DATA-DRIVEN ARTIFICIAL INTELLIGENCE (AI) FOR COMMUNICATION NETWORKS

Aspects of the subject disclosure may include, for example, obtaining first data from a first component of a disaggregated wireless communication network; obtaining second data from a second component of the disaggregated wireless communication network; formatting the first data and the second data for use in a generative artificial intelligence (AI) process, wherein the formatting results in formatted data; applying the formatted data to the generative AI process, wherein the generative AI process results in one or more first commands for the first component of the disaggregated wireless communication network; and transmitting the one or more first commands to the first component of the disaggregated wireless communication network. Other embodiments are disclosed.

FIELD OF THE DISCLOSURE

The subject disclosure relates to data-driven artificial intelligence (AI) for communication networks.

BACKGROUND

With the emergence of fifth generation (5G) Open Radio Access Network (O-RAN) and soon-to-be sixth generation (6G) O-RAN, there is a great deal of network-related data that is/will be available. However, it is traditionally difficult (if not impossible) for network operators to utilize all of such data in a manner that monetizes the data. For example, in an O-RAN environment, a given network solution provider (e.g., hardware provider) does not typically have the capability to offer a network operator (e.g., wireless carrier) the ability to make sense out of disaggregated device, network, and core data from different solution providers. In another example, data collection capabilities are typically external to the RAN and core networks (in which case carriers can buy AI systems, but such AI systems would reside outside of the carrier network and data collection from multiple sources would be difficult (if not impossible)).

In addition, certain conventional service management and orchestration (SMO) functions and/or intelligent RAN interface controllers (RICs) have been available in order to facilitate data extraction and/or to manage components from different vendors/manufacturers.

DETAILED DESCRIPTION

The subject disclosure describes, among other things, illustrative embodiments for data-driven artificial intelligence (AI) for communication networks. In various embodiments, a communication network can comprise a wireless communication network, the communication network can comprise a disaggregated network, and/or the communication network can comprise an Open Radio Access Network (O-RAN) architecture. Other embodiments are described in the subject disclosure.

As described herein, various embodiments provide mechanisms that enable a network operator (e.g., wireless carrier) the ability to make sense out of disaggregated device, network, and core data from different solution providers. In various examples, the mechanisms can enable the making of dynamic network decisions.

One or more aspects of the subject disclosure include a device, comprising: a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising: obtaining first data from a first component of a disaggregated wireless communication network; obtaining second data from a second component of the disaggregated wireless communication network; formatting the first data and the second data for use in a generative artificial intelligence (AI) process, wherein the formatting results in formatted data; applying the formatted data to the generative AI process, wherein the generative AI process results in one or more first commands for the first component of the disaggregated wireless communication network; and transmitting the one or more first commands to the first component of the disaggregated wireless communication network.

One or more aspects of the subject disclosure include a non-transitory machine-readable medium comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, the operations comprising: obtaining data associated with a plurality of components of a disaggregated wireless communication network; inputting the data to a generative artificial intelligence (AI) process; responsive to the inputting of the data, producing via the generative AI process a first command for a first component of the plurality of components and a second command for a second component of the plurality of components; transmitting the first command to the first component; and transmitting the second command to the second component.

One or more aspects of the subject disclosure include a method, comprising: obtaining, by a processing system including a processor, training data associated with a plurality of components of a disaggregated wireless cellular network, wherein the training data comprises for each of the plurality of components one or more respective historical operating parameters; inputting, by the processing system, the training data to an artificial intelligence (AI) model generation engine, wherein the AI model generation engine produces an AI model; obtaining, by the processing system, operational data associated with the plurality of components, wherein the operational data comprises for each of the plurality of components one or more respective operating parameters; inputting, by the processing system, the operational data to a generative AI process, wherein the generative AI process utilizes the operational data and the AI model to produce a first command for a first component of the plurality of components and a second command for a second component of the plurality of components; transmitting the first command to the first component; and transmitting the second command to the second component.

Referring now toFIG.2A, this is a block diagram illustrating an example, non-limiting embodiment of a system200(in accordance with various aspects described herein. As seen in this figure, a 5G/6G application ecosystem includes a plurality of UE/CPE devices202(in various examples, the UE (user equipment) and CPE (customer premises equipment) can comprise smartphones, vehicles, drones, medical monitoring devices and/or robots). The UE/CPE devices202are configured for bi-directional wireless communication with a disaggregated network (including Disaggregated RAN and Disaggregated Core). More particularly, the UE/CPE devices202communicate with RU204(which can include macro radio and/or small cell antennas); RU204communicates with DU206(which can include 5G/6G base stations); and DU206communicates with Edge Cloud208(which can include a CU-CP-L, a CU-UP, and a UPF). Further, Edge Cloud208communicates with Centralized Cloud210(which can include a UPF); and Centralized Cloud210communicates with Internet212. In this example, the Disaggregated Core214further includes a UDM, an ASUF, a PCF, an SMF, and an AMF as shown. In one specific example, the Disaggregated Core214can be a 3GPP 5GC compliant Disaggregated Core. Further still, Server(s)216are configured for bi-directional communication with the various network elements as shown. The Server(s)216include functionality for 5G/6G Data Extraction, Organic AI Intelligence Capabilities, and Analysis/Management/Control as described in more detail below. In various examples, Server(s)216are part of the Disaggregated RAN, Server(s)216are part of the Disaggregated Core, or Server(s)216are part of the Disaggregated RAN and the Disaggregated Core.

Still referring toFIG.2A, reference will now be made to the 5G/6G Data Extraction mechanism of Server(s)216(this 5G/6G networks data extraction can, for example, enhance and/or replace one or more RAN Interface Controllers (RICs). This 5G/6G Data Extraction mechanism performs operations via which data is ingested, extracted, transformed, and loaded (e.g., into one or more databases). This 5G/6G Data Extraction mechanism can be part of the 5G/6G core (and reside within the carrier network). This data extraction domain can enable an entity (e.g., a carrier) to extract data from multiple sources. In one example, the data can be collected as it is ingested in the network. The data extraction can be based upon interfacing with the RU(s), DU(s), and CU(s) from multiple different vendors/manufacturers. In one example of this disaggregated network, at least one RU, DU, or CU is from a first vendor and at least one RU, DU, or CU is from a second (different) vendor. In one example of this disaggregated network, at least one RU, DU, or CU is from a first manufacturer and at least one RU, DU, or CU is from a second (different) manufacturer. The data that is obtained by the Server(s)216can be Extracted, Transformed and Loaded (ETL) into an AI repository. The network data (e.g., the RAN network data) can be collected dynamically by the Data Extraction modules. This system can be configured to know (and/or determine) the type of data ingested. AI techniques can be used to learn from the data ingested and to transform this data into a format that makes sense for the AI analytics capabilities.

Still referring toFIG.2A, reference will now be made to the Organic AI Intelligence mechanism of Server(s)216(these organic Artificial Intelligence (AI) capabilities can be used to analyze and/or manage network data, and the AI Model(s) can be refined as more data is collected). These organic Artificial Intelligence (AI) capabilities can perform operations on the data that had undergone Extraction, Transformation and Loading. These organic Artificial Intelligence (AI) capabilities can be part of the 5G/6G core (and reside within the carrier network). These organic Artificial Intelligence (AI) capabilities can facilitate one or more of the following functions: (a) The extracted network data can be loaded into an internal AI repository (this AI repository can be considered part of the 5G/6G Core and can be internal to the carrier network and 5G/6G Core—not external); (b) The AI repository can be used to store data and to enable AI programs to derive knowledge and provide insights; (c) The AI repository can have a Unified infrastructure supporting multiple programming languages (this can enable data scientists, data engineers, and data analysts to leverage the maximum potential for the data captured); (d) The AI repository can enable data scientists, data engineers, and data analysts to manage and deploy ML features at scale by delivering reproducibility, discoverability, and scalability; and/or (e) The internal AI repository can provide potential access to web app development empowering network data scientists, data engineers, and data analysts to make their models more comprehensible and actionable.

Still referring toFIG.2A, reference will now be made to the Analysis/Management/Control mechanism of Server(s)216. In one example, this Analysis/Management/Control can be implemented via a 5G/6G Data-Driven AI Insights Engine. This 5G/6G Data-Driven AI Insights Engine can facilitate analysis, deep understanding, metadata management, insightful monitoring and reporting, and generative AI (e.g., generation of 5G/6G core/RAN commands). This 5G/6G Data-Driven AI Insights Engine can be part of the 5G/6G core (and reside within the carrier network). This 5G/6G Data-Driven AI Insights Engine can use the data captured from the network and can facilitate one or more of the following functions: (a) Identify hidden patterns, provide personalized services, learn from data and make predictions; (b) Interface with other 5G/6G Virtual Network Functions (or VNFs) to seamlessly provide information and capture additional data; and/or (c) Analyze complex network scenarios and provide dynamic results (delivering unprecedented value to network planners and operators).

Referring now toFIG.2B, this is a block diagram illustrating an example, non-limiting embodiment of a system240in accordance with various aspects described herein. ThisFIG.2Brelates to an Open RAN architecture including Centralized Unit (CU)242, Distributed Unit (DU)244, Radio Unit (RU)246, and Antenna248. The Open RAN architecture shown here is in the form of a disaggregated network in which the CU, DU, and RU are from different vendors/manufacturers (this Open RAN example uses off-the-shelf hardware, open interfaces between the CU/DU and between the DU/RU, proprietary software, and is multi-vendor). In one example of this disaggregated network, at least one RU, DU, or CU is from a first vendor and at least one RU, DU, or CU is from a second (different) vendor. In one example of this disaggregated network, at least one RU, DU, or CU is from a first manufacturer and at least one RU, DU, or CU is from a second (different) manufacturer. ThisFIG.2Balso shows Server(s)250. The Server(s)250include hardware, firmware, and/or software that is configured to implement the various data collection, data analysis, and component control functions described herein.

Referring now toFIG.2C, various steps of a method2000according to an embodiment are shown. As seen in thisFIG.2C, step2002comprises obtaining first data from a first component of a disaggregated wireless communication network. Next, step2004comprises obtaining second data from a second component of the disaggregated wireless communication network. Next, step2006comprises formatting the first data and the second data for use in a generative artificial intelligence (AI) process, wherein the formatting results in formatted data. Next, step2008comprises applying the formatted data to the generative AI process, wherein the generative AI process results in one or more first commands for the first component of the disaggregated wireless communication network. Next, step2010comprises transmitting the one or more first commands to the first component of the disaggregated wireless communication network.

Referring now toFIG.2D, various steps of a method2100according to an embodiment are shown. As seen in thisFIG.2D, step2102comprises obtaining data associated with a plurality of components of a disaggregated wireless communication network. Next, step2104comprises inputting the data to a generative artificial intelligence (AI) process. Next, step2106comprises responsive to the inputting of the data, producing via the generative AI process a first command for a first component of the plurality of components and a second command for a second component of the plurality of components. Next, step2108comprises transmitting the first command to the first component. Next, step2110comprises transmitting the second command to the second component.

Referring now toFIG.2E, various steps of a method2200according to an embodiment are shown. As seen in thisFIG.2E, step2202comprises obtaining, by a processing system including a processor, training data associated with a plurality of components of a disaggregated wireless cellular network, wherein the training data comprises for each of the plurality of components one or more respective historical operating parameters. Next, step2204comprises inputting, by the processing system, the training data to an artificial intelligence (AI) model generation engine, wherein the AI model generation engine produces an AI model. Next, step2206comprises obtaining, by the processing system, operational data associated with the plurality of components, wherein the operational data comprises for each of the plurality of components one or more respective operating parameters. Next, step2208comprises inputting, by the processing system, the operational data to a generative AI process, wherein the generative AI process utilizes the operational data and the AI model to produce a first command for a first component of the plurality of components and a second command for a second component of the plurality of components. Next, step2210comprises transmitting the first command to the first component. Next, step2212comprises transmitting the second command to the second component.

Reference will now be made to a number of example use cases according to various embodiments:Autonomous Vehicles (AV's) and Drones: The network elements/methods described herein can be used to track, manage, and predict the routes of AV's and/or Drones (e.g., in a private network, in an O-RAN network, and/or in a combination of carrier, hybrid, and private network). The mechanisms described herein can store data, track movement, predict locations, and provide insights that can be used by owners and/or enterprise fleet operators of the AV's/Drones.Insurance Companies: The data obtained from the network elements/methods described herein can be used by insurance companies in order to track endpoints and obtain network insights. The information thus derived (and associated business insights) can be used to adjust rates and monetize user behavior.Generative AI (Network commands, Cloud interfaces): The network elements/methods described herein can be used to generate network commands/cloud interfaces that would facilitate service management and orchestration offers.Security Attacks: Another use case can be to use the O-RAN network data (particularly coming from the DUs into the CUS), to determine whether malicious actor(s) are attempting to enter the carrier network. In various examples, the data can be used to detect AMF manipulation via SCTP attacks and/or DDOS attacks). The AI network elements/methods described herein would be able to determine behavior characteristics and anomalies.

As described herein, various embodiments provide mechanisms (e.g., network elements and/or associated methods) that enable enterprises (e.g., carriers) to: (1) implement the native ability in 5G/6G networks to extract, transform, and load network data so it can be captured and operationalized; (2) provide native artificial intelligence (AI) capabilities to analyze network data, learn as data is collected over time, and provide network insights; and/or (3) provide data insight (e.g., via one or more applications) to support emerging applications such as Extended Reality (XR), Autonomous Vehicles (AV) and/or Advanced Network Management capabilities.

As described herein, various embodiments provide for internal AI and/or organic AI services that reside within the 5G/6G architecture (in contrast, for example, with certain conventional 5G AI services that are external to the RAN and 5G core).

As described herein, various embodiments provide mechanisms to organically provide data collection/analysis capabilities in an O-RAN environment (e.g., where operators and private enterprises will have the ability, for example, to implement Radio Access Networks from one vendor/manufacturer and Core networks from another (different) vendor/manufacturer).

As described herein, various embodiments provide mechanisms applicable to O-RAN environments wherein organic AI data-driven capabilities will add significant value (e.g., in the context of monetizing services).

As described herein, various embodiments provide mechanisms via which RAN network data is Extracted, Transformed and Loaded. This can enable data scientists, data engineers, and data analysts to manage and deploy ML features at scale by delivering reproducibility, discoverability, and scalability.

As described herein, various embodiments enable intelligent systems that learn and provide network insights without human intervention.

As described herein, various embodiments provide AI mechanisms to track and/or control what type of devices are connected to a disaggregated network, what frequencies are being used, which subscribers are allowed to connect, and/or where the subscribers are moving. In one specific example, an AI mechanism can track/control wireless communication usage by multiple users using multiple network radios at a university campus or the like. In another specific example, only engineering students may be provided wireless access within an engineering building at a particular time (e.g., while school is in session). In another specific example, tracking/control can be dynamic and can be based upon location, time of day, day of week, month, etc.

As described herein, various embodiments provide AI mechanisms that operate on the cloud, receive data from the cloud, and/or send data to the cloud.

As described herein, various embodiments provide AI mechanisms to dynamically reconfigure a network without human interaction based upon patterns of recognition (e.g., learning patterns of recognition, applying statistics and/or new rules, and then implementing some dynamic rule changes). In one specific example, AI-based decisions can be made as to who can access a network (e.g., under what conditions).

As described herein, various embodiments provide AI mechanisms to generate new commands, configurations, security, connectivity, and/or alerts based on the information that is gathered. In various examples, there can be many thousands of commands being generated in real-time essentially simultaneously (or near-simultaneously).

As described herein, various embodiments provide AI mechanisms that can operate in the context of software defined radio.

As described herein, various embodiments can gather data via software routines that are inside of the network elements and/or via hardware devices that reside adjacent network elements (and that can detect the traffic that goes by). In one specific example, a first such hardware device can sit between the RU and the DU, a second such hardware device can sit between the DU and the CU, and a third such hardware device can sit between the CU and the Core.

As described herein, various embodiments provide a repository (e.g., AI data repository) that comprises one or more databases (e.g., one or more SQL databases).

As described herein, various embodiments provide AI decision-making based upon patterns of behavior, preexisting rules, and/or statistics (e.g., wherein rules can be applied to patterns that are detected and compared with other information). In one specific example, if the AI engine sees a first rule, do this and then do that. In one specific example, the AI engine can generate commands for provisioning/updating network elements.

As described herein, various embodiments can send/receive data and send/receive commands in the context of thousands of AV's/drones (e.g., a swarm). In one example, radio access rules for given AV's/drones can vary depending upon the AV/drone location. In one specific example, if a car has been at a given location previously then radio access could be granted; if the car has not previously been at that location, then no radio access will be granted. In one example, a car can be shut down (e.g., in a case of suspected car theft based upon being at an unexpected location at an unexpected time). In one example, a message can be sent to a user to determine whether a car is in a location known by the user or not (e.g., whether the car was stolen).

As described herein, various embodiments can facilitate sharing of information (e.g., between carriers, via a national database). In one example, based upon a determination that a type of malicious attack is going on, commands can be generated to alter and/or shut down parts of the network.

Referring now toFIG.3, a block diagram300is shown illustrating an example, non-limiting embodiment of a virtualized communication network in accordance with various aspects described herein. In particular a virtualized communication network is presented that can be used to implement some or all of the subsystems and functions of system100, some or all of the subsystems and functions of system200, some or all of the subsystems and functions of system240, some or all of the subsystems and functions of system280, and/or some or all of the functions of methods2000,2100,2200. For example, virtualized communication network300can facilitate in whole or in part data-driven AI for communication networks (e.g., monitoring and controlling various components of a disaggregated communication network via use of AI analysis and generative processing).

Turning now toFIG.4, there is illustrated a block diagram of a computing environment in accordance with various aspects described herein. In order to provide additional context for various embodiments of the embodiments described herein,FIG.4and the following discussion are intended to provide a brief, general description of a suitable computing environment400in which the various embodiments of the subject disclosure can be implemented. In particular, computing environment400can be used in the implementation of network elements150,152,154,156, access terminal112, base station or access point122, switching device132, media terminal142, and/or VNEs330,332,334, etc. Each of these devices can be implemented via computer-executable instructions that can run on one or more computers, and/or in combination with other program modules and/or as a combination of hardware and software. For example, computing environment400can facilitate in whole or in part data-driven AI for communication networks (e.g., monitoring and controlling various components of a disaggregated communication network via use of AI analysis and generative processing).

Turning now toFIG.6, an illustrative embodiment of a communication device600is shown. The communication device600can serve as an illustrative embodiment of devices such as data terminals114, mobile devices124, vehicle126, display devices144or other client devices for communication via either communications network125. For example, computing device600can facilitate in whole or in part data-driven AI for communication networks (e.g., monitoring and controlling various components of a disaggregated communication network via use of AI analysis and generative processing).

As described herein, various embodiments can employ artificial intelligence (AI) to facilitate automating one or more features described herein. The embodiments (e.g., in connection with automatically monitoring and controlling various components of a disaggregated communication network via use of AI analysis and generative processing) can employ various AI-based schemes for carrying out various embodiments thereof. Moreover, the classifier can be employed to determine a ranking or priority of each piece of user equipment, each subscriber, each user, each communication network component, and/or each communication channel. 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, that is, 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 determine or infer an action that a user desires to be automatically performed. 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 the hypersurface attempts to split the triggering criteria from the non-triggering events. Intuitively, this makes the classification correct for testing data that is near, but not identical to training data. Other directed and undirected model classification approaches comprise, 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 also is inclusive of statistical regression that is utilized to develop models of priority.

As will be readily appreciated, one or more of the embodiments can employ classifiers that are explicitly trained (e.g., via a generic training data) as well as implicitly trained (e.g., via observing UE behavior, operator preferences, historical information, receiving extrinsic information). For example, SVMs can be configured via a learning or training phase within a classifier constructor and feature selection module. Thus, the classifier(s) can be used to automatically learn and perform a number of functions, including but not limited to determining according to predetermined criteria which of the piece(s) of user equipment, subscriber(s), user(s), communication network component(s), and/or communication channel(s) is to receive priority.