with (or include) a network, a network controller or any number of other network components. Further, as utilized herein, the non-limiting term radio network node, or simply network node (e.g., network device, network node device) is used herein to refer to any type of network node serving communication devices and/or connected to other network nodes, network elements, or another network node from which the communication devices can receive a radio signal. In cellular radio access networks (e.g., Universal Mobile Telecommunications System (UMTS) networks), network devices can be referred to as Base Transceiver Stations (BTS), radio base station, radio network nodes, base stations, NodeB, eNodeB (e.g., evolved NodeB), and so on. In 5G terminology, the network nodes can be referred to as gNodeB (e.g., gNB) devices. Network devices can also comprise multiple antennas for performing various transmission operations (e.g., Multiple Input Multiple Output (MIMO) operations). A network node can comprise a cabinet and other protected enclosures, an antenna mast, and actual antennas. Network devices can serve several cells, also called sectors, depending on the configuration and type of antenna. Examples of network nodes or radio network nodes (e.g., the network device  102 ) can include but are not limited to: NodeB devices, Base Station (BS) devices, Access Point (AP) devices, TRPs, and Radio Access Network (RAN) devices. The network nodes can also include Multi-Standard Radio (MSR) radio node devices, comprising: an MSR BS, a gNodeB, an eNode B, a network controller, a Radio Network Controller (RNC), a Base Station Controller (BSC), a relay, a donor node controlling relay, a Base Transceiver Station (BTS), an Access Point (AP), a transmission point, a transmission node, a Remote Radio Unit (RRU), a Remote Radio Head (RRH), nodes in Distributed Antenna System (DAS), and the like. 
     Described herein are systems, methods, articles of manufacture, and other embodiments or implementations that can facilitate web page spectroscopy in a communication network. Facilitating web page spectroscopy can be implemented in connection with any type of device with a connection to the communication network (e.g., a mobile handset, a computer, a handheld device, etc.) any Internet of things (IoT) device (e.g., toaster, coffee maker, blinds, music players, speakers, etc.), and/or any connected vehicles (cars, airplanes, space rockets, and/or other at least partially automated vehicles (e.g., drones)). In some embodiments, the non-limiting term User Equipment (UE) is used. It can refer to any type of wireless device that communicates with a radio network node in a cellular or mobile communication system. Examples of UE are target device, device to device (D2D) UE, machine type UE or UE capable of machine to machine (M2M) communication, PDA, Tablet, mobile terminals, smart phone, Laptop Embedded Equipped (LEE), laptop mounted equipment (LME), USB dongles etc. Note that the terms element, elements and antenna ports can be interchangeably used but carry the same meaning in this disclosure. The embodiments are applicable to single carrier as well as to Multi-Carrier (MC) or Carrier Aggregation (CA) operation of the UE. The term Carrier Aggregation (CA) is also called (e.g., interchangeably called) “multi-carrier system,” “multi-cell operation,” “multi-carrier operation,” “multi-carrier” transmission and/or reception. 
     In some embodiments, the non-limiting term radio network node or simply network node is used. It can refer to any type of network node that serves one or more UEs and/or that is coupled to other network nodes or network elements or any radio node from where the one or more UEs receive a signal. Examples of radio network nodes are Node B, Base Station (BS), Multi-Standard Radio (MSR) node such as MSR BS, eNode B, network controller, Radio Network Controller (RNC), Base Station Controller (BSC), relay, donor node controlling relay, Base Transceiver Station (BTS), Access Point (AP), transmission points, transmission nodes, RRU, RRH, nodes in Distributed Antenna System (DAS) etc. 
     Cloud Radio Access Networks (RAN) can enable the implementation of concepts such as Software-Defined Network (SDN) and Network Function Virtualization (NFV) in 5G networks. This disclosure can facilitate a generic channel state information framework design for a 5G network. Certain embodiments of this disclosure can comprise an SDN controller that can control routing of traffic within the network and between the network and traffic destinations. The SDN controller can be merged with the 5G network architecture to enable service deliveries via open Application Programming Interfaces (APIs) and move the network core towards an all Internet Protocol (IP), cloud based, and software driven telecommunications network. The SDN controller can work with, or take the place of Policy and Charging Rules Function (PCRF) network elements so that policies such as quality of service and traffic management and routing can be synchronized and managed end to end. 
     To meet the huge demand for data centric applications, 4G standards can be applied to 5G, also called New Radio (NR) access. 5G networks can comprise the following: data rates of several tens of megabits per second supported for tens of thousands of users; 1 gigabit per second can be offered simultaneously (or concurrently) to tens of workers on the same office floor; several hundreds of thousands of simultaneous (or concurrent) connections can be supported for massive sensor deployments; spectral efficiency can be enhanced compared to 4G; improved coverage; enhanced signaling efficiency; and reduced latency compared to LTE. In multicarrier system such as OFDM, each subcarrier can occupy bandwidth (e.g., subcarrier spacing). If the carriers use the same bandwidth spacing, then it can be considered a single numerology. However, if the carriers occupy different bandwidth and/or spacing, then it can be considered a multiple numerology. 
     The various aspects described herein can relate to new radio, which can be deployed as a standalone radio access technology or as a non-standalone radio access technology assisted by another radio access technology, such as Long Term Evolution (LTE), for example. It should be noted that although various aspects and embodiments have been described herein in the context of 5G, Universal Mobile Telecommunications System (UMTS), and/or LTE, or other next generation networks, the disclosed aspects are not limited to 5G, a UMTS implementation, and/or an LTE implementation as the techniques can also be applied in 3G, 4G, or LTE systems. For example, aspects or features of the disclosed embodiments can be exploited in substantially any wireless communication technology. Such wireless communication technologies can include UMTS, Code Division Multiple Access (CDMA), Wi-Fi, Worldwide Interoperability for Microwave Access (WiMAX), General Packet Radio Service (GPRS), Enhanced GPRS, Third Generation Partnership Project (3GPP), LTE, Third Generation Partnership Project 2 (3GPP2) Ultra Mobile Broadband (UMB), High Speed Packet Access (HSPA), Evolved High Speed Packet Access (HSPA+), High-Speed Downlink Packet Access (HSDPA), High-Speed Uplink Packet Access (HSUPA), Zigbee, or another IEEE 802.XX technology. Additionally, substantially all aspects disclosed herein can be exploited in legacy telecommunication technologies. Further, the various aspects can be utilized with any Radio Access Technology (RAT) or multi-RAT system where the mobile device operates using multiple carriers (e.g., LTE Frequency Division Duplexing (FDD)/Time-Division Duplexing (TDD), Wideband Code Division Multiplexing Access (WCMDA)/HSPA, Global System for Mobile Communications (GSM)/GSM EDGE Radio Access Network (GERAN), Wi Fi, Wireless Local Area Network (WLAN), WiMax, CDMA2000, and so on). 
     As used herein, “5G” can also be referred to as New Radio (NR) access. Accordingly, systems, methods, and/or machine-readable storage media for facilitating improvements to the uplink performance for 5G systems are desired. As used herein, one or more aspects of a 5G network can comprise, but is not limited to, data rates of several tens of megabits per second (Mbps) supported for tens of thousands of users; at least one gigabit per second (Gbps) to be offered simultaneously to tens of users (e.g., tens of workers on the same office floor); several hundreds of thousands of simultaneous connections supported for massive sensor deployments; spectral efficiency significantly enhanced compared to 4G; improvement in coverage relative to 4G; signaling efficiency enhanced compared to 4G; and/or latency significantly reduced compared to LTE. 
     Referring now to  FIG.  21   , illustrated is an example block diagram of an example mobile handset  2100  operable to engage in a system architecture that facilitates wireless communications according to one or more embodiments described herein. Although a mobile handset is illustrated herein, it will be understood that other devices can be a mobile device, and that the mobile handset is merely illustrated to provide context for the embodiments of the various embodiments described herein. The following discussion is intended to provide a brief, general description of an example of a suitable environment in which the various embodiments can be implemented. While the description includes a general context of computer-executable instructions embodied on a machine-readable storage medium, those skilled in the art will recognize that the innovation also can be implemented in combination with other program modules and/or as a combination of hardware and software. 
     Generally, applications (e.g., program modules) can include routines, programs, components, data structures, etc., that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that the methods described herein can be practiced with other system configurations, including single-processor or multiprocessor systems, minicomputers, mainframe computers, as well as personal computers, hand-held computing devices, microprocessor-based or programmable consumer electronics, and the like, each of which can be operatively coupled to one or more associated devices. 
     A computing device can typically include a variety of machine-readable media. Machine-readable media can be any available media that can be accessed by the computer and includes both volatile and non-volatile media, removable and non-removable media. By way of example and not limitation, computer-readable media can comprise computer storage media and communication media. Computer storage media can include volatile and/or non-volatile media, removable and/or non-removable media implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules, or other data. Computer storage media can include, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, solid state drive (SSD) or other solid-state storage technology, Compact Disk Read Only Memory (CD ROM), digital video disk (DVD), Blu-ray disk, or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computer. In this regard, the terms “tangible” or “non-transitory” herein as applied to storage, memory or computer-readable media, are to be understood to exclude only propagating transitory signals per se as modifiers and do not relinquish rights to all standard storage, memory or computer-readable media that are not only propagating transitory signals per se. 
     Communication media typically embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of the any of the above should also be included within the scope of computer-readable media. 
     The handset includes a processor  2102  for controlling and processing all onboard operations and functions. A memory  2104  interfaces to the processor  2102  for storage of data and one or more applications  2106  (e.g., a video player software, user feedback component software, etc.). Other applications can include voice recognition of predetermined voice commands that facilitate initiation of the user feedback signals. The applications  2106  can be stored in the memory  2104  and/or in a firmware  2108 , and executed by the processor  2102  from either or both the memory  2104  or/and the firmware  2108 . The firmware  2108  can also store startup code for execution in initializing the handset  2100 . A communications component  2110  interfaces to the processor  2102  to facilitate wired/wireless communication with external systems, e.g., cellular networks, VoIP networks, and so on. Here, the communications component  2110  can also include a suitable cellular transceiver  2111  (e.g., a GSM transceiver) and/or an unlicensed transceiver  2113  (e.g., Wi-Fi, WiMax) for corresponding signal communications. The handset  2100  can be a device such as a cellular telephone, a PDA with mobile communications capabilities, and messaging-centric devices. The communications component  2110  also facilitates communications reception from terrestrial radio networks (e.g., broadcast), digital satellite radio networks, and Internet-based radio services networks. 
     The handset  2100  includes a display  2112  for displaying text, images, video, telephony functions (e.g., a Caller ID function), setup functions, and for user input. For example, the display  2112  can also be referred to as a “screen” that can accommodate the presentation of multimedia content (e.g., music metadata, messages, wallpaper, graphics, etc.). The display  2112  can also display videos and can facilitate the generation, editing and sharing of video quotes. A serial I/O interface  2114  is provided in communication with the processor  2102  to facilitate wired and/or wireless serial communications (e.g., USB, and/or IEEE 1394) through a hardwire connection, and other serial input devices (e.g., a keyboard, keypad, and mouse). This supports updating and troubleshooting the handset  2100 , for example. Audio capabilities are provided with an audio I/O component  2116 , which can include a speaker for the output of audio signals related to, for example, indication that the user pressed the proper key or key combination to initiate the user feedback signal. The audio I/O component  2116  also facilitates the input of audio signals through a microphone to record data and/or telephony voice data, and for inputting voice signals for telephone conversations. 
     The handset  2100  can include a slot interface  2118  for accommodating a SIC (Subscriber Identity Component) in the form factor of a card Subscriber Identity Module (SIM) or universal SIM  2120 , and interfacing the SIM card  2120  with the processor  2102 . However, it is to be appreciated that the SIM card  2120  can be manufactured into the handset  2100 , and updated by downloading data and software. 
     The handset  2100  can process IP data traffic through the communications component  2110  to accommodate IP traffic from an IP network such as, for example, the Internet, a corporate intranet, a home network, a person area network, etc., through an ISP or broadband cable provider. Thus, VoIP traffic can be utilized by the handset  2100  and IP-based multimedia content can be received in either an encoded or a decoded format. 
     A video processing component  2122  (e.g., a camera) can be provided for decoding encoded multimedia content. The video processing component  2122  can aid in facilitating the generation, editing, and sharing of video quotes. The handset  2100  also includes a power source  2124  in the form of batteries and/or an AC power subsystem, which power source  2124  can interface to an external power system or charging equipment (not shown) by a power I/O component  2126 . 
     The handset  2100  can also include a video component  2130  for processing video content received and, for recording and transmitting video content. For example, the video component  2130  can facilitate the generation, editing and sharing of video quotes. A location tracking component  2132  facilitates geographically locating the handset  2100 . As described hereinabove, this can occur when the user initiates the feedback signal automatically or manually. A user input component  2134  facilitates the user initiating the quality feedback signal. The user input component  2134  can also facilitate the generation, editing and sharing of video quotes. The user input component  2134  can include such conventional input device technologies such as a keypad, keyboard, mouse, stylus pen, and/or touch screen, for example. 
     Referring again to the applications  2106 , a hysteresis component  2136  facilitates the analysis and processing of hysteresis data, which is utilized to determine when to associate with the access point. A software trigger component  2138  can be provided that facilitates triggering of the hysteresis component  2136  when the Wi-Fi transceiver  2113  detects the beacon of the access point. A SIP client  2140  enables the handset  2100  to support SIP protocols and register the subscriber with the SIP registrar server. The applications  2106  can also include a client  2142  that provides at least the capability of discovery, play and store of multimedia content, for example, music. 
     The handset  2100 , as indicated above related to the communications component  2110 , includes an indoor network radio transceiver  2113  (e.g., Wi-Fi transceiver). This function supports the indoor radio link, such as IEEE 802.11, for the dual-mode GSM handset  2100 . The handset  2100  can accommodate at least satellite radio services through a handset that can combine wireless voice and digital radio chipsets into a single handheld device. 
     Referring now to  FIG.  22   , illustrated is an example block diagram of an example computer  2200  operable to engage in a system architecture that facilitates wireless communications according to one or more embodiments described herein. The computer  2200  can provide networking and communication capabilities between a wired or wireless communication network and a server (e.g., Microsoft server) and/or communication device. In order to provide additional context for various aspects thereof,  FIG.  22    and the following discussion are intended to provide a brief, general description of a suitable computing environment in which the various aspects of the innovation can be implemented to facilitate the establishment of a transaction between an entity and a third party. While the description above is in the general context of computer-executable instructions that can run on one or more computers, those skilled in the art will recognize that the innovation also can be implemented in combination with other program modules and/or as a combination of hardware and software. 
     Generally, program modules include routines, programs, components, data structures, etc., that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that the various methods can be practiced with other computer system configurations, including single-processor or multiprocessor computer systems, minicomputers, mainframe computers, as well as personal computers, hand-held computing devices, microprocessor-based or programmable consumer electronics, and the like, each of which can be operatively coupled to one or more associated devices. 
     The illustrated aspects of the innovation can also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote memory storage devices. 
     Computing devices typically include a variety of media, which can include computer-readable storage media or communications media, which two terms are used herein differently from one another as follows. 
     Computer-readable storage media can be any available storage media that can be accessed by the computer and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer-readable storage media can be implemented in connection with any method or technology for storage of information such as computer-readable instructions, program modules, structured data, or unstructured data. Computer-readable storage media can include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or other tangible and/or non-transitory media which can be used to store desired information. Computer-readable storage media can be accessed by one or more local or remote computing devices, e.g., via access requests, queries or other data retrieval protocols, for a variety of operations with respect to the information stored by the medium. 
     Communications media can embody computer-readable instructions, data structures, program modules, or other structured or unstructured data in a data signal such as a modulated data signal, e.g., a carrier wave or other transport mechanism, and includes any information delivery or transport media. The term “modulated data signal” or signals refers to a signal that has one or more of its characteristics set or changed in such a manner as to encode information in one or more signals. By way of example, and not limitation, communication media include wired media, such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. 
     With reference to  FIG.  22   , implementing various aspects described herein with regards to the end-user device can include a computer  2200 , the computer  2200  including a processing unit  2204 , a system memory  2206  and a system bus  2208 . The system bus  2208  couples system components including, but not limited to, the system memory  2206  to the processing unit  2204 . The processing unit  2204  can be any of various commercially available processors. Dual microprocessors and other multi processor architectures can also be employed as the processing unit  2204 . 
     The system bus  2208  can be any of several types of bus structure that can further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. The system memory  2206  includes read-only memory (ROM)  2227  and random access memory (RAM)  2212 . A basic input/output system (BIOS) is stored in a non-volatile memory  2227  such as ROM, EPROM, EEPROM, which BIOS contains the basic routines that help to transfer information between elements within the computer  2200 , such as during start-up. The RAM  2212  can also include a high-speed RAM such as static RAM for caching data. 
     The computer  2200  further includes an internal hard disk drive (HDD)  2214  (e.g., EIDE, SATA), which internal hard disk drive  2214  can also be configured for external use in a suitable chassis (not shown), a magnetic floppy disk drive (FDD)  2216 , (e.g., to read from or write to a removable diskette  2218 ) and an optical disk drive  2220 , (e.g., reading a CD-ROM disk  2222  or, to read from or write to other high capacity optical media such as the DVD). The hard disk drive  2214 , magnetic disk drive  2216  and optical disk drive  2220  can be connected to the system bus  2208  by a hard disk drive interface  2224 , a magnetic disk drive interface  2226  and an optical drive interface  2228 , respectively. The interface  2224  for external drive implementations includes at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies. Other external drive connection technologies are within contemplation of the subject innovation. 
     The drives and their associated computer-readable media provide nonvolatile storage of data, data structures, computer-executable instructions, and so forth. For the computer  2200  the drives and media accommodate the storage of any data in a suitable digital format. Although the description of computer-readable media above refers to a HDD, a removable magnetic diskette, and a removable optical media such as a CD or DVD, it should be appreciated by those skilled in the art that other types of media which are readable by a computer  2200 , such as zip drives, magnetic cassettes, flash memory cards, cartridges, and the like, can also be used in the exemplary operating environment, and further, that any such media can contain computer-executable instructions for performing the methods of the disclosed innovation. 
     A number of program modules can be stored in the drives and RAM  2212 , including an operating system  2230 , one or more application programs  2232 , other program modules  2234  and program data  2236 . All or portions of the operating system, applications, modules, and/or data can also be cached in the RAM  2212 . It is to be appreciated that the innovation can be implemented with various commercially available operating systems or combinations of operating systems. 
     A user can enter commands and information into the computer  2200  through one or more wired/wireless input devices, e.g., a keyboard  2238  and a pointing device, such as a mouse  2240 . Other input devices (not shown) can include a microphone, an IR remote control, a joystick, a game pad, a stylus pen, touch screen, or the like. These and other input devices are often connected to the processing unit  2204  through an input device interface  2242  that is coupled to the system bus  2208 , but can be connected by other interfaces, such as a parallel port, an IEEE 1394 serial port, a game port, a USB port, an IR interface, etc. 
     A monitor  2244  or other type of display device is also connected to the system bus  2208  through an interface, such as a video adapter  2246 . In addition to the monitor  2244 , a computer  2200  typically includes other peripheral output devices (not shown), such as speakers, printers, etc. 
     The computer  2200  can operate in a networked environment using logical connections by wired and/or wireless communications to one or more remote computers, such as a remote computer(s)  2248 . The remote computer(s)  2248  can be a workstation, a server computer, a router, a personal computer, portable computer, microprocessor-based entertainment device, a peer device or other common network node, and typically includes many or all of the elements described relative to the computer, although, for purposes of brevity, only a memory/storage device  2250  is illustrated. The logical connections depicted include wired/wireless connectivity to a local area network (LAN)  2252  and/or larger networks, e.g., a wide area network (WAN)  2254 . Such LAN and WAN networking environments are commonplace in offices and companies, and facilitate enterprise-wide computer networks, such as intranets, all of which can connect to a global communications network, e.g., the Internet. 
     When used in a LAN networking environment, the computer  2200  is connected to the local network  2252  through a wired and/or wireless communication network interface or adapter  2256 . The adapter  2256  can facilitate wired or wireless communication to the LAN  2252 , which can also include a wireless access point disposed thereon for communicating with the wireless adapter  2256 . 
     When used in a WAN networking environment, the computer  2200  can include a modem  2258 , or is connected to a communications server on the WAN  2254 , or has other means for establishing communications over the WAN  2254 , such as by way of the Internet. The modem  2258 , which can be internal or external and a wired or wireless device, is connected to the system bus  2208  through the input device interface  2242 . In a networked environment, program modules depicted relative to the computer, or portions thereof, can be stored in the remote memory/storage device  2250 . It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers can be used. 
     The computer is operable to communicate with any wireless devices or entities operatively disposed in wireless communication, e.g., a printer, scanner, desktop and/or portable computer, portable data assistant, communications satellite, any piece of equipment or location associated with a wirelessly detectable tag (e.g., a kiosk, news stand, restroom), and telephone. This includes at least Wi-Fi and Bluetooth™ wireless technologies. Thus, the communication can be a predefined structure as with a conventional network or simply an ad hoc communication between at least two devices. 
     Wi-Fi, or Wireless Fidelity, allows connection to the Internet from a couch at home, in a hotel room, or a conference room at work, without wires. Wi-Fi is a wireless technology similar to that used in a cell phone that enables such devices, e.g., computers, to send and receive data indoors and out; anywhere within the range of a base station. Wi-Fi networks use radio technologies called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, fast wireless connectivity. A Wi-Fi network can be used to connect computers to each other, to the Internet, and to wired networks (which use IEEE 802.3 or Ethernet). Wi-Fi networks operate in the unlicensed 2.4 and 5 GHz radio bands, at an 11 Mbps (802.11a) or 54 Mbps (802.11b) data rate, for example, or with products that contain both bands (dual band), so the networks can provide real-world performance similar to the basic 10BaseT wired Ethernet networks used in many offices. 
     An aspect of 5G, which differentiates from previous 4G systems, is the use of NR. NR architecture can be designed to support multiple deployment cases for independent configuration of resources used for RACH procedures. Since the NR can provide additional services than those provided by LTE, efficiencies can be generated by leveraging the pros and cons of LTE and NR to facilitate the interplay between LTE and NR, as discussed herein. 
     Reference throughout this specification to “one embodiment,” or “an embodiment,” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of the phrase “in one embodiment,” “in one aspect,” or “in an embodiment,” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. 
     As used in this disclosure, in some embodiments, the terms “component,” “system,” “interface,” and the like are intended to refer to, or comprise, a computer-related entity or an entity related to an operational apparatus with one or more specific functionalities, wherein the entity can be either hardware, a combination of hardware and software, software, or software in execution, and/or firmware. As an example, a component can be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, computer-executable instructions, a program, and/or a computer. By way of illustration and not limitation, both an application running on a server and the server can be a component. 
     One or more components can reside within a process and/or thread of execution and a component can be localized on one computer and/or distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures stored thereon. The components can communicate via local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the Internet with other systems via the signal). As another example, a component can be an apparatus with specific functionality provided by mechanical parts operated by electric or electronic circuitry, which is operated by a software application or firmware application executed by one or more processors, wherein the processor can be internal or external to the apparatus and can execute at least a part of the software or firmware application. As yet another example, a component can be an apparatus that provides specific functionality through electronic components without mechanical parts, the electronic components can comprise a processor therein to execute software or firmware that confer(s) at least in part the functionality of the electronic components. In an aspect, a component can emulate an electronic component via a virtual machine, e.g., within a cloud computing system. While various components have been illustrated as separate components, it will be appreciated that multiple components can be implemented as a single component, or a single component can be implemented as multiple components, without departing from example embodiments. 
     In addition, the words “example” and “exemplary” are used herein to mean serving as an instance or illustration. Any embodiment or design described herein as “example” or “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word example or exemplary is intended to present concepts in a concrete fashion. As used in this application, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless specified otherwise or clear from context, “X employs A or B” is intended to mean any of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, then “X employs A or B” is satisfied under any of the foregoing instances. In addition, the articles “a” and “an” as used in this application and the appended claims should generally be construed to mean “one or more” unless specified otherwise or clear from context to be directed to a singular form. 
     Moreover, terms such as “mobile device equipment,” “mobile station,” “mobile,” subscriber station,” “access terminal,” “terminal,” “handset,” “communication device,” “mobile device” (and/or terms representing similar terminology) can refer to a wireless device utilized by a subscriber or mobile device of a wireless communication service to receive or convey data, control, voice, video, sound, gaming or substantially any data-stream or signaling-stream. The foregoing terms are utilized interchangeably herein and with reference to the related drawings. Likewise, the terms “access point (AP),” “Base Station (BS),” BS transceiver, BS device, cell site, cell site device, “Node B (NB),” “evolved Node B (eNode B),” “home Node B (HNB)” and the like, are utilized interchangeably in the application, and refer to a wireless network component or appliance that transmits and/or receives data, control, voice, video, sound, gaming or substantially any data-stream or signaling-stream from one or more subscriber stations. Data and signaling streams can be packetized or frame-based flows. 
     Furthermore, the terms “device,” “communication device,” “mobile device,” “subscriber,” “customer entity,” “consumer,” “customer entity,” “entity” and the like are employed interchangeably throughout, unless context warrants particular distinctions among the terms. It should be appreciated that such terms can refer to human entities or automated components supported through artificial intelligence (e.g., a capacity to make inference based on complex mathematical formalisms), which can provide simulated vision, sound recognition and so forth. 
     Embodiments described herein can be exploited in substantially any wireless communication technology, comprising, but not limited to, wireless fidelity (Wi-Fi), global system for mobile communications (GSM), universal mobile telecommunications system (UMTS), worldwide interoperability for microwave access (WiMAX), enhanced general packet radio service (enhanced GPRS), third generation partnership project (3GPP) long term evolution (LTE), third generation partnership project 2 (3GPP2) ultra mobile broadband (UMB), high speed packet access (HSPA), Z-Wave, Zigbee and other 802.XX wireless technologies and/or legacy telecommunication technologies. 
     Systems, methods and/or machine-readable storage media for facilitating a two-stage downlink control channel for 5G systems are provided herein. Legacy wireless systems such as LTE, Long-Term Evolution Advanced (LTE-A), High Speed Packet Access (HSPA) etc. use fixed modulation format for downlink control channels. Fixed modulation format implies that the downlink control channel format is always encoded with a single type of modulation (e.g., quadrature phase shift keying (QPSK)) and has a fixed code rate. Moreover, the forward error correction (FEC) encoder uses a single, fixed mother code rate of 1/3 with rate matching. This design does not take into the account channel statistics. For example, if the channel from the BS device to the mobile device is very good, the control channel cannot use this information to adjust the modulation, code rate, thereby unnecessarily allocating power on the control channel. Similarly, if the channel from the BS to the mobile device is poor, then there is a probability that the mobile device might not be able to decode the information received with only the fixed modulation and code rate. As used herein, the term “infer” or “inference” refers generally to the process of reasoning about, or inferring states of, the system, environment, user, and/or intent from a set of observations as captured via events and/or data. Captured data and events can include user data, device data, environment data, data from sensors, sensor data, application data, implicit data, explicit data, etc. Inference can be employed to identify a specific context or action, or can generate a probability distribution over states of interest based on a consideration of data and events, for example. 
     Inference can also refer to techniques employed for composing higher-level events from a set of events and/or data. Such inference results in the construction of new events or actions from a set of observed events and/or stored event data, whether the events are correlated in close temporal proximity, and whether the events and data come from one or several event and data sources. Various classification schemes and/or systems (e.g., support vector machines, neural networks, expert systems, Bayesian belief networks, fuzzy logic, and data fusion engines) can be employed in connection with performing automatic and/or inferred action in connection with the disclosed subject matter. 
     In addition, the various embodiments can be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques to produce software, firmware, hardware, or any combination thereof to control a computer to implement the disclosed subject matter. The term “article of manufacture” as used herein is intended to encompass a computer program accessible from any computer-readable device, machine-readable device, computer-readable carrier, computer-readable media, machine-readable media, computer-readable (or machine-readable) storage/communication media. For example, computer-readable media can comprise, but are not limited to, a magnetic storage device, e.g., hard disk; floppy disk; magnetic strip(s); an optical disk (e.g., compact disk (CD), a digital video disc (DVD), a Blu-ray Disc™ (BD)); a smart card; a flash memory device (e.g., card, stick, key drive); and/or a virtual device that emulates a storage device and/or any of the above computer-readable media. Of course, those skilled in the art will recognize many modifications can be made to this configuration without departing from the scope or spirit of the various embodiments 
     The above description of illustrated embodiments of the subject disclosure, including what is described in the Abstract, is not intended to be exhaustive or to limit the disclosed embodiments to the precise forms disclosed. While specific embodiments and examples are described herein for illustrative purposes, various modifications are possible that are considered within the scope of such embodiments and examples, as those skilled in the relevant art can recognize. 
     In this regard, while the subject matter has been described herein in connection with various embodiments and corresponding FIGS., where applicable, it is to be understood that other similar embodiments can be used or modifications and additions can be made to the described embodiments for performing the same, similar, alternative, or substitute function of the disclosed subject matter without deviating therefrom. Therefore, the disclosed subject matter should not be limited to any single embodiment described herein, but rather should be construed in breadth and scope in accordance with the appended claims below.