Actively probing a time-sensitive network (TSN)-based 5g system

Abstract

Systems, methods, techniques, and other embodiments are disclosed regarding a communication network, e.g., a wireless network. A system associated with a communication network configured to support time-sensitive deterministic communication, comprises: a processor configured to: receive configuration data of the communication network and an input query for retrieving status information related to data communication over the communication network, the communication network including a plurality of communication network (CN) components, each CN component configured to provide a discrete functionality for the data communication from a source device across the communication network to a destination device, input the configuration data and the input query to an Artificial Intelligence (Al) model to generate an intermediate query, obtain one or more query responses from the communication network based on the intermediate query, and optimize the intermediate query by removing redundant data based on the one or more query responses to output an optimized query.

Description

ACTIVELY PROBING A TIME-SENSITIVE NETWORK (TSN)-BASED 5G SYSTEMCROSS-REFERENCE TO RELATED APPLICATION

[0001] The present application claims priority to U.S. Provisional Application No. 63 / 609,514, filed on December 13, 2023, which is incorporated herein as its entirety.TECHNICAL FIELD

[0002] The present description relates generally to the integration of a communication network and a time-sensitive network (TSN) and, more particularly, for example, a network diagnostic and monitoring technique, by applying an artificial intelligence (Al) model.BACKGROUND

[0003] Some applications such as industrial automation and manufacturing require ubiquitous and seamless connectivity with strict, deterministic timing requirements for communications between various devices or components (e.g., an industrial controller, a sensor, an actuator, etc.) of the application. To meet such requirements, a TSN system, which provides deterministic communication with relatively stringent quality of service (QoS) parameters, such as latency, jitter and reliability requirements for data traffic, may be integrated with a communication network in accordance with a 3GPP-defmed specification (e.g., 5G, 6G), which provides a high reliability service, such as an ultra-reliable low latency communication (URLLC) service.

[0004] Conventional network diagnostics and fault detection applied in wireless networks such as 5G passively gather large amounts of data about the 5G system. Dedicated analytics and monitoring tools or platforms collect data from various network devices and controllers in the 5G system (e.g., by sending queries or probes to the 5G system), analyze the collected data, and generate insights of performance of the network. Such techniques, however, may be inefficient and may introduce overhead traffic into the system. Furthermore, the probes and queries arepredefined and are not adapted on the fly or in real time based on the data that is collected, thereby consuming unnecessary bandwidth by collecting redundant information.BRIEF DESCRIPTION OF THE DRAWINGS

[0005] Certain features of the subject technology are set forth in the appended claims.However, for purpose of explanation, several aspects of the subject technology are set forth in the following figures.

[0006] Fig. 1 illustrates an example of a conventional integrated TSN-5G system in accordance with one or more implementations.

[0007] Fig. 2 illustrates a block diagram of an example integrated TSN-5G system architecture in accordance with one or more implementations.

[0008] Fig. 3 illustrates an example of an integrated TSN-5G system in accordance with one or more implementations.

[0009] Fig. 4 illustrates a block diagram of an example TSN block in accordance with one or more implementations.

[0010] Fig. 5 illustrates an example set of parameters for an example TSN block in accordance with one or more implementations.

[0011] Figs. 6A, 6B illustrate a block diagram of an example integrated TSN-5G system architecture including ME in accordance with one or more implementations.

[0012] Fig. 7 illustrates an electronic system with which one or more implementations of the subject technology may be implemented.

[0013] Fig. 8 illustrates a block diagram of an example integrated TSN-5G system architecture in accordance with one or more implementations.

[0014] Fig. 9 illustrates a block diagram of an example system for active probing of a wireless communication system, in accordance with one or more implementations.

[0015] Fig. 10 illustrates an example of operations of an AI / ML-based active probing of a wireless communication system, in accordance with one or more implementations.

[0016] Fig. 11 illustrates a “term-document matrix” used to represent queries and obtained responses, in accordance with one or more implementations.

[0017] Fig. 12 illustrates a flowchart showing a method for probing a wireless communication system using an AI / ML model, in accordance with one or more implementations.

[0018] Fig. 13 illustrates a flow diagram of an example method for probing a wireless communication system using an AI / ML model, in accordance with one or more implementations.DETAILED DESCRIPTION

[0019] The detailed description set forth below is intended as a description of various configurations of the subject technology and is not intended to represent the only configurations in which the subject technology can be practiced. The appended drawings are incorporated herein and constitute a part of the detailed description. The detailed description includes specific details for the purpose of providing a thorough understanding of the subject technology.However, the subject technology is not limited to the specific details set forth herein and can be practiced using one or more other implementations. In one or more implementations, structures and components are shown in block diagram form in order to avoid obscuring the concepts of the subject technology.

[0020] As noted above, for some applications such as, but not limited to, industrial automation, manufacturing, and aerospace and automotive in-vehicle communications, a TSN system, which provides deterministic communication may be integrated with a fifth-generation (5G) or sixth-generation (6G) wireless communication system (or any communication network or system defined per a 3GPP standard or IEEE 802.1 standard). However, typically, in such an integrated TSN-5G system, the entire 5G system is configured to operate as one single TSN component (e.g., a TSN bridge), and the integrated system is set up as a fully centralized configuration model, e.g., that uses one centralized TSN configuration controller. Accordingly, such an integrated system may not support flexibility of deploying a 5G system that includesvarious components provided by different vendors or for a decentralized TSN configuration of the integrated system. Further, a 5G system may support reliable communication by using redundant paths for data transmission. However, with the 5G system being set up as one TSN component in a typical TSN-5G integrated system, the redundant paths are configured across the entire 5G system through all its components (e.g., from user equipment (UE) to user plane function (UPF)). This does not allow for flexibility of setting up redundant data transmission paths for only a portion of the 5G system, e.g., the 5G system portion (such as an air interface between UE and radio access network (RAN)ZgNodeB) that is more susceptible to data delay and / or errors.

[0021] To address the above-discussed issues in an integrated TSN-5G system, the subject technology provides for a novel architecture in which, instead of being configured as a single TSN component or block, the 5G system is configured as a set of discrete 5G components where each 5G component is configured as one discrete TSN block. In other words, the disclosure provides for an architecture in which the 5G system in an integrated TSN-5G system is partitioned into a plurality of TSN blocks, where each TSN block is configured in accordance with TSN specifications (e.g., per IEEE 802. IQ and related standards), e.g., as a TSN bridge, TSN end device, or a combination of the two. Further, instead of having a centralized configuration controller to control the TSN-5G system, the subject technology provides for a plurality of distributed configuration modules in the TSN-5G system. The configuration modules may be interconnected in one or more topologies (including mesh, star, tree, or random) and each configuration module may be responsible to communicate with, and configure, one or more TSN blocks.

[0022] As is discussed in detail below, each TSN block of the plurality of TSN blocks of the 5G system includes a set of parameters describing the capabilities to support and execute a data flow (e.g., carrying URELC data traffic) through that TSN block. Further, each TSN block may include at least one configuration interface configured to support interaction of the TSN block with its respective configuration module. The configuration interface may be used to provide the set of parameters of the TSN block to the respective configuration module, and receive from the configuration module configuration data (e.g., transmission schedule, data flow identities, policing rules, etc.) to support one or more data flows through the TSN block. Each TSN blockmay be configured to execute or operate according to the configuration data (received via the configuration interface) and transmit data for each data flow according to the specifications provided in the configuration data. Lastly, each TSN block may also include a monitoring and diagnostics module configured to monitor run-time behavior of the TSN block and report the behavior either through the configuration interface or via a separate interface of the TSN block.

[0023] Each configuration module of the plurality of distributed configuration modules may be either an external utility responsible for configuring one or more TSN blocks or may be configured as a software module within the TSN block that only configures that TSN block. Each configuration module may be configured to determine and provide to TSN blocks controlled by the configuration module configuration data including a TSN schedule for one or more data flows to be transmitted through the TSN blocks. The configuration modules may exchange information with each other using a standardized Application Programming Interface (API). The exchanged information may include information regarding cycle times for the TSN system (e.g., supported admin cycle times (ACTs) including discrete levels of ACT buckets each corresponding to a specific data flow, max / min cycle times), configuration data including transmission schedule (include time offsets / duration / resources for transmission) for one or more TSN blocks, and information to request or respond to requests for resource allocations. In some implementations, one or more of the plurality of configuration modules may be adapted as a “controller” or “controller module,” which may include a component configured or adapted to provide instruction, control, operation, or any form of communication for operable components to affect the operation thereof. A controller module may include any known processor, microcontroller, or logic device, including, but not limited to, field programmable gate arrays (FPGA), an application-specific integrated circuit (ASIC), a full-authority digital engine control (FADEC), an aviation system, a proportional controller (P), a proportional integral controller (PI), a proportional derivative controller (PD), a proportional integral derivative controller (PID controller), a hardware-accelerated logic controller (e.g. for encoding, decoding, transcoding, etc.), the like, or a combination thereof.

[0024] 3GPP Release 8 classifies self-organizing networks (SON) into three main categories: self-configuration, self-optimization, and self-healing. Self-organization is regarded as a mechanism or a process that enables a system or network to change its organization withoutexplicit commands during its execution time. Self-configuration is defined as a process of incorporating a new Network Element (NE) into a service requiring minimal human operator intervention where a network element is a manageable logical entity uniting one or more physical devices. In some implementations, a TSN block (discussed below) may be selfconfiguring and incorporated into a 5G system as an NE with minimum human intervention. This can be accomplished by TSN applications that automatically share their TSN flow (stream) characteristics and latency requirements with the TSN block. The TSN blocks may collaborate to share the flow characteristics and determine feasible TSN schedules.

[0025] In some implementations, the present disclosure provides a communication network, e.g., a wireless network such as a 5G or 6G wireless communication system or network (or any communication network or system defined per a 3GPP standard or IEEE 802.1 standard) that is configured to support time-sensitive deterministic communication based on a TSN mechanism. In the context of the present disclosure, any reference to a “communication network,” “wireless network,” and “communication / wireless network” denotes a network of various components designed, arranged and / or configured for communication of data, signals, or information from a source node or device to a destination node or device across the network, where at least a portion of the communication is completed wirelessly. Accordingly, the various components of such a network may be communicatively interconnected via wireless links or channels and / or via wired links or channels. The various components of the network include modules and channel s / links implemented in hardware, software, and in a combination of hardware and software.

[0026] The communication network may include a plurality of discrete communication network (CN) components. Each CN component may be configured to provide a discrete functionality for data communication from a source device across the network to a destination device. The communication / wireless network may further include a processor arranged to configure at least one (or each) of the plurality of CN components with a set of TSN parameters of the TSN mechanism. Configured with the set of TSN parameters, the at least one (or each) of the plurality of CN components may support time-sensitive deterministic communication as a TSN block in a TSN network in accordance with the set of TSN parameters. In other words, the at least one (or each) of the plurality of CN components may operate as a typical TSN block in a TSN network.

[0027] The plurality of discrete CN components of the 5G network may include a UE, a RAN, a UPF, a control plane function, and transport network channels connecting CN components including a transport network channel connecting the RAN and the UPF. In some implementations, the at least one of the plurality of CN components that is configured with the TSN parameter(s) is the 5G transport network channel connecting the RAN and the UPF.

[0028] The communication network may include a configuration module configured to determine the set of TSN parameters for the at least one of the plurality of CN components, the set of TSN parameters including one or more of a TSN stream identification, a transmission schedule, a deadline or delay budget, a filtering configuration, and a redundancy scheme. In some implementations, the configuration module is configured within a session management function (SMF) of a control plane of the 5G network.

[0029] In some implementations, the present disclosure provides a communication network, e.g., a wireless network such as a 5G network, includes a plurality of configuration modules interconnected in a certain arrangement and configured to provide a unique set of TSN parameters to each of the plurality of CN components such that each of the plurality of CN components supports time-sensitive deterministic communication as a corresponding TSN block in a TSN network in accordance with the respective unique set of TSN parameters. The plurality of configuration modules may be interconnected in a hierarchical arrangement, a mesh arrangement, a star arrangement, a tree arrangement, or a combination thereof.

[0030] In some implementations, the present disclosure provides a system including a first communication (such as 5G / 6G) network, a second communication (such as a 5G / 6G) network, and a TSN transport channel in connection with the first and second communication networks. The first network may include a first plurality of discrete CN components, each CN component configured to provide a discrete functionality for data communication via the first network, wherein at least one of the first plurality of CN components is configured to provide timesensitive deterministic communication in accordance with a first set of TSN parameters. The second network may include a second plurality of discrete CN components, each CN component configured to provide a discrete functionality for data communication via the second network, wherein at least one of the second plurality of CN components is configured to provide time-sensitive deterministic communication in accordance with a second set of TSN parameters. The TSN transport channel may be configured to facilitate time-sensitive deterministic communication of data exchanged between the first network and the second network, in accordance with a third set of TSN parameters. In some implementations, the TSN transport channel is communicatively connected with a first TSN translator of the first wireless network and a second TSN translator of the second wireless network.

[0031] Further, as described above, probing of a wireless communication network is inefficient and may introduce overhead traffic. Accordingly, there is a need to minimize the amount of overhead traffic in the wireless communication network. In some embodiments, a wireless communication network in accordance with 3GPP-defined specifications (e.g., 5G, 6G,) is combined with an artificial intelligence / machine learning (AI / ML) system to improve network probing techniques, e.g., to improve fault detection and / or diagnose emergent faults in the wireless communication network. For example, rather than continuously requesting most, if not all, network management information from the wireless communication network, an AI / ML system learns in real-time (or near real-time) to infer what data or metrics are required and reduces the amount of collected data. More specifically, for example, the AI / ML system learns to improve the queries or probes such that orthogonality, redundancy, and pertinence of the collected information is improved. In some embodiments, the AI / ML system learns to intelligently query the real-world environment (e.g., airports, wind tunnels, or planes with similar controllers) to refine a controller that is already developed, or while the controller is under development. In some embodiments, the AI / ML system learns to optimize queries or probes to obtain from the wireless communication network and to minimize overhead in querying redundant information. In some embodiments, the AI / ML system learns from information as it is being collected (i.e., learns from feedback while querying).

[0032] In some implementations, the present disclosure provides a system associated with a communication network configured to support time-sensitive deterministic communication based on a TSN mechanism. The system comprises a processor configured to optimize an input query to retrieve status information related to data communication over the communication network based on an artificial intelligence (Al) model, the communication network including a plurality of discrete communication network (CN) components, each CN component configured toprovide a discrete functionality for the data communication from a source device across the communication network to a destination device; retrieve the status information using the optimized query; and generate a network schedule based on the status information such that at least one of the plurality of CN components supports time-sensitive deterministic communication as a network block based on the network schedule.

[0033] Fig. 1 illustrates a non-limiting example of a conventional integrated TSN-5G system 100 in which a 5G network or system 106 is configured to be emulated as a single TSN component (e.g., a TSN bridge). Overall, the system 100 is configured as a deterministic TSN system to communicate data between end-devices, e.g., input / output (I / O) devices 102 and a controller 104, via the 5G system 106 (emulating as a TSN bridge) and one or more (conventional) TSN bridges 108 and using a TSN controller 110. The system 100 is configured based on standard methods for time synchronization and traffic management, allowing deterministic communication over standard Ethernet networks between end-devices, e.g., the I / O devices 102 and the controller 104. For example, the system 100 may operate in accordance with the IEEE 802. IQ TSN specification suite, which standardizes layer-2 communication for networking protocols providing deterministic communication while sharing the same infrastructure. For example, a number of standards establish various technological paradigms for a TSN system - clock synchronization (802. IAS, generalized Precision Time Protocol (gPTP)), frame preemption (802.3br and 802.1Qbu), scheduled traffic (802.1Qbv), and redundancy management (Frame Replication and Elimination for Reliability (FRER) IEEE 802.1CB). These standards work together at the Ethernet layer-2 to ensure that control and safety functions are executed while meeting their respective deadlines and constraints. As another implementation, similar integrated system may be configured to implement TSN techniques over a wireless local area network (wireless LAN) such as a Wi-Fi network, e.g., based on Wi-Fi 6 and other common wireless LANs.

[0034] For example, the 802.1Qbv TSN standard provides scheduled transmissions for safety-critical data frames in a predetermined manner, and is incorporated herein in its entirety. As used herein, “TSN schema” can refer, without limitation, to networks, components, elements, units, nodes, hubs, switches, controls, modules, pathways, data, data frames, traffic, protocols, operations, transmissions, and combinations thereof, that adhere to, are configured for, or arecompliant with, one or more of IEEE 802.1 TSN standards. The 802. IQbv TSN standard addresses the transmission of critical and non-critical data traffic within a TSN. Critical data traffic is guaranteed for delivery at a scheduled time while non-critical data traffic is usually given lower priority. Various traffic classes have been established according to IEEE 802. IQ that are used to prioritize different types of data traffic.

[0035] Ethernet frame preemption is defined by the IEEE 802.3br and IEEE 802. IQbu standards, which can suspend the transmission of a non-critical Ethernet frame, is also beneficial to decrease latency and latency variation of critical traffic. Resource management basics are defined by the TSN configuration models (IEEE 802.1Qcc). Centralized Network Configuration (CNC) 112 can be applied to the network devices (bridges, e.g., the 5G system bridge 106, bridges 108), whereas, Centralized User Configuration (CUC) 114 can be applied to user devices (end stations, e.g., the I / O devices 102), e.g., as specified in IEEE 802.1Qdj [xx], The fully centralized configuration model follows a software-defined networking (SDN) approach. In other words, the CNC 112 and the CUC 114 in the controller 110 provide the control plane instead of distributed protocols. In contrast, distributed control protocols are applied in the fully distributed model, where there may be no CNC or CUC.

[0036] High availability, as a result of ultra-reliability, may be provided by FRER (IEEE 802.1CB) for data flows through a per-packet-level reliability mechanism. This provides reliability by transmitting multiple copies of the same data packets over disjoint paths in the network. Per-Stream Filtering and Policing (802.1Qci) improves reliability by protecting against bandwidth violation, malfunctioning and malicious behavior. Further, the time synchronization in the TSN system may be defined by the gPTP (802. IAS), which is a profile of the Precision Time Protocol standard (IEEE 1588). The gPTP provides reliable time synchronization, which can be used by other TSN tools, such as Scheduled Traffic (802.1Qbv).

[0037] To achieve desired levels of reliability, TSNs employ time synchronization and time- aware data traffic shaping. The data traffic shaping uses the schedule to control gating of transmissions on the network switches and bridges (e.g., nodes). In some aspects, the schedules for such data traffic in TSNs can be determined prior to operation of the network. In other aspects, the schedules for data traffic can be determined during an initial design phase based onsystem requirements and updated as desired. For example, in addition to defining a TSN topology (including communication paths, bandwidth reservations, and various other parameters), a networkwide synchronized time for data transmission can be predefined. Such a plan for data transmission on communication paths of the network is typically referred to as a “communication schedule” or simply “schedule.” The schedule for data traffic on a TSN can be determined for a specific data packet over a specific path, at a specific time, for a specific duration. A non-limiting example of a technique for generating a schedule for TSN data traffic is discussed in U.S. Application No. 17 / 100,356, which is incorporated herein in its entirety by reference.

[0038] Time-critical communication between end devices or nodes (e.g., the I / O devices 102 and the controller 104) in TSNs includes “TSN flows” also known as “data flows” or simply, “flows.” For example, data flows can comprise datagrams, such as data packets or data frames. Each data flow is unidirectional, going from a first originating or source end device (e.g., the I / O device 102) to a second destination end device (e.g., the controller 104) in a system, having a unique identification and time requirement. These source devices and destination devices are commonly referred to as “talkers” and “listeners.” Specifically, the “talkers” and “listeners” are the sources and destinations, respectively, of the data flows, and each data flow is uniquely identified by the end devices operating in the system. It will be understood that for a given network topology comprising a plurality of interconnected devices, a set of data flows between the inter-connected devices or nodes can be defined. For example, the set of data flows can be between the interconnected devices. For the set of data flows, various subsets or permutations of the dataflows can additionally be defined. Further, time-critical communication between end devices or nodes in TSNs includes “TSN streams” or “streams,” where each TSN stream may originate at a specific talker node intended to be communicated to one or more listener nodes. As such, each TSN stream may include one or more data flows, where each data flow is between the talker node (where the TSN stream originated) and a listener node.

[0039] Both end devices (e.g., 102, 104) and switches (commonly called “bridges” or “switching nodes”) (e.g., 106, 108) transmit and receive the data (in one non-limiting example, Ethernet frames) in a data flow based on a predetermined time schedule. The switching nodes and end devices must be time synchronized to ensure the predetermined time schedule for thedata flow is followed correctly throughout the network. For example, in Fig. 1, the clocks 116 represent that the various switching nodes and end devices in the TSN system 100 (including in the 5G system 106) are time synchronized with reference to a global clock (grandmaster clock timing). In some other aspects, only the switches can transmit the data based on the predetermined schedule, while the end devices, for example legacy devices, can transmit data in an unscheduled manner.

[0040] The data flows within a TSN can be scheduled using a single device (e.g., the controller 110) that assumes fixed, non-changing paths through the network between the talker / listener devices and switching nodes in the network. Alternatively, the data flows can be scheduled using a set of devices or modules. The scheduling devices, whether a single device or a set of devices, can be arranged to define a centralized scheduler. In still other aspects, the scheduler devices can comprise a distributed arrangement. The TSN can also receive non-time sensitive communications, such as rate-constrained communications. In one non-limiting example, the scheduling devices can include an offline scheduling system or module.

[0041] TSN traffic may be tagged using a variety of mechanisms, including VLAN tag Ethernet address IP header information, and a combination of VLAN tag Ethernet address and IP header information. Traffic may be identified and tagged anywhere in the system before protocol data unit (PDU) identification is required. A TSN Talker may create multiple TSN flows (streams) with different TSN latency and determinism requirements and may be assigned different paths that meet the requirements. In some embodiments, the latency and determinism values may be specified and offered to TSN applications as a limited set of static, discrete values, rather than an offering to accept an unlimited set of continuous values.

[0042] In some implementations, the I / O end device 102 may be, in various aspects, a complex mechanical entity such as the production line of a factory, a gas-fired electrical generating plant, avionics data bus on an aircraft, a jet engine on an aircraft amongst a fleet (e.g., two or more aircraft), a digital backbone in an aircraft, an avionics system, mission or flight network, a wind farm, a locomotive, etc. In various implementations, the I / O end device 102 may include any number of end devices, such as sensors, actuators, motors, and software applications. The sensors may include any conventional sensor or transducer, such as a camerathat generates video or image data, an x-ray detector, an acoustic pick-up device, a tachometer, a global positioning system receiver, a wireless device that transmits a wireless signal and detects reflections of the wireless signal in order to generate image data, or another device.

[0043] Further, the actuators (e.g., devices, equipment, or machinery that move to perform one or more operations of the I / O device 102) can communicate using the TSN system 100. Nonlimiting examples of the actuators may include brakes, throttles, robotic devices, medical imaging devices, lights, turbines, etc. The actuators can communicate status data of the actuators to one or more other devices (e g., other I / O devices 102, the controller 104 via the TSN system 100). The status data may represent a position, state, health, or the like, of the actuator sending the status data. The actuators may receive command data from one or more other devices (e.g., other I / O devices 102, the controller 104) of the TSN system 100. The command data may represent instructions that direct the actuators how or when to move, operate, etc.

[0044] In some implementations, the controller 104 can communicate a variety of data between or among the I / O end devices 102 via the TSN 100. For example, the control system 104 can communicate the command data to one or more of the devices 102 or receive data, such as status data or sensor data, from one or more of the devices 102. Accordingly, the controller 104 may be configured to control operations of the I / O devices 102 based on data obtained or generated by, or communicated among the I / O devices 102 to allow for, e.g., automated control of the VO devices 102 and provide information to operators or users of the I / O devices 102. The controller 104 may define or determine the data flows and data flow characteristics in the TSN system 100.

[0045] Referring now to the 5G system 106 within the system 100, the 5G network or system 106 is a wireless communication network or system used to carry TSN traffic between various TSN end devices, e g., the I / O devices 102 and the controller 104. In some implementations, the 5G system 106 is configured to emulate as one TSN bridge per UPF (similar to TSN bridges 108, according to the TSN standards discussed above). The 5G system 106 may be a New Radio (NR) network implemented in accordance with 3GPP 23 and 38 series specifications (which are incorporated herein in their entirety), and integrated into the system 100 in accordance with the 3GPP Release 17 23.501 standard (for example, V17.1.1 and V17.2.0), which is incorporatedherein in their entirety. As shown, the 5G system 106 may include various CN components such as, in the 5G user plane, UE 118, RAN (gNB) 120, UPF 122, and in the 5G control plane, application function (AF) 124, and SMF and policy control function (PCF) 126, among other components, as defined in the 3GPP 23.501 standard. In some implementations, the 5G system 106 may be configured to provide an URLLC service. The 5G system 106 based on the NR interface includes several functionalities to achieve low latency for selected data flows. NR enables shorter slots in a radio subframe, which benefits low-latency applications. NR also introduces mini-slots, where prioritized transmissions can be started without waiting for slot boundaries, further reducing latency. As part of giving priority and faster radio access to URLLC traffic, NR introduces preemption, where URLLC data transmission can preempt ongoing non- URLLC transmissions. Additionally, NR applies very fast processing, enabling retransmissions even within short latency bounds.

[0046] In some implementations, 5G defines extra-robust transmission modes for increased reliability for both data and control radio channels. Reliability is further improved by various techniques, such as multi -antenna transmission based on multiple-input and multiple-output (MIMO) techniques, the use of multiple carriers and packet duplication over independent radio links.

[0047] Time synchronization is embedded into the 5G cellular radio systems as an essential part of their operation, which has already been common practice for earlier cellular network generations. The radio network components themselves are also time synchronized, for instance, through the precision time protocol telecom profile, e.g., based on a 5G internal system clock 190. This provides a good basis to produce synchronization for time-critical applications. For URLLC service, the 5G system 106 uses time synchronization for its own operations, as well as the multiple antennas and radio channels that provide reliability. Besides the 5G RAN features, the 5G system 106 may also provide solutions in the core network for Ethernet networking and URLLC. The 5G core network supports native Ethernet PDU sessions. 5G assists the establishment of redundant user plane paths through the 5GS, including RAN, the core network, and the transport network. The 5GS also allows for a redundant user plane separately between the RAN and core network nodes, as well as between the UE and the RAN nodes.

[0048] As noted above, in the integrated system 100, the 5G system 106 includes one TSN (virtual) bridge per UPF. The 5G system 106 includes TSN Translator (TT) functionality for the adaptation of the 5G system 106 to the TSN domain, both for the user plane and the control plane, hiding the 5G system 106’s internal procedures from the TSN bridged network. The 5G system 106 provides TSN bridge ingress and egress port operations through the TT functionality. For instance, the TTs support hold and forward functionality for de-jittering. Fig. 1 illustrates the case when the 5G system 106 connects an end station 102 to a bridged network 108; however, the 5G system 106 may also interconnect bridges 108.

[0049] For the 5G system 106 to be integrated into the TSN system 100, requirements of a TSN stream can be fulfilled only when resource management allocates the network resources for each hop along the whole path. In line with TSN configuration (802.1Qcc), this is achieved through interactions between the 5G system 106 and a configuration controller, e.g., a centralized configuration controller 110 (including the CUC 114 and the CNC 112) and / or a set of decentralized controller modules (e.g., as discussed below with respect to Fig. 2). The interface between the 5G system 106 and the CNC allows for the CNC 112 to learn the characteristics of the 5G virtual bridge, and for the 5G system 106 to establish connections with specific parameters based on the information received from the CNC 112. Bounded latency requires deterministic delay from 5G as well as QoS alignment between the TSN and 5G domains. For instance, if a 5G virtual bridge acts as a TSN bridge, then the 5G system 106 emulates time-controlled packet transmission in line with Scheduled Traffic per 802.1Qbv for example. For the 5G control plane, the TT in the AF 124 receives the transmission time information of the TSN traffic classes from the CNC 112. In the 5G user plane, the TT at the UE 118 and the TT at the UPF 122 may regulate the time-based packet transmission accordingly. The different TSN traffic classes may be mapped to different 5G QoS Indicators (5QIs) in the AF 124 and the PCF 126 as part of the QoS alignment between the TSN and 5G domains, and the different 5QIs are treated according to their QoS requirements.

[0050] With respect to time synchronization, the 5G system 106 may implement the gPTP of the connected TSN network. The 5G system 106 may act as a virtual gPTP time-aware system and support the forwarding of gPTP time synchronization information between end stations 102and bridges 108 through the 5G user plane TTs. All of the various 3GPP and TSN standards mentioned in this disclosure are incorporated herein by reference in their entireties.

[0051] Referring now to Fig. 2, which illustrates a block diagram of a system 200 architecture in accordance with some implementations of the subject technology. Broadly, the system 200 depicts a block diagram of an example implementation of an integrated TSN-5G system similar to the system 100 described above, and also include similar physical components as in the system 100 discussed above. However, unlike the system 100, the system 200 provides a novel architecture for an integrated TSN-5G system in which the 5G system 106 is configured as a set of discrete 5G components where each 5G component is configured to emulate as one discrete TSN block or element 202. In other words, in the system 200, the 5G system 106 is configured as a disaggregated structure including a plurality of TSN blocks 202-1 to 202-N, where each TSN block 202 is configured in accordance with TSN specifications (e.g., per IEEE 802.1 and related standards discussed above), e.g., as a TSN bridge, TSN end device (i.e., as a TSN Talker and / or a TSN Listener), or a combination of the two. Further, instead of having a centralized configuration controller 110 to control the TSN-5G system, the subject technology provides for a plurality of distributed configuration modules 215 in a distributed controller 210 in the TSN-5G system 200. The configuration modules 215-a to 215-f may be interconnected in one or more topologies (mesh, star, tree), and each configuration module 215 may be responsible to communicate with and configure one or more TSN blocks 202. In some implementations, one or more of the configuration modules 215-a to 215-f may be implemented within or as part of a function (e.g., SMF 126) of the control plane of the 5G system 106. As used herein, a “topology” can refer to one or more arrangement(s) of a network that can include a plurality of nodes (e.g., sender devices, receiver devices, switches, or bridges) and connecting lines (e.g., communication links, or “hops” including wired communication links or wireless communication links) between the nodes in the network. Each link can communicatively couple a corresponding pair of nodes. A set of links can be coupled in sequence via their respective nodes to define a link path, for example, between an originating node and a destination node. Topologies may comprise, but are not limited to, one or more of mesh, star, bus, ring, and tree topologies.

[0052] In some implementations, the system 200 is configured to support and manage deterministic TSN data flows between a data source 204 (“source device”) and a data destinationdevice 206 (“destination device”) via the 5G system 106 in accordance with TSN configuration including a TSN schedule determined by one or more of the configuration modules 215. The data source 204 and the data destination 206 may include one or more of the I / O devices 102 and the controller 104. Although not shown, the system 200 may also include TSN bridges 108 and other TSN components.

[0053] In some implementations, in the disaggregated structure, each of the plurality of TSN blocks 202-1 to 202-N may correspond to one specific component of the 5G system, e.g., the 5G network or system 106 shown in Fig. 1 and discussed above. For example, as shown in Fig. 3, the UE 118 may be configured to emulate as the TSN block 202-1, the RAN 120 may be configured to emulate as the TSN block 202-2, the 5G transport network link 140 between the RAN 120 and the UPF 122 may be configured to emulate as the TSN block 202-3, the UPF 122 may be configured to emulate as the TSN block 202-4, and the core network and / or other typical components of a 5G system (e.g., fronthaul, backhaul, or MEC module) may be configured to emulate as one or more TSN blocks 202-N. Each TSN block 202-1 to 202-N is configured in accordance with TSN specifications (e.g., per IEEE 802.1 and related standards discussed above), e.g., as a TSN bridge, TSN end device (TSN Talker and / or TSN Listener), or a combination of two.

[0054] In some implementations, as shown in Fig. 4, each TSN block 202 includes a processor 402, a memory device 404, an internal configuration interface (ICI) 406, a transmission module 408, a reporting module 410, and TSN TTs-1 412-1, TT-2 412-2, and TT-3 412-3. The processor 402 may be a microprocessor or multi-core processor, an integrated circuit, a field programmable gate array, etc. that processes TSN configuration data and executes instructions (stored in memory device 404, for example) to process and transmit TSN data traffic from one or more TSN data flows in accordance with the TSN configuration data.

[0055] The memory device 404 may store a set of parameters describing the capabilities to support and execute a data flow (e.g., carrying URLLC data traffic) through the corresponding TSN block 202. In some implementations, the set of parameters include, but are not limited to, identity, link quality, link bandwidth. The identity parameter(s) may include the device type (i.e., whether the TSN block 202 is a TSN bridge or a TSN end station). The latency parameter(s) mayinclude at least port-to-port (start of TSN block to end of TSN block) latency, and latency variation (commonly known as “jitter”). The link quality parameter(s) may include at least packet error rate. The link bandwidth parameters) may include at least the available bandwidth in bits per second.

[0056] In some implementations, the set of parameters for a TSN block 202 may include a subset of parameters specific to 5G RAN including short transmission-time intervals, TSC assistance information (TSCAI), configured grant (CG) information, semi-persistent scheduling (SPS) allocation and / or other parameters as specified in, e.g., 3GPP TS 28.540. Further, in some implementations, the set of parameters for a TSN block 202 may include a subset of parameters specific to TSN including time synchronization properties, scheduled transmissions (Qbv) attributes, redundancy attributes including a number of RANs connected to, number of paths to UPF, path diversity, number of available frequencies, propagation characteristics, available radios, different physical media (e.g., free space optics). As an example, Fig. 5 illustrates an example set of parameters 502 for an example TSN block 202.

[0057] In some implementations, the set of parameters for a TSN block 202 may define a worst case time synchronization error, a worst case gate operation error, a maximum gate control list size, a maximum cycle time, a maximum gate interval duration, a transmission start delay, the like, or a combination thereof. The set of parameters may include a discrete set of deterministic parameters that may vary by the type of traffic handled by the TSN block 202. The set of parameters may be at least partially utilized to generate a TSN Schedule, configuration, or the like, that is realizable on the hardware of the TSN block 202. In the absence of such set of parameters, a TSN scheduling module has to take a lowest common denominator approach, wherein all devices of the system 200 including TSN blocks 202 are assumed to have the most limiting characteristics resulting in a sub-optimal solution. In this sense, the set of parameters enable better scheduling solution in the TSN system 200 resulting in improved performance metrics including latency jitter, packet delay variation, and bandwidth utilization.

[0058] In some implementations, the set of parameters for a TSN block 202 may further describe or relate to devices created, programmed by, or otherwise of different operation or manufacturer. In this sense, the set of parameters may define a set or subset of disparate devices(e g., heterogenous, as from multiple vendors), as opposed to homogenous or all-similar devices. For example, the set of parameters may include, but are not limited to, definitions of specific configuration models, error tolerances, hardware limitations, software limitation, and firmware options of each end node and switching node in the system 200. In this sense, the set of parameters enable scheduling and configuration of a heterogenous network comprising devices from multiple vendors and of varying characteristics.

[0059] In some implementations, the set of parameters for a TSN block 202 may further the specific TSN features supported by each end node and switching node in the TSN system 200. For example, the set of parameters may define if a node or TSN block 202 supports one or more of time synchronization, time aware shaping, asynchronous shaping, frame replication and elimination for reliability, frame pre-emption, ingress policing, and other TSN features. The set of parameters may further define the specific version or variant of the feature or standard supported by end nodes and switching nodes in the TSN system 200. This set of parameters enables scheduling and configuration of a mixed capability network wherein end nodes and switching nodes have varying degrees of support (including no support) for the desired TSN features and version.

[0060] Further non-limiting examples of the set of parameters for a TSN block 202 device may include, but are not limited to, additional parameters utilized for programming functionality of the respective TSN block 202. For example, additional set of parameters may define or enable for the programming of the respective TSN block 202 with a generated or scheduled TSN configuration. Non-limiting examples of the set of parameters that may define or enable for the programming of the respective TSN block 202 can include, but are not limited to, a programming method, a communication protocol, a device login name, a device login password, a device programming port, a device programming file structure or file path for programming data, a device programming file format, a device configuration file format, a device schedule file format, the like, or a combination thereof. In this sense, this set or subset of parameters defining or enabling for the programming of the respective TSN block 202 with a generated or scheduled TSN configuration (collectively, “programming parameters”) and enable or allow for the system 200 to update, install, program, configure, or otherwise modify the set of TSN blocks 202 tooperate in response to, or in accordance with, a schedule or a configuration for the TSN system 200.

[0061] Further, each TSN block 202 may include at least one ICI 406 configured to support interaction of the TSN block 202 with, e.g., its respective configuration module 215. The ICI 406 may be used to provide some or all of the set of parameters of the TSN block 202 to the respective configuration module 215, and receive from the configuration module 215 configuration data (e.g., transmission schedule, data flow identities, policing rules, etc.) to support one or more data flows through the TSN block 202. Each TSN block 202 may be configured to execute or operate according to the configuration data (received via the ICI 406) and transmit data for each data flow according to the specifications provided in the configuration data.

[0062] In some implementations, the configuration data received via the ICI 406 at a TSN block 202 from its respective configuration module 215 may include stream identification information, transmission schedule or a deadline or delay budget or (for rate-constrained traffic) a data rate, filtering and policing configuration information, redundancy scheme and / or other TSN configuration information.

[0063] TSN Talker information may be separated into different frequency components that require different TSN flow latency and determinism requirements. Conversely, TSN flows from different TSN Talkers may be aggregated into a single TSN flow in order to achieve greater capacity and higher channel utilization. Having discrete cycle times in the integrated TSN-5G system may help ensure ease of TSN flow aggregation.

[0064] In some implementations, each TSN block 202 may include the transmission module 408 that is configured to send the transmission of the specific data flow as per the schedule, deadline, delay budget, or data rate received in the configuration data from the configuration module 215 via the ICI 406. In one example in which the TSN block 202-1 corresponds to a UE 118 of the 5G system, the transmission module 408 is configured to send the data transmission based on the resource scheduling of the 5G air interface (between the UE 118 and the RAN 120). The resource scheduling of the 5G air interface may be phase aligned with the integrated TSN- 5G system’s cycle time. The transmission module 408 may assign resource elements of the 5Gcomponent corresponding to the TSN block 202 (of which 408 is a part) in such a manner as to be able to meet schedule transmissions for each Qbv stream. For example, instead of using classical 802.1Qbv style gate control, the UE 118 corresponding to the TSN block 202-1 may use a phase offset (in reference to a cycle time) to align the transmission of a TSN data stream with the assigned schedule. In this example, instead of the UE 118 getting this phase offset from the CNC, the UE 118 may get the phase offset as a special command from the RAN 120.

[0065] In some implementations, each TSN block 202 may also include a reporting module 410 configured to monitor run-time behavior of the TSN block 202 and report the behavior either through the ICI 406 or via a separate interface of the TSN block 202. This behavior monitoring includes metrics including, but not limited to, packet drops and missed transmission windows.

[0066] Referring back to Fig. 2, the subject technology provides for a plurality of distributed configuration modules 215 in a distributed controller 210 in the TSN-5G system 200. The configuration modules 215-a to 215-f may be interconnected in one or more topologies (mesh, star, tree) and each configuration module (CM) 215 may be responsible to communicate with and configure one or more TSN blocks 202. For example, the CM 215-a may be responsible for and operatively and communicatively connected to the TSN blocks 202-1 and 202-2. The CM 215-b may be responsible for and operatively and communicatively connected to the TSN blocks 202-3. The CM 215-c may be responsible for and operatively and communicatively connected to the TSN blocks 202-3 and 202-N. As such, the TSN block 202-3 may be configured and controlled by both the CM 215-b and the CM 215-c. For example, a portion of functionalities (e g., with respect to a first type of TSN applications) at the TSN block 202-3 may be configured and controlled by the CM 215-b, and another portion of functionalities (e.g., with respect to a second type of TSN applications) at the TSN block 202-3 may be configured and controlled by the CM 215-c.

[0067] In the example shown in Fig. 2, the configuration modules 215 are arranged in a tree structure where the CM 215-f forms the highest level of the tree structure, the CM 215-a, 215-b, and 215-c form the lowest level of the tree structure, and the CM 215-d and 215-e are between the highest and lowest levels of the tree structure. The configuration modules 215, however, are operatively and communicatively connected to each other via an API 230. In someimplementations, in a tree structure (e.g., as shown in Fig. 2), the configuration modules 215 may communicate with other configuration modules 215 at an adjacent tree level (one tree level higher or lower). However, in other topologies (e.g., in a mesh or peer-to-peer structure), any two configuration modules 215 in a distributed controller 210 may be operatively connected to and communicate with each other directly.

[0068] In some implementations, each configuration module 215 may be either an external utility responsible for configuring one or more corresponding TSN blocks 202 or may be configured as a software module within the TSN block 202. Each configuration module 215 may be configured to determine and provide to TSN blocks 202 controlled by the configuration module 215 configuration data including a TSN schedule for one or more data flows to be transmitted through the TSN blocks 202. The configuration modules 215 may exchange information with each other using a standardized API 230. The exchanged information may include information regarding cycle times for the TSN system (e.g., supported ACTs including discrete levels of ACT buckets each corresponding to a specific data flow, max / min cycle times), configuration data including transmission schedule (include time offsets / duration / resources for transmission) for one or more TSN blocks 202, and information to request or respond to requests for resource allocations. In some implementations, one or more of the configuration modules 215 may be configured as the CNC 112 and / or the CUC 114 of the TSN controller 110 discussed above. In some implementations, one or more of the configuration modules 215-a to 215-f may be implemented within or as part of a function (e.g., SMF 126) of the control plane and / or an element or function (e.g., the 5G transport network 202-3) of the user plane of the 5G system 106. For example, the SMF 126 may be configured to support functionalities of the CUC 114 and / or the CNC 112. In some implementations, the 5G transport network 202-3 may be configured to support functionalities of the CNC 112.

[0069] In some implementations, each configuration module (CM) 215 receives from the ICI 406 of the TSN block 202 controlled by the CM 215 some or all of the set of parameters (discussed above) of the TSN block 202. The CM 215 also receives, from another entity in the system 200 through the API 230, information on the data flows to be configured through the TSN block 202. In a non-limiting example, the data source 204 and / or the data destination 206 provide requirements for a data flow between the data source 204 and the data destination 206 tothe CM 215 either directly or via an intermediary such as a CUC 114. In some implementations, the CM itself may allow users to define the set of data flows to be configured via a user interface. As used herein, the information on the data flows may include or define a set of data flows, data streams, transmission pathways (predetermined or otherwise adapted), or the like, to define the desired TSN communication pathways between the data source 202 and the data destination 206. A set of non-limiting examples of the information on the data flows may include the maximum allowable latencies, data rate, data frame sizes (“payload”), data frame destinations, band allocation gaps, the like, or a combination thereof.

[0070] Based at least on the received data flow information and the set of parameters for the TSN block 202, the CM 215 determines a “solution” (or configuration data), wherein the solution would indicate how to handle each data flow through the TSN block 202. This solution may include time aware schedule, policing rules, amongst other things, as discussed in U.S. Application No. 17 / 100,356, which is incorporated herein in its entirety by reference. The CM 215 may then send this solution or configuration data to the TSN block 202 via the ICI 406. The TSN block 202 may then execute this solution and transmit data for each flow according to its configuration. In some implementations, as an example process to make the distributed CMs 215 compute a solution, at each level of the tree structure of the CMs 215, a same system modulo theory (SMT) solver may be used, wherein the data flows and their requirements are expressed as constraints and a linear programming methodology is used to solve for a feasible solution. The solution from a lower level of the CM tree is input as used resources (represented again by constraints) at the one-level higher on the CM tree. This process is repeated until the top-most level of the CM tree is reached, where a global solution is determined.

[0071] In some implementations, different data sources (e.g., the data source 204) and their applications operate at different cycle times or intervals. Relatedly, different data sources and destinations (and their applications) require different levels of time determinism for many data flows therebetween. In a conventional TSN system, a converged cycle time (commonly known as “admin cycle time”) is determined that works for all the data flows in the network. However, in some implementations of the subject disclosure, the integrated TSN-5G system may use a set of discrete / quantized cycle times in the network. Each data flow chooses one of the available quantized cycle times to operate on. The scheduling in the TSN-5G system 100 for scheduledtransmissions may be based on a quantized / discrete set of cycle times. As an example, the integrated TSN-5G system 100 may limit the available stream intervals and therefore the corresponding cycle times to a set of discrete values including, but not limited to, essentially 1, 10, 100, 1000 milliseconds. Similarly, the stream or data flow requirements may be restricted, e.g., jitter (packet delay variation) requirements could be limited to a predetermined set of discrete values including, but not limited to, essentially 1, 10, 100, 1000 microseconds. In some implementation, a different set of discrete values may be used depending on the applications and use cases supported by the integrated TSN-5G system. For example, geographically dispersed system may use discrete cycle times in the order of milliseconds. In yet another example, a system restricted to a local factory may use discrete cycle times in the order of microseconds. The members of this discrete set may be regularly or irregularly spaced or follow other statistical distribution (including, but not limited to, logarithmic, linear, gaussian) without defaulting to a continuous set of cycle times. In another implementation, a set of cycle times is standardized in such a manner that all TSN blocks have cycle times that are a product of elements chosen from a small, common set of prime numbers. This ensures that all composite cycle times will be easily computed by the TSN scheduler and results in one common network cycle time.

[0072] Each TSN block in the integrated TSN-5G system may support a set (wherein a set includes one or more) of cycle times. The CMs 215 configure the TSN-5G system 106 or 200 to enable scheduled transmission of data flows across TSN blocks 202 operating at different cycle times. In some implementations, the TSN blocks 202 may be required to operate with compatible cycle times, where compatibility implies the cycle times are an integer harmonic of each other. When an application requests an interval that does not directly map to the available set of discrete cycle times across the set of TSN blocks 202 the flow traverses, the CMs 215 may fit to the closest available cycle time. The closest available cycle time would be an integer multiple or integer divisor of the available cycle times. The CMs 215 may exchange the supported quantized / discrete set of cycle time information with each other during the configuration process. In this instance, the subject disclosure allows a disaggregated TSN-5G system to create a feasible configuration for a large number of data stream s / flows. In the absence of a quantized cycle / interval, the configuration typically requires large computation time and may even prevent the discovery of a feasible solution.

[0073] In some instances, each integrated TSN-5G network slice in the 5G system 106 may have a predefined set of supported cycle times and jitter bounds. In some instances, network slices might be more granular than the typical 5G network slice as per 3GPP specification 23.501, which is incorporated herein by reference. In some implementations, the TSN-5G system 106 or 200 may be sliced based on the TSN cycle times. For example, a 5G network supporting multiple critical services may have URLLC slices dedicated to the periodicity of the applications and their streams. For example, services that have applications operating at approximately 1 msec period may have a dedicated slice that operates at 1 msec cycle time. Similarly, services and applications operating at 100 msec period (or interval) may have a dedicated slice in the integrated TSN-5G system that operates at 100 msec cycle time. Such cycle-time slicing improves both the speed of configuration as well as the overall network performance. In some implementations, the TSN block 202 exposes the supported cycle time(s) for a given slice to its respective CM 215 through its ICI 406. The CMs 215 in turn may exchange with each other the supported cycle times for the TSN blocks under their management via inter-config module API 230 in order to create a configuration solution for the sliced TSN-5G system.

[0074] In some implementations, the solution or configuration data determined by a CM 215 may include, but is not limited to, a collective or set of configurations, timings, commands, controls, instructions, the like, or a combination thereof, for operating the respective TSN block 202 in accordance with the characteristics (e.g., as defined by the set of parameters) of the TSN block 202. In some aspects, the configuration data may include specific transmission information for an individual or collective (e.g., “global”) data frame transmission for one or more respective TSN blocks 202. The transmission information can include temporal information for the transmission of the data frame. In one or more aspects, the configuration data for a data frame can include a transmission start time. For example, the transmission start time can be the time at which the transmission of the data frame from the respective TSN block 202 initiates. In an aspect, the transmission of the data frame can be initiated by a selective opening of a gate of the respective TSN block 202 to transmit the data frame, as a data flow to a destination node (e.g., another TSN block 202). Conversely, the transmission of the data frame can be ceased or prevented by a selective closing of the gate of the respective TSN block 202 to transmit the data frame. The configuration data may also define or assign a specific path or link communicativelycoupling the respective TSN block 202 and another node to transmit the data flow thereon. Additionally, the configuration data may define a duration of the transmission of the respective data flow from the respective TSN block 202. In an aspect, the duration of the data flow transmission can be defined by a time period between the selective opening of the gate (i.e., to transmit the data frame) and the selective closing of the gate of the respective node (i.e., to cease transmission of the data frame to the destination node).

[0075] In a conventional TSN system, the TSN schedule is expressed as an absolute time offset in a periodic cycle at which a TSN block is instructed to transmit that data. However, that may be too restrictive for a 5G system 106 composed of components from multiple vendors. In some implementations, a deadline-based schedule is determined and provided with the configuration data by a CM 215. The deadline-based schedule may instruct the TSN block 202 to transmit the data of a configured data flow no later than a deadline (which is expressed in terms of absolute time in a periodic cycle). In some implementations, the delay budget based approach would instruct the TSN block to transmit the data frame of a configured data flow within a delay budget. As such, under the delay budget based approach, the TSN block 202 is required to send a data frame arriving at its ingress port within a certain duration to its egress port. Such a scheme would not require every TSN block 202 to be time synchronized. In some implementations in which the TSN block 202 is configured under a rate constraint, the TSN block 202 is configured to transmit the data frames of a given data flow in a manner that the average or peak transmission rate (in bits per second) does not exceed a configured value (per the configuration data from the CM 215).

[0076] In some 5G systems, TSN blocks may be a set of shared resources made available by network slicing based on service profiles bounding network latency, periodic cycle including, but not limited to, delay / budget. In such implementations, two-level scheduling may exist where the 5GS TSN-AF may enable configuring the TSN blocks 202 as a shared resource in addition to slice level TSN scheduling. In either case, the configuration attributes such as resource identifier may identify the TSN blocks for the appropriate configuration. As an example, a service provider may have multiple service profiles with specific periodic cycles, and multiple tenants of the service provider may utilize the same TSN blocks as specified by the 5GS TSN-AF configuration and may perform TSN flow aggregation. In some implementations, a serviceprovider may provide a set of non-shared TSN blocks where only a single layer of service profile may exist. Device specific operating / required resource sharing mode may be available to the CNC 112 through TSN-AF.

[0077] As an example implementation of the deadline / delay budget approach by a TSN block 202, when a data frame arrives at an ingress port of the TSN block 202, the TSN block 202 would note the arrival time of the data frame using its local clock. The TSN block 202 may then identify the frame as belonging to a configured data flow, and may then start a countdown timer equal to the configured delay budget for that data flow. Using the transmission module 408, the TSN block 202 may prioritize the transmissions of data with the least amount of time left. If the timer of a packet expires before it is transmitted, that event is recorded as a missed transmission and be accounted for in the monitoring metrics by the recording module 410.

[0078] In some implementations, with respect to scheduled transmissions between the TSN block 202-1 (corresponding to the UE 118) and the TSN block 202-2 (corresponding to the RAN 120) may involve “enhanced” allocation (assignment and transmission) of uplink and downlink transmissions between the UE 118 and the RAN 120 in order meet the schedule assigned by the CM 215-a to the TSN block 202-1 corresponding to the UE 118. In some implementations, the CM 215-a may take into account the UE 118 buffer status and radio conditions as reported by RAN 120 when instantiating the TSN schedule and may adjust or report the required change in requested schedule. In some other implementation, the CM 215-a may send real-time feedback on the radio conditions as received from RAN 120 to a master CM, e.g., the CM 215-d. This feedback loop may support re-calculating the TSN schedule to meet the packet delay budget either at this specific TSN block or across the TSN blocks on a given end-to-end path.

[0079] In this implementation, the link quality may be monitored and the CM 215-a may continuously adjust the radio resources in the configuration data to meet the transmission schedule. Radio resources may include, but are not limited to, a logical channel, transmission power, and UE specific slot duration. In some implementations, a static assignment of fixed / deterministic uplink and downlink slots for a given UE 118 may be made such that, e.g., all UEs connected to a given RAN slice are given a scheduled transmission slot. 5G native over-the- air scheduling may be used to determine if the transmission from the UE 118 will meet thetransmission deadline. If not, the UE 118 may request elevated access to the RAN 120 to achieve the schedule transmission. In some implementations, the scheduling in the 5G system 106 for scheduled transmissions may be based on a quantized / discrete set of cycle times, wherein the set includes at least a 100 msec admin cycle time. In accordance with the subject technology, the radio link between the UE (e.g., represented by TSN block 202-1) and RAN (e.g., represented by TSN block 202-2) may be sliced based on the cycle time. In some implementations, the uplink and downlink between TSN block 202-1 and TSN block 202-2 may have radio resources allocated to each network slice based on the cycle time of the slice. For example, a 1 msec cycle time slice would require radio resources (channels, airtime, etc.) capable of delivering data at the 1 msec rate.

[0080] In some implementations, 5G resource elements (e.g., frequency and time slots) may be scheduled such that they meet TSN flow latency requirements in addition to “standard” 5G scheduling traffic prioritization requirements. More specifically, 5G timeslots for a TSN flow may be allocated such that they transmit the TSN flow’s messages at both the proper cycle time offset (phase) and within the time limit (TSN window time) required by the TSN schedule. In this case, the 5G scheduler differs from a “traditional” TSN Ethernet port in that multiple messages may egress simultaneously if transmitted on different frequencies. In some aspects, under the presence of poor RF channel conditions, the 5G system 106 may send multiple copies of a message over different frequencies in order to increase the probability of meeting the transmission schedule and / or deadline.

[0081] To achieve reliable data transmissions in the system 200, redundant flow paths may be implemented. Disaggregated TSN blocks 202 of the 5G system 106 allow handling error (delayed, dropped, or corrupted frames) in a better way. In some implementations, UE 118 may initiate two redundant disjoint PDU Sessions to UPF 122 for redundancy in which case 5GC may configure the NG-RAN for dual connectivity according to 3GPP 38.300. In some other implementations, FRER may be used between some TSN blocks 202 but not others. For example, redundant streams may be implemented over the air interface between the UE 118 (TSN block 202-1) and the RAN 120 (TSN block 202-2) and then combined at the RAN 120, and can be split again over the core network (TSN block 202-3, 202-4), if needed. As shown in Fig. 1, current redundancy requirement according to 3GPP 23.501 is maximally disjoint pathsbetween the UE 118 and the UPF 122. However, in accordance with the subject technology, there is no need to establish redundant disjoint paths across the entire 5G system, and instead may be implemented for only a portion of the 5G system. For example, in case of the over-the- air interface between the UE 118 and the RAN 120, the redundancy requirement may specify that the two paths should be on different frequencies or different MIMO channels or different time slots. In some instances, the subject disclosure allows for flexible use of redundancy including, but not limited to, more than two data paths, merging and splitting of data flows between TSN blocks, and support of TSN blocks with varying degree of redundancy capabilities.

[0082] The concept of partitioning the 5GS into multiple TSN blocks (e.g., as discussed above with respect to Fig. 2) may potentially enhance security if strict scheduling can impede the flow of malicious traffic between said blocks. However, enabling the 5GS to operate as multiple TSN blocks may introduce security vulnerabilities, primarily via configuration. Specifically, TSN users may become aware of internal 5GS details and connectivity that impact other users sharing the same physical and logical infrastructure. This may occur during the required TSN network discovery phase (e.g., via information returned by the link layer discovery protocol). TSN users may also misconfigure either their own or other users’ configuration. This may be partially addressed using NETCONF’s notion of secure subtrees, where users have a limited view into their own data model subtree. The 5G system may inherently have a notion of isolated network slices, depending, for example, on whether TSN is implemented virtually (via software) or physically (via hardware). The infrastructure provider may have to set limits on what physical and logical capabilities are exposed to each TSN user. This may be done via the 5G network exposure function. The subject disclosure provides a solution to the security problem by using “virtual TSN blocks.” Virtual TSN blocks are internal 5G TSN blocks that contain only the capabilities offered by the user’s 5G network slice. In other words, users may only see and can configure the TSN block information that is exposed via the 5G network slice, and no more than that. In this sense, the internal 5G TSN block is the intersection of the set of information contained by the internal TSN block and the 5G network slice offered to the user.

[0083] In some implementations, IETF DETNET standards (as provided at IETF: “Deterministic Networking Working Group,” https: / / datatracker.ietf.org / wg / detnet / about / ) may be implemented or integrated into a 5GS to interconnect islands of smaller Ethernets conformingto TSN. The 5GS may utilize DETNET to enable the transport of TSN messages over IP layer 3 (rather than layer 2). Envisioning such a system, techniques discussed in the present disclosure include a 5GS that supports the DETNET edge, relay, and transit nodes interconnecting spatially separated TSN networks, to create a larger, composite TSN over 5G. All of the aspects of an integrated TSN-5G system provided in this disclosure are applicable to (but are not restricted to) the TSN islands of the 5G DETNET, and in particular the TSN blocks of disaggregated TSN.

[0084] DETNET is composed of the following components: (1) TSN End System: the IEEE conformant end system that communicates with the DETNET Edge Node; (2) DETNET Edge Nodes: process TSN frames into the DETNET; (3) DETNET Relay Nodes; and (4) DETNET Transit Nodes: provides congestion avoidance for time-sensitive messages. DETNET is routed, rather than bridged, thus enabling routable TSN messages between TSN LANs. Essential TSN Ethernet frame information is either transported or reconstructed in the transport among LANs. DETNET accomplishes its higher-layer functions by adding sublayers: (1) DETNET service sublayer: provides DETNET service (e.g., service protection), to higher layers in the protocol stack and applications; and (2) DETNET transport sub-layer: supports DETNET service in the underlying network (e.g., by providing explicit routes and congestion protection) to DETNET flows and encapsulating the TSN Ethernet frames. DETNET routing incorporates IP headers are modified per standard router behavior, e.g., time-to-live (TTL handling), where TTL specifies the maximum time the routable IP message is allowed to survive, clearly related to the TSN maximum latency requirement.

[0085] The DETNET components may reside within any computational element of the 5GS, specifically within the 5G MEC or Core. Thus, in one implementation, the 5GS may be a fully compliant DETNET that interconnects TSN LANs. In order to provide determinism, DETNET may reserve data plane resources for DETNET flows in some or all of the intermediate nodes along the path of the flow. DETNET may provide explicit routes for DETNET flows. DETNET may distribute data from DETNET flow packets over time and / or space to ensure delivery of each packet’s data in spite of the loss of a path. Thus, the TSN CNC / DNC and scheduler, as described above, may require interaction with DETNET, specifically the ability to configure flow paths (including redundant flow paths) and TTL, and acquire routing latency and jitter. TSNtraffic shapers, time-aware shaping, and network calculus may be used to achieve the required level of determinism in the 5G DETNET.

[0086] It is also noted that a hybrid 5GS, in which portions are TSN (layer 2) and portions are DETNET (layer 3), may coexist and interoperate. In such cases, the DETNET portion of the network may itself be treated as a TSN block (or blocks) 202, as discussed above.

[0087] Further with respect to caching within the 5GS to minimize latency and increase determinism for TSN applications, the amount of storage, location, and naming of information in the 5GS may be managed in such a manner to enable rapid and shorter proximity access to minimize jitter. Each piece of information may be cryptographically signed to increase its security and access provided through a hash of encrypted signature and the information stored in 5GS components such as the MEC. A cache-forwarding unit will track each data request to allow optimal forward, placement, and service of the cached data. The TSN CNC / DNC and scheduler may compute where to position cached information within the network (specifically the 5GS) to maximize access and minimize jitter (packet delay variance) among 5G applications.

[0088] Real-time MEC applications typically have well-defined characterizations of their behavior. Cloud (cloud computing) and fog (fog computing) 5G MEC applications may be partitioned into microservices with hard, real-time constraints that are provided to the TSN scheduler. The constraints may be the longest (worst-case) time to complete a call to the service or a clearly defined statistical description as per network calculus requirements for an arrival or service curve. In some implementations, microservices may be chained together to create a complete MEC application. By breaking computation down into a series of smaller microservices, each service may be better controlled and managed and able to provide more determinism. Each microservice may be abstracted as an internal TSN block, composed of deterministic input, output, schedulable operation, and coordination with the 5G-TSN AF. The microservices may reside on the same or spatially distinct processing systems interconnected via TSN-scheduled communication.

[0089] Such hard real-time processing may include the 5G MEC and 5G Core functions. Messages ingress and egress from MEC TSN blocks following a deterministic schedule that can be computed by the TSN scheduler. Note that such TSN blocks will be referred to as“ computational TSN blocks.” Given that messages generated by the MEC and their corresponding size and transmit times may vary depending upon the computational complexity of the MEC processing task, processing load, and application state, the TSN scheduling component can utilize an internal model, such model being a simulation, emulation, or purely analytical or a hybrid of each, of the MEC processor for TSN scheduling purposes (aka a digital twin). The TSN scheduling component can then generate a complete, end-to-end TSN schedule for all messages flowing among the 5G UE, MEC, and 5G Core, and any cloud processing that is required for a real-time application, where 5G Core and cloud processing are similarly modeled by the TSN scheduler. The TSN schedule can dynamically recompute schedules as required to maintain the required determinism for the 5G TSN real-time MEC / cloud application as the effective processing rate, and thus output message transmission time, varies. The TSN scheduler can provide feedback to the application developer (and for management and deployment) regarding the best location (UE, MEC, cloud) for each processing component of a real-time 5G application, considering link speeds, variation, processing capability, available memory, etc. The TSN system may employ a gate-based approach or a rate-control mechanism such as a leaky bucket and may use any number of optimization techniques or network calculus to determine feasible schedules. Thus, a complete flow for a TSN application will include not only an end-to- end path through the network for a specific message, but also the complete processing path through all computational TSN blocks (microservices) acting on the information in the message. IEEE 802.1CB redundant paths can be configured through either redundant or parallel computational TSN blocks (microservices). One should be able to visualize the complete realtime processing activity encoded in the TSN schedule in the embodiments, where computational TSN blocks appear as Ethernet bridges with the difference that they either process messages or can create new messages that egress on a deterministic schedule.

[0090] MEC applications are configured to use TSN flows (to participate as TSN Talkers or Listeners) using NETCONF, RESTCONF, or a RESTful API. Messages defined in the aforementioned protocols contain IEEE 802. IQcc information required to configure the TSN flows used by MEC applications. Additionally, an MEC application is designed to move from one MEC platform to another and a RESTful API is defined to query the current platform’s TSN configuration to verify that it has the required deterministic communication requirements,specifically including latency and jitter. It also has a RESTful API containing the aforementioned information required to notify the TSN CNC of its new location and required information to dynamically reschedule TSN traffic to its new location. As mentioned, IEEE 802.1CB may be used to establish redundant TSN flows to anticipated locations where the MEC application may migrate. The TSN scheduler, i.e., the CNC or Distributed Network Configurator (DNC), may be an MEC application offering 5G TSN scheduling-as-a-service and configuration-as-a-service, useful for dynamic rescheduling, where low latency is required.

[0091] A TSN application may transmit a timing performance profile-query microservice request with the goal of determining statistical information regarding processing time. When responding to such a request, the microservice will execute the request and return both the result and a timing performance profile. The timing performance profile may contain at least the network start and end time of the microservice call, and may optionally contain the start time and end time of all called subfunctions. The information may also contain an average of the MEC processor load during the timing performance profile. The timing performance profile may be used by the TSN application to refine TSN scheduling where microservices are required to process TSN traffic flow. The timing performance profile may be obtained during live operation, where output from the profile is used normally, or as a special test sample prior to live operation. The timing performance profile information may be used to determine which MEC hardware to employ for current and future operations and as constraint information for the TSN scheduler.

[0092] The subject disclosure also provides additional new aspects including (1) TSN- scheduled 5G core functions; (2) ensuring the central processing unit has direct time synchronization with the Ethernet hardware timestamping mechanism; (3) enabling TSN scheduling of specific 5G network function and processes; (4) adding new time-aware programming features, e.g., time-based event processing; and (5) integrating conditional processing based upon time, e g., real-time programming implemented via YANG configuration of microservice scheduling to implement service chaining (see https:z7www.rfc-example of YANG scheduled operation).

[0093] Further, a LinkDelayStatistic (implemented as a YANG model) cumulative distribution function data model may be implemented within the TSN-5G system 106 or 200. Insuch an implementation, wireless links may collect and feed information to the CNC’s scheduler that would then utilize it for scheduling variable speed links. The LinkDelayStatistic YANG model may also provide information regarding whether the link delay is stationary and ergodic. The scheduler may use this information to rely on a given sample to predict the future and create an accurate schedule. The CNC may then utilize this knowledge for each TSN block to deploy the best possible TSN traffic shaping or gate schedule, including the use of network calculus in determining the result.

[0094] The subject disclosure also envisions a virtualized network function (VNF-TSN), capable of residing as software within the 5G system. It may require specialized processor hardware to support real-time operation. In some implementations, TSN may be provided as software-defined TSN (SDN-TSN) and 5G TSN-as-a-service. In some implementations, VNF- TSN may be configured within every 5G sub-component (TSN block): UE, Radiohead, CU / DU, RAN, MEC, Core, etc. As discussed above, microservices may be chained together as part of TSN scheduling. Each microservice can expose its service time characterization to the TSN schedule. The service is part of the delay, but it may have more variance than a communication link. In such an implementation, the service is the link in this integration of microservices with TSN scheduling. Network calculus may be used to incorporate a processing delay. TSN input provides a clear arrival curve. Processor execution time provides a service curve (assuming 5G equipment has a well-characterized processing time).

[0095] In another aspect of the subject disclosure, a time-aware MEC platform is defined to be a platform that acts as a PTP client (conformant to 802. IAS End Station) and if needed a PTP bridge (conformant to 802. IAS Bridge). A PTP bridge, along with a network bridge functionality, is needed for a virtualized MEC platform that runs multiple slices (OSes, VMs, containers) in parallel. Typically, a virtual bridge / switch is used in virtualized compute platforms. In this disclosure, a TSN-enabled virtual switch is defined, which includes time awareness. The time-aware PTP client in an MEC platform runs a PTP state machine along with a servo to synchronize the locally available clock with grandmaster clock in the network. Furthermore, the MEC platform synchronizes the system clock to the PTP clock, wherein the system includes operating system, network stack, application stack or any other software and hardware elements that utilize a clock. This enables each MEC application to operate on thesynchronized PTP time. In one example, MEC host operates this PTP client and provides all MEC applications with the synchronized time via the virtualization infrastructure (say as system / host time). In another instance, every MEC application may run an individual instance of a PTP client connected to the host via a time-aware bridge.

[0096] As a configuration interface, 3GPP 23.501 specifies a centralized configuration model in which a CNC configures the 5GS as a time-aware bridge. Similarly, an MEC platform should be configurable with a CNC as a time-aware end station or a time aware bridge. This MEC may support configuration using 802. IQcw yang models for TSN features including time-aware shaping, forwarding, and frame replication and elimination for redundancy. Furthermore, the MEC host may have a CUC component that provides the data flow requirements of the resident applications to a CNC using the 802.1Qcc interface. MEC may also provide the information about its TSN capabilities to the CNC so that CNC can model the MEC accurately. For example, an MEC can present itself as a TSN end station originating a certain set of data flows. The CNC will model MEC appropriately in the network and generate the correct configurations. The MEC shall support identification of data streams in collaboration with the OS and applications. The specific functionality varies depending on the TSN awareness of MEC components. TSN unaware applications on an MEC host requires an IP stream identification in an MEC bridge.

[0097] TSN fronthaul / b ackhaul (e.g., TSN block 202) may connect directly to the MEC, providing deterministic input to the MEC. However, MEC applications are meant to continuously migrate, or perhaps more intuitively “float,” to the edge of the 5G network to remain closest to their potentially mobile clients. Thus, MEC applications that require determinism will specify a minimum acceptable packet delay variance (MPDV) threshold. An MPDV may be incorporated into the specification of all MEC applications, which may limit feasible application locations to those MEC platforms that exist as TSN blocks within the 5GS. The application must ensure that the proper TSN stream identification and translation rules are implemented on the new MEC platform (which may be a single processor or a subnetwork of processors) such that the MEC Talker / Listener message frames are properly tagged and handled. MEC applications may want to carry their own configuration instructions when possible in order to “self-install” as they migrate to new MEC platforms. It is noted that a load-balancing mechanism may be employed to ensure users do not overwhelm any set of MEC applications andthat MEC applications are distributed in an optimal manner. In other scenarios, redundant MEC platforms and applications may be instantiated and / or MEC applications migrate due to noise, rather than due strictly to mobility. Multiple MECs may also be connected as TSN redundant systems.

[0098] Figs. 6A and 6B illustrate an integrated TSN-5G system 600 (similar to the system 200) including a TSN block 605 (similar to the TSN block 202-N) representing or corresponding to an MEC module. The TSN block 605 is configured as a data source / sink (i.e., as a TSN end station) but is located within the 5G system 106 as opposed to arranged at the edge of 5G system 106. The TSN block 605 may be configured as a bridged end station.

[0099] Referring to Fig. 8, an application data stream may span multiple integrated TSN-5G systems 805 and 810. In this case, TSN blocks may be geographically distributed interconnecting more than one 5G systems using a TSN compatible transport 820, including, but not limited to, a 5G backbone, private network tunnel or other wide-area networks. In these embodiments, the TSN blocks are configured by their respective CMs 215. In some implementations, the configuration across the two TSN-5G systems 805, 810 may be coordinated through a CNC 112. In some other implementations, the CMs 215 between the two TSN-5G systems 805, 810 may communicate directly. In some implementations, multiple CUCs and CNCs may be used to capture the user requirements and generate TSN solutions and techniques provided in this disclosure (e.g., TSN Schedule, forwarding instruction, etc.).

[0100] Fig. 7 illustrates an electronic system 700 with which one or more implementations of the subject technology may be implemented. The electronic system 700 can be a part of the TSN block 202 and / or the configuration module 215. The electronic system 700 may include various types of computer-readable media and interfaces for various other types of computer-readable media. The electronic system 700 includes a bus 708, one or more processing unit(s) 712, a system memory 704 (and / or buffer), a ROM 710, a permanent storage device 702, an input device interface 714, an output device interface 706, and one or more network interfaces 716, or subsets and variations thereof.

[0101] The bus 708 collectively represents all system, peripheral, and chipset buses that communicatively connect the numerous internal devices of the electronic system 700. In one ormore implementations, the bus 708 communicatively connects the one or more processing unit(s) 712 with the ROM 710, the system memory 704, and the permanent storage device 702. From these various memory units, the one or more processing unit(s) 712 retrieves instructions to execute and data to process in order to execute the processes of the subject disclosure. The one or more processing unit(s) 712 can be a single processor or a multi-core processor in different implementations.

[0102] The ROM 710 stores static data and instructions that are needed by the one or more processing unit(s) 712 and other modules of the electronic system 700. The permanent storage device 702, on the other hand, may be a read-and-write memory device. The permanent storage device 702 may be a non-volatile memory unit that stores instructions and data even when the electronic system 700 is off. In one or more implementations, a mass-storage device (such as a magnetic or optical disk and its corresponding disk drive) may be used as the permanent storage device 702.

[0103] In one or more implementations, a removable storage device (such as a floppy disk, flash drive, and its corresponding disk drive) may be used as the permanent storage device 702. Like the permanent storage device 702, the system memory 704 may be a read-and-write memory device. However, unlike the permanent storage device 702, the system memory 704 may be a volatile read-and-write memory, such as random access memory. The system memory 704 may store any of the instructions and data that one or more processing unit(s) 712 may need at runtime. In one or more implementations, the processes of the subject disclosure are stored in the system memory 704, the permanent storage device 702, and / or the ROM 710 (which are each implemented as a non-transitory computer-readable medium). From these various memory units, the one or more processing unit(s) 712 retrieves instructions to execute and data to process in order to execute the processes of one or more implementations.

[0104] The bus 708 also connects to the input and output device interfaces 714 and 706. The input device interface 714 enables a user to communicate information and select commands to the electronic system 700. Input devices that may be used with the input device interface 714 may include, for example, alphanumeric keyboards and pointing devices (also called “cursor control devices”). The output device interface 706 may enable, for example, the display ofimages generated by electronic system 700. Output devices that may be used with the output device interface 706 may include, for example, printers and display devices, such as a liquid crystal display (LCD), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, a flexible display, a flat panel display, a solid state display, a projector, or any other device for outputting information. One or more implementations may include devices that function as both input and output devices, such as a touchscreen. In these implementations, feedback provided to the user can be any form of sensory feedback, such as visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0105] Finally, as shown in Fig. 7, the bus 708 also couples the electronic system 700 to one or more networks and / or to one or more network nodes through the one or more network interface(s) 716. In this manner, the electronic system 700 can be a part of a network of computers (such as a local area network (LAN), a wide-area network (WAN), an Intranet, or a network of networks, such as the Internet). Any or all components of the electronic system 700 can be used in conjunction with the subject disclosure.

[0106] These functions described above can be implemented in computer software, firmware or hardware. The techniques can be implemented using one or more computer program products. Programmable processors and computers can be included in or packaged as mobile devices. The processes and logic flows can be performed by one or more programmable processors and by one or more programmable logic circuitry. General and special-purpose computing devices and storage devices can be interconnected through communication networks. The functions disclosed herein may be implemented using quantum computing, pulse-coupled oscillation (PCO) / Ising computing.

[0107] Some implementations include electronic components such as microprocessors, storage and memory that store computer program instructions in a machine-readable or computer-readable medium (also referred to as computer-readable storage media, machine- readable media, or machine-readable storage media). Some examples of such computer-readable media include RAM, ROM, read-only compact discs, recordable compact discs, rewritable compact discs, read-only digital versatile discs (e.g., DVD-ROM, dual-layer DVD-ROM), avariety of recordable / rewritable DVDs (e.g., DVD-RAM, DVD-RW, DVD+RW, etc.), flash memory (e.g., SD cards, mini-SD cards, micro-SD cards, etc.), magnetic and / or solid-state hard drives, read-only and recordable Blu-Ray® discs, ultra-density optical discs, any other optical or magnetic media, and floppy disks. The computer-readable media can store a computer program that is executable by at least one processing unit and includes sets of instructions for performing various operations. Examples of computer programs or computer code include machine code, such as is produced by a compiler, and files including higher-level code that are executed by a computer, an electronic component, or a microprocessor using an interpreter.

[0108] While the above discussion primarily refers to microprocessor or multi-core processors that execute software, some implementations are performed by one or more integrated circuits, such as application-specific integrated circuits or field programmable gate arrays. In some implementations, such integrated circuits execute instructions that are stored on the circuit itself.

[0109] As used in this specification and any claims of this application, the terms “computer,” “server,” “processor,” and “memory” all refer to electronic or other technological devices. These terms exclude people or groups of people. For the purposes of the specification, the terms “display” or “displaying” mean displaying on an electronic device. As used in this specification and any claims of this application, the terms “computer-readable medium” and “computer- readable media” are entirely restricted to tangible, physical objects that store information in a form that is readable by a computer. These terms exclude any wireless signals, wired download signals, and any other ephemeral signals.

[0110] To provide for interaction with a user, implementations of the subject matter described in this specification can be implemented on a computer having a display device, e.g., a cathode ray tube or LCD monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; e.g., feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input. In addition, a computer can interact with a user bysending documents to and receiving documents from a device that is used by the user; e.g., by sending web pages to a web browser on a user’s client device in response to requests received from the web browser.

[0111] Aspects of the subject matter described in this specification can be implemented in a computing system that includes a back-end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front-end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the subject matter described in this specification, or any combination of one or more such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a LAN and a WAN, an inter-network (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks).

[0112] In accordance with aspects of the disclosure, a wireless network (e g., the 5G network 106) is provided, the wireless network being configured to support time-sensitive deterministic communication based on a TSN mechanism. The wireless network may include a plurality of discrete CN components, e.g., components 110, 118, 120, 122, 124, 126, 140, 190, etc., discussed above. Each CN component may be configured to provide a discrete functionality for data communication from a source device (e.g., 102) across the wireless network (e.g., 106) to a destination device (e.g., 104). A processor (e.g., 402) may be arranged to configure at least one (e.g., 140) of the plurality of CN components with a set of TSN parameters of the TSN mechanism such that the at least one of the plurality of CN components supports time-sensitive deterministic communication as a TSN block (e.g., 202-3) in a TSN network in accordance with the set of TSN parameters. The at least one of the plurality of CN components that is being configured with the set of TSN parameters is the 5G transport network channel 140 connecting the RAN 120 and the UPF 122. In some implementations, the processor is arranged to configure each of the plurality of CN components with a respective set of TSN parameters of the TSN mechanism such that each of the plurality of CN components supports time-sensitive deterministic communication as a corresponding TSN block in the TSN network in accordance with the respective set of TSN parameters.

[0113] The wireless network may further include a configuration module (e.g., 110 or 210) configured to determine the set of TSN parameters for the at least one of the plurality of CN components. The set of TSN parameters may include one or more of a TSN stream identification, a transmission schedule, a deadline or delay budget, a filtering configuration, and a redundancy scheme. In some implementations, the processor is configured to assign one or more CN resources for data transmission by the at least one of the plurality of CN components in accordance with the transmission schedule or the deadline or delay budget. In some implementations, the configuration module is configured within a control plane component (e.g., SMF 126) of the plurality of CN components.

[0114] In some implementations, the configuration module is configured to determine the set of TSN parameters based on one or more CN attributes assigned to the at least one of the plurality of CN components, wherein the one or more CN attributes relate to the discrete functionality provided by the at least one of the plurality of CN components.

[0115] The processor may be configured to monitor data transmission by the at least one of the plurality of CN components and report to the configuration module any change to the set of TSN parameters or the one or more CN attributes, and the configuration module is configured to update the set of TSN parameters based on the reported change.

[0116] In accordance with aspects of the disclosure, a wireless network (e.g., the 5G network 106) configured to support time-sensitive deterministic communication based on a TSN mechanism is provided. The wireless network may include a plurality of discrete CN components, e.g., components 110, 118, 120, 122, 124, 126, 140, 190, etc., discussed above. Each CN component may be configured to provide a discrete functionality for data communication from a source device (e.g., 102, 204) across the wireless network (e.g., 106) to a destination device (e.g., 104, 206). The wireless network may include a plurality of configuration modules (e.g., 210, 215) interconnected (e.g., via 230) in a certain arrangement and configured to provide a unique set of TSN parameters to each of the plurality of CN components such that each of the plurality of CN components supports time-sensitive deterministic communication as a corresponding TSN block in a TSN network in accordance with the respective unique set of TSN parameters. The plurality of configuration modules may be interconnected in a hierarchicalarrangement, a mesh arrangement, a star arrangement, a tree arrangement, or a combination thereof.

[0117] As illustrated and discussed above in relation to Fig. 2, the corresponding TSN block (e.g., 202-3) may be configured to receive the unique set of TSN parameters thereof from a configuration module (e.g., 215-b and / or 215-c) of the plurality of configuration modules assigned to the corresponding TSN block. The unique set of TSN parameters may include one or more of a TSN stream identification, a transmission schedule, a deadline or delay budget, a filtering configuration, and a redundancy scheme.

[0118] In some implementations, at least one of the plurality of configuration modules is configured as a CUC 114 and / or a CNC 112 and implemented within the SMF 126 of the control plane and / or within the TSN block 202-3 (which represents the TSN configuration of the 5G transport channel 140). In implementations in which the 5G transport channel 140 is being configured with TSN parameters in accordance with various aspects of this disclosure, the RAN 120 and the UPF 122 each may be configured to support the functionality of a TSN Talker and / or Listener as defined in IEEE P802.Qdj [xx]. The SMF 126 / CUC 114 may be responsible to convert the 5GS parameters from / to the parameters in IEEE P802. IQdj [xx]. The SMF 126 / CUC 114 may be configured to communicate with the TSN Talker and / or Listener in the RAN 120 and the UPF 122 to exchange data sets defined in IEEE P802. IQdj [xx]. In some implementations, during the Qos flow establishment or modification, the SMF 126 / CUC 114 creates the Talker Group and Listener Group per QoS Flow as defined in the Annex I.x and sent to TSN CNC 112. The TSN CNC 112 uses the Talker Group and Listener Group as input to select path(s) and calculate schedules in TSN. The TSN CNC 112 provides a Status group to the SMF 126 / CUC 114. The SMF 126 / CUC 114 provides the Status group to the TSN Talker and / or Listener in the RAN 120 and the UPF 122, respectively. After receiving the Status group, the TSN Talker and / or Listener send the data to the N3 interface according to the configuration in the Status group.

[0119] In some implementations, a processor (e.g., 402) is configured to assign one or more CN resources for data transmission by at least one of the plurality of CN components in accordance with the transmission schedule or the deadline or delay budget.

[0120] In accordance with aspects of the disclosure, a system (e.g., system 100 as illustrated and described with respect to Fig. 8) is provided. The system includes a first wireless network (e.g., 805) and a second wireless network (e.g., 810). The first wireless network may include a first plurality of discrete CN components, each CN component configured to provide a discrete functionality for data communication via the first wireless network, wherein at least one of the first plurality of CN components is configured to provide time-sensitive deterministic communication in accordance with a first set of TSN parameters. The second wireless network may include a second plurality of discrete CN components, each CN component configured to provide a discrete functionality for data communication via the second wireless network, wherein at least one of the second plurality of CN components is configured to provide timesensitive deterministic communication in accordance with a second set of TSN parameters.

[0121] In some implementations, a TSN transport channel (e.g., 820) is provided, which is configured to facilitate time-sensitive deterministic communication of data exchanged between the first wireless network and the second wireless network, in accordance with a third set of TSN parameters. The TSN transport channel may be communicatively connected with a first TSN translator (e.g., NW-TT in 122 of 805) of the first wireless network and a second TSN translator (e.g., NW-TT in 122 of 810) of the second wireless network.

[0122] In accordance with aspects of the disclosure, a method is provided that includes configuring at least one (or each) of a plurality of CN components of a wireless network (e.g., a 5G network) with a set of TSN parameters of the TSN mechanism such that the at least one (or each) of the plurality of CN components supports or provides time-sensitive deterministic communication as a TSN block in a TSN network in accordance with the set of TSN parameters. The at least one of the plurality of CN components may be the 5G transport network channel connecting the RAN and the UPF.

[0123] The method may further include determining the set of TSN parameters for the at least one of the plurality of CN components, e.g., at one or more configuration modules. The set of TSN parameters may include one or more of a TSN stream identification, a transmission schedule, a deadline or delay budget, a filtering configuration, and a redundancy scheme. The method may further include assigning one or more CN resources for data transmission by the atleast one of the plurality of CN components in accordance with the transmission schedule or the deadline or delay budget. The method may also include monitoring data transmission by the at least one of the plurality of CN components and reporting to the configuration module any change to the set of TSN parameters or the one or more CN attributes, wherein the set of TSN parameters is updated based on the reported change.

[0124] As discussed above, a wireless communication network in accordance with 3GPP- defined specifications (e.g., 5G, 6G) and an artificial intelligence / machine learning (AI / ML) system may be combined to improve detection of emerging faults in the wireless communication network, e.g., by actively probing the wireless communication network and continuously optimizing the queries that are used to probe the wireless communication network based on responses to the queries that are obtained from the wireless communication network. For example, the AI / ML system continuously learns from the query responses as the query responses are collected to determine what future queries to form and / or what parameters of the wireless communication network to explore (e.g., the AI / ML system continuously learns from feedback while querying). In some embodiments, the TSN mechanism is improved because the AI / ML system is learning to predict the impact of latency on the performance of the controller (e.g., CNC 112 in Fig. 1). In some embodiments, the AI / ML system generates and outputs one or more TSN schedules based on the information gathered from the wireless communication network. The AI / ML system may be referred to as an AI / ML application, ML application, or Al application. In order to generate a TSN schedule, the system needs the network topology, link latencies, application message sizes, Talkers and Listeners, and application flow maximum latency requirements between Talkers and Listeners. The problem can be abstracted as a jobshop scheduling problem with associated calculations depending upon the approach to find a feasible solution. All the raw information would be rather blindly collected and processed, assuming the network has no insight into an application which is a control application with an associated control law that must account for sensing and communication delays. But this can be a large amount of information. In some implementations, rather than sending all the information, the input information is learned as needed by the AI / ML system. For example, in a complex network topology, the AI / ML system can actively probe by querying portions of the network and infer the rest of the network topology based upon monitoring only a subset of all network links.Portions of the network that are irrelevant to the TSN flows need not be discovered at all, saving even more query-response data. In some implementations, application flow maximum latency values are not fixed a priori, but rather allow for a range of values over which the AI / ML system is able to perturb the control application using active probing, and determine potential tradeoffs in flow latency requirements, thus inferring minimal flow requirements while minimizing the number of queries to infer the system response to changes in latency.

[0125] In some implementations, the present disclosure provides a system associated with a communication network configured to support time-sensitive deterministic communication based on a TSN mechanism. The system comprises a processor configured to optimize an input query to retrieve status information related to data communication over the communication network based on an artificial intelligence (Al) model, the communication network including a plurality of discrete communication network (CN) components, each CN component configured to provide a discrete functionality for the data communication from a source device across the communication network to a destination device; retrieve the status information using the optimized query; and generate a network schedule based on the status information such that at least one of the plurality of CN components supports time-sensitive deterministic communication as a network block based on the network schedule.

[0126] In various embodiments, the AI / ML system for communication network troubleshooting, monitoring, fault detection, planning, automation of network operations (e.g., provisioning, optimization, fault prediction, security, fraud detection), network slicing, reducing operating costs, time synchronization and improving both the quality of service and customer experience may be based on chatbots, recommender systems, and techniques such as robotic process automation (RPA). The AI / ML system can also be used for optimizing use of system resources, autoscaling, anomaly detection, predictive analytics, prescriptive policies, and so on. In addition to the above, the AI / ML system can be used for customer experience management and business support systems to support a multitude of emerging applications (e.g., AR / VR, industrial loT, autonomous vehicles, drones, Industry 4.0 initiatives, Smart Cities, Smart Ports).

[0127] As discussed above, the communication network may be a wireless communication network which is defined in accordance with 3GPP standard (e.g., 5G, 6G). In someimplementations, the communication network is integrated with a TSN system and associated with a TSN-wireless communication system such as the 5G-TSN system 100, the system 200 as described above. The AI / ML system may actively probe the wireless communication network to detect existing and / or emerging faults in the wireless communication network. In some embodiments, the AI / ML system learns to efficiently query the 5G system by reducing overhead traffic while increasing the amount of relevant information obtained per query or “question.” For example, rather than continuously requesting contents of all the network management information an 5G system may have, the AI / ML system learns and optimizes the queries to request the smallest possible amount of such information. In some embodiments, the AI / ML system learns to obtain information from the 5G system that is “orthogonal,” non-redundant, and pertinent. Based on the query responses, the AI / ML system can improve TSN scheduling by generating and providing a TSN schedule (or feasible range of schedules) to the controller, e.g., CNC 112 in Fig. 1).

[0128] A framework for AI / ML application is defined by 3GPP TS 22, 23 and 28 specifications such as 3GPP TS 22.261 (2022-12, Release 19), 3GPP TS 23.288 (2022-12, Release 18), 3GPP TR 22.874 (2021-12, Release 18), 3GPP TR 22.876 (2022-12 and 2023-03, Release 19), 3GPP TR 23.700-18 (2022-12, Release 18), 3GPP TR 23.700-36 (2022-12, Release 18), 3GPP TR 23.700-80(2022-12, Release 18), 3GPP TS 28.104 (2022-12, Release 17), 3GPP TS 28.105 (2022-12, Release 17), 3GPP TR 28.838 (2023-01, Release 18), 3GPP TR 28.908 (2022-12 and 2023-03, Release 18) which are incorporated herein by reference in their entirety.

[0129] Fig. 9 illustrates a block diagram of an example system for the active probing of a wireless communication system, in accordance with one or more implementations.

[0130] Fig. 9 illustrates a non-limiting example of a system 9000. The system 9000 is similar to the systems 100, 200, and 600, which are associated with a wireless communication network (communication network) in accordance with a 3GPP-defined specification (e.g., 5G, 6G) to support time-sensitive deterministic communication based on a TSN mechanism. In some implementations, the communication network includes a plurality of discrete communication network (CN) components e.g., components 110, 118, 120, 122, 124, 126, 140, 190 as discussedabove. Each CN component may be configured to provide a discrete functionality for the data communication from a source device across the communication network to a destination device.

[0131] The system 9000 includes a 5G system 9006 which is similar to the 5G system 106. The 5G system 9006 is a wireless communication network or system used to carry TSN traffic between various TSN end devices, e.g., end station(s) 9002 (e.g., I / O devices 102) and a configuration controller 9020 (e.g., a centralized configuration controller 110 in Fig. 1), including Centralized User Configuration (CUC) 9014 (e.g., CUC 1 14 in Fig. 1) and Centralized Network Configuration (CNC) 9012 (e.g., CNC 112 in Fig. 1). In some embodiments, the system 9000 is actively probed using AI / ML system. Thus, the system 9000 may be referred to as AI / ML-based system. The AI / ML system is implemented in a distributed manner such that AI / ML models optionally resides in both the 5G system 9006 and configuration controller 9020. For example, an AI / ML model 9010 resides in 5G core such as 5G network data analytics function (NWDAF) 9008 and another AI / ML model 9016 resides in CNC 9012 of the configuration controller 9020. In some embodiments, NWDAF 9008 includes wide range of techniques and processes used to analyze and derive insights from data generated by the 5G system 9006. NWDAF 9008 collects data from user equipment, network functions, operations, administration, and maintenance (0AM) systems within the 5G Core, Cloud, and Edge networks.

[0132] NWDAF 9008 includes a number of functions that are used for optimizing network performance, ensuring security, and making informed decisions about network operations. In some embodiments, NWDAF 9008 is a 3GPP standard defined by a number of 3GPP TS specifications such as 3GPP TS 23.501, 3GPP TS 23.502, 3GPP TS 23.503, 3GPP TS 23.288, which are incorporated herein by reference. Examples of common network data analytics functions provided by NWDAF 9008 include, but are not limited to, traffic analysis (e.g., analyzing network traffic to evaluate its volume, patterns, and characteristics., thereby identifying trends, anomalies, and areas where network capacity may need adjustment) performance monitoring (e.g., continuously monitoring network performance metrics, such as latency, packet loss, and bandwidth utilization to evaluate the network performance), anomaly detection (e.g., identifying abnormal network behavior or security threats by comparing real-time traffic patterns to historical data or predefined baselines), root cause analysis (e.g., investigating network issues by tracing back to their origins to identify the underlying causes., therebyresolving network problems and preventing recurring issues), quality of service (QoS) analysis (e.g., monitoring and optimizing QoS parameters to ensure that critical applications receive the required network resources and that service-level agreements (SLAs) are met), capacity planning (e g., predicting future network resource needs based on historical data and trends to ensure that the network can handle increasing demands without degradation in performance), user behavior analysis (e.g., analyzing user activities and behaviors on the network to detect unusual or unauthorized actions, which can be indicative of security breaches or policy violations), security analytics (e.g., examining network traffic for signs of malicious activity, intrusion attempts, malware, or other security threats, thereby improving early threat detection and incident response), flow analysis (e.g., analyzing network flows, which represent sequences of packets that share common attributes, to gain insights into application usage, device interactions, and network bottlenecks), protocol analysis (e.g., examining network traffic at the protocol level to understand how different applications and protocols are utilized on the network), packet capture and inspection (e.g., capturing and analyzing individual packets to diagnose complex network issues, troubleshoot problems, or investigate security incidents in detail), visualization (e.g., presenting network data in graphical or visual formats to make it more accessible and understandable), historical trend analysis (e.g., analyzing historical network data to identify longterm trends, plan for network upgrades, and make strategic decisions about network infrastructure), compliance and reporting (e.g., generating reports and compliance documentation to demonstrate adherence to industry regulations and internal policies), and / or predictive analytics (e.g., using machine learning and predictive modeling to forecast network performance, detect impending failures, and proactively take corrective actions).

[0133] In some implementations, the AI / ML-based system (e.g., 9000) actively probes (or queries) the 5G system 9006. In some embodiments, active probing refers to a network troubleshooting and monitoring technique in which a device or software actively sends test packets or queries to other devices or network elements to gather information about the network’s status, performance, or potential issues. Active probing is a proactive approach to network monitoring and troubleshooting, where devices or tools actively generate test traffic to assess network health, diagnose issues, and measure performance. This technique is commonly used for network diagnostics, network performance testing, and fault detection. In someembodiments, a network node or a monitoring tool, such as NWDAF 9008, initiates tests or probes by sending specific packets or requests to target devices or network endpoints (e.g., end stations(s) 9002). These probes are designed to elicit responses that contain information about the 5G system or network’s health and performance. In some embodiments, active probing includes sending ping packets to remote hosts to check if remote hosts are reachable and to measure round-trip latency. In some embodiments, active probing includes a traceroute technique that identifies the path taken by packets to reach a destination. The traceroute technique helps diagnose routing issues and shows the network hops along the way. In some embodiments, active probing includes sending test traffic with known characteristics (e.g., bandwidth, packet loss) to measure network performance, identify bottlenecks, and assess Quality of Service (QoS). In some embodiments, active probing is used to detect network faults, such as link failures, high packet loss, or excessive latency, by analyzing the responses or lack thereof from target devices (e.g., the AI / ML model 9010 and / or AI / ML model 9016 analyzes responses or the lack thereof from the 5G system 9006). In some embodiments, active probing can be scheduled to run tests at regular intervals or initiated on-demand when network issues are suspected. In some embodiments, different types of active probing may use specific protocols or packet types. In some embodiments, the success or failure of active probes is determined by analyzing the responses received. If responses conform to expected behavior, the network is considered healthy. Deviations from expected responses may indicate issues that require further investigation. In some embodiments, active probing can be automated using network monitoring tools and scripts. These tools can perform a wide range of tests and provide reports or alerts based on the results.

[0134] In some embodiments, the AI / ML model 9010 actively probes the 5G system 9006 and learns from one or more query responses obtained from the 5G system 9006 and optimizes subsequent queries or probes that are sent to the 5G system 9006. In some embodiments, the AI / ML model 9010 communicates with a counterpart AI / ML model 9016 via application programming interface (API) 9022. In some implementations, the AI / ML model 9010 in the 5G may be referred to as a core model and the counterpart AI / ML model 9016 in CNC 9012 may be referred to as a controller model. The core model can be oriented toward large scale performance to coordinate many sessions from many users through the 5GS. The controllermodel, on the other hand, can be oriented toward a targeted application with the goal of being lightweight and fast. The two models 9010, 9016 can also be under two different types of owners. For example, the core model may be the network operator while the controller model may be the industrial client of the network and perhaps also be concerned with containing the release of any sensitive proprietary information in the process. The controller model may have knowledge of the entire industrial system network of which the 5GS is only a portion. In some implementations, the core model may actively probe the effect of perturbations to client flow Quality-of-Service (QoS) through the 5GS by mapping the client’s flows to different 5QI QoS types and examining the effect on the client’s satisfaction. The core model may achieve maximum satisfaction of all clients’ flows while the client’s controller may explore traffic configuration changes throughout the client’s industrial network, only a portion of which may flow through the 5GS. The information exchanged would be in the form of latency histograms for each configured TSN flow. The controller model would determine via active probing how the histogram changes and thus infer resource allocation strategies for feasible performance of the system. Similarly, the controller model will be reporting satisfaction with the core model active probing changes, as discussed above. Both sides need to coordinate their active probing activities in order to avoid overlapped probing results. Thus, a probing time window should be announced during which the model’s counterpart will refrain from probing. In some embodiments, redundancy in the obtained query responses is reduced (e.g., by AI / ML model 9010) by selecting a set of query responses from the obtained query responses that are orthogonal. In some embodiments, the selected set of query responses (e.g., with reduced redundancy) are sent back to the configuration controller 9020 (e.g., AI / ML model 9016), as opposed to all obtained query responses, thereby reducing the overhead in the network traffic. In some embodiments, instructions can be sent to AI / ML model 9010 via API 9022 to discover correlation between parameters.

[0135] In some embodiments, an AI / ML model 9010 may represent an AI / ML-based logic or algorithm used for inference, e.g., for producing analytics output results based on “real-time” “live” data input. The AI / ML model may be software and may be located anywhere within or outside of the 5G system. In some embodiments, different model / algorithms may be available for usage as the AI / ML model. In that case, multiple AI / ML models / algorithms may be trainedsimultaneously to find the best performing model based on one or more of the model performance metrics including: e.g., time to learn, time to react to apply compensation, ability to avoid over-training (sufficient breadth of learning), as well as overhead (how much input data is required to learn).

[0136] Fig. 10 illustrates an example of operations of an AI / ML-based active probing of a system, in accordance with one or more implementations.

[0137] Fig. 10 illustrates operations by the system (e.g., system 9000) using AI / ML model to optimize query. In some implementations, the system includes a processor configured to operations depicted in Fig. 10. In some embodiments, the system provides input 10002 to AI / ML model 10010. In some embodiments, AI / ML model 10010 corresponds to AI / ML model 9010, AI / ML model 9012 or both. In some embodiments, AI / ML model 10010 is a neural network. In some implementations, the Al components of AI / ML model 10010 are implemented as data models to collect training data and be trained based on the training data. In some implementations, the collected training data and output result may be provided to other Al components which are configured to perform subsequent operations, processes and / or functions. Also, the training data and output result from Al components may be provided to the AI / ML application to control and improve performance of the AI / ML application. In some implementations, the AI / ML model 10010 may include a generative adversarial network (GAN), which has two parts. The generator learns to generate plausible data. The generated instances become negative training examples for the discriminator. The discriminator learns to distinguish the generator's fake data from real data. In this case, the AI / ML model 10010 learns how perturbations will impact the 3 GPP system and uses that to form the smallest possible network management queries of the system.

[0138] As discussed above, input 10002 is provided to AI / ML model 10010. In some embodiments, input 10002 includes a model (or model abstraction )10004 of a target system (or communication network), such as 5G system 10020, and an input query 10006. In some implementations, the input 10002 is obtained from the 5G system 10020. In some implementations, the same input query can be sent over to different nodes in the network.

[0139] In some embodiments, model 10004 of the target system is a network management data model that includes structured collections of data and objects that define managed elements of the target communication network (e.g., 5G system 10020). In some embodiments, model 10004 of the target system defines the structure and semantics of data exchanged between network devices and management systems of the 5G system 10020. The model 10004 of the target system is an abstraction of 5G system 10020 that is being queried. In some embodiments, model 10004 of the target system is a textual abstraction of 5G system 10020. In some embodiments, various network management data models can be used as input to AI / ML model 10008 including, but not limited to, SNMP MIBs (Simple Network Management Protocol Management Information Bases), a YANG (Yet Another Next Generation) data model (seegpp~5 gc-nnn-nwdaffuiicti on.y ang, for an example of YANG model for NWDAF) , orother data model. In some embodiment, the model 10004 is referred to as configuration data of the communication network.

[0140] In some implementations, the input query 10006 is to retrieve status information related to data communication over the communication network. In some implementations, the status information may include , but is not limited to, latency in the data communication over the communication network, traffic, the AI / ML total data exchange over all time, a capacity allowing for the same amount of data over a longer time period. In some implementations, the status information may be included in a query response. In some implementations, the input query 10006 may be used by 5G core functions (e.g., NWDAF 9008). In some embodiments, the input query 10006 is expressed using a respective protocol based on the data modeling protocol or language that is used to represent model 10004 of the target system. For example, when YANG data modeling is used to represent model 10004 of the target system, the input query 10006 is typically expressed using protocol Network Configuration Protocol (e.g., NETCONF). In some embodiments, 3GPP TS 23.501 is a specification that defines the Network Data Analytics Function (NWDAF) that is incorporated herein.

[0141] In some embodiments, the AI / ML model 10010 generates an output 10008 that includes an intermediate query 10012. This intermediate query 10012 is referred to as a first optimized query. The AI / ML model 10010 generates intermediate query 10012 based on the inputquery 10006 and model 10004 of the target 5G system 10020 (or configuration data of the communication network). In some implementations, the intermediate query 10012 is generated by reducing the number of nodes to which the input query 10006 is sent. In some embodiments, the intermediate query 10012 may be formed in XML YANG format. In some embodiments, input query 10006 and / or intermediate query 10012 can be numerical queries and / or semantic (e.g., logical) queries. For example, the numerical query may include a Simple Network Management Protocol (SNMP) object id, in which all information is described in Management Information Base (MIB) structure and the query contains only a string of numbers that uniquely identifies the desired information to be returned. A specific example is .1.3.6.1.2.1.7.1, the total number of UDP datagrams delivered to UDP users. The same query can be sent to all nodes. However, the goal of active probing is to reduce the number of nodes that must be queried. In some implementations, the intermediate query 10012 is sent to 5G system 10020, by AI / ML model 10010, and the 5G system 10020 responds to the intermediate query 10012 and send one or more query responses 10022. In some implementations, the intermediate query 10012 may be used by Network Configuration Protocol (NETCONF) in 5G system 10020.

[0142] In some embodiments, the AI / ML model 10010 is trained based on the query responses 10022 from the target 5G system 10020. In some implementations, the AI / ML model 10010 waits for a query response from each node before proceeding to send another input query to another node. In some embodiments, the AI / ML model 10010 is trained based on the model 10004 of the target 5G system 10020. Model 10004 of the target 5G system 10020, such as a YANG model, is a description of the structure and relationship of entities that form the 5G system 10020, whereas instance data represents data that has been retrieved from a specific entity of the 5G system 10020. Accordingly, the AI / ML model 10010 maybe trained on both a YANG model and its instance data. For example, when the target 5G system 10020 experiences information loss, a query that determines packet loss in the 5G system 10020 may be based on relevant YANG variables in the model that are related to determining loss in the network (e.g., based on descriptions from the relevant YANG modules as opposed to data directly from the entity that was modeled). Further, the YANG model can be used to query the entity being modeled (e.g., a network device or control system in target 5G system 10020) and obtain respective values of the relevant YANG variables in response. In some embodiments, the respective instance data may reside in a local datastore. In some embodiments, the AI / ML model10010 is trained on both the YANG model and its instance data (e.g., value of the response data in the datastore), thereby learning from both what variable to query based on the model 10004 of the target system and respective results from the query.

[0143] In some embodiments, AI / ML model 10010 continuously learns (e.g., from obtained responses from the 5G system 10020) to focus upon only what it needs to learn and to query the system using a minimal amount of overhead. In other words, it learns to increase the amount of meaning information learned per “question” or query. As a simple example, rather than continuously requesting the entire contents of a system’s all network management information, the AI / ML model 10010 learns to infer what is required by requesting a smaller amount of such information. In some embodiments, AI / ML model 10010 learns to obtain information that is “orthogonal,” non-redundant, and pertinent to the probe or query.

[0144] In some embodiments, the system processes the obtained query responses 10022 from the target 5G system 10020 and analyze them for both size (e.g., bits of information) and the degree to which the responses match or satisfy the query. For example, at 10026, the obtained query responses 10022 are processed, by the AI / ML model 10010, based on both qualitative metric and quantitative metric. In some embodiments, the AI / ML model 10010 is updated based on the results from the analysis, and a new query (e.g., the optimized query) is formed and the process is optionally repeated. In some embodiments, the obtained query responses 10022 are evaluated for information redundancy. The information redundancy can be obtained or achieved by orthogonality measurement. For example, the orthogonality measurement is applied to all responses to the same query of the same type. In some embodiments, the obtained query responses 10022 are evaluated for orthogonality with other responses to previous or other queries. Mathematically, “orthogonality” is a generalization of the geometric notion of perpendicularity meaning that there is no overlap (information redundancy) in the returned information. This implies that only “new” information in its most concise (shortest and lowest overhead) form is returned. When the intermediate query 10012 and / or the obtained query responses 10022 are numerical, orthogonality in the obtained query responses 10022 can be determined mathematically. For example, responses may be represented as vectors and the dot product of any two vectors may be calculated to determine if it is equal to zero and is thus orthogonal. In some embodiments, for queries that are semantic or logical, a technique is appliedthat matches the queries and obtained query responses 10022 to a mathematical representation where orthogonality may be applied. In some embodiments, Latent Semantic Analysis (LSA) technique (or variation thereof) is applied, where “terms” (represented as rows) in a constructed “term-document matrix” correspond to queries, and “documents” (represented as columns) in the “term-document matrix” represent query responses (e.g., sentences). An example of a “termdocument matrix” is described below with reference to Fig. 11.

[0145] In some implementations, the system optimizes the intermediate query 10012 based on the qualitative metric and the quantitative metric (e.g., 10026) and outputs the optimized query 10028. The optimized query 10028 is formed by removing redundancy from the intermediate query 10012 based on the processed query responses. In some implementations, the optimized query 10028 has a smaller size than the intermediate query 10012 or input query 10006. In some implementations, the system retrieves the status information from the communication network based on the optimized query 10028 and generates a network schedule (e.g., TSN schedule) based on the retrieved status information such that at least one of the plurality of CN components supports time-sensitive deterministic communication as a network block (e.g., TSN block) based on the network schedule. In some implementations, the system may retrieve the status information from the query responses. In some implementations, the system sends the status information to the TSN controller 110 to generate the network schedule. In some implementations, the optimized query 10028 is provided to the AI / ML model 10010 as a new input query, thus, the AI / ML model 10010 is trained based on the optimized query 10028 to repeat the operations discussed above. Further, the AI / ML model 10010 is learning about the target system by perturbing it, not just querying it. For example, the system (or the AI / ML model 10010) may inject traffic, reduce QoS in some manner, or otherwise change the target system to learn how it reacts. This operation may be referred to as a perturbation operation. The system (or AI / ML model 10010) may perform one or more perturbation operations between the operation to output the intermediate query 100012 and the operation to output the optimized query 10028, but is not limited to this. In some implementations, reaction information on how the target system reacts in response to the perturbation operation may be included in, but is not limited to, the one or more query responses.

[0146] Fig. 11 illustrates a “term-document matrix” A that is used to represent queries and obtained responses, according to some embodiments. For example, generic textual terms are used to represent an AI / ML query (e.g., query 10006 or query 10012 in Fig. 10) over the 5G system 10020. The term-document matrix A is generally sparse and large. The computation on such a large matrix is expansive and much of the values in the matrix are zero. In some embodiments, Singular Value Decomposition (SVD) is used to reduce the computational complexity (including reducing overhead traffic in the 5G system) and to obtain more relevant and useful results from the target 5G system. SVD decomposes the term-document matrix A into three different matrixes: (1) an orthogonal column matrix (e.g., matrix U that corresponds to the left singular vectors matrix, which captures the relationships between terms); (2) an orthogonal row matrix (e.g., S (Sigma) matrix that corresponds to the singular values matrix, which represents the importance of each dimension in the latent space); and (3) one singular matrix (e.g., VTthat corresponds to the right singular vectors matrix, which captures the relationships between documents), as illustrated in Fig. 11. An advantage of SVD is that the size of the termdocument matrix A can be reduced substantially. One of the insights of LSA is that the singular values in S can be used to rank the dimensions by importance. By retaining only the top k singular values and their associated vectors from U and VT, LSA reduces the dimensionality of the original term-document matrix A. This reduction in dimensionality helps uncover the most significant semantic relationships while reducing noise. In Fig. 11, K is the rank of the termdocument matrix A. If only K columns and rows are used, an approximation of the termdocument matrix A can be computed without a significant loss. During SVD calculations, a cooccurrence matrix (e.g., represented by A * (AT)) is calculated. The value with index (i,j) in a co-occurrence matrix represents the number of times term(i) and term(j) exist together in a dataset of documents. In some embodiments, the results are the most important terms in each sentence. In some embodiments, in the context of querying the 5G system, the results in the cooccurrence matrix are the most useful or relevant responses for a respective query. In some embodiments, latent semantic analysis is applied to guide learning of the AI / ML model 10010, and the AI / ML model 10010 leams using the results of the SVD. For example, the SVD helps in determining relationships and finding common elements and patterns in the data. The AI / ML model 10010 can learn that when things occur at the same time, then there is no need to query both of them, only one of them needs to be queried and the others will follow the pattern. Insome embodiments, by applying SVD, hidden relationships in the obtained query responses are uncovered. Further, the AI / ML model 10010 learns to minimize the sum of the size of the query and the responses from the query.

[0147] Fig. 12 illustrates a flow diagram of an example method for probing a wireless communication system using an AI / ML model, in accordance with one or more implementations.

[0148] The flow diagram 12000 describes operations for active probing of a communication network by a system (e.g., 100, 900, 9006) that is in communication with the communication network configured to support time-sensitive deterministic communication and communication network management. For example, the time-sensitive deterministic communication is based on a time-sensitive network (TSN) mechanism.

[0149] The operations include operation 12020 to optimize an input query (e.g., input query 10006) to retrieve status information related to data communication over the communication network based on an artificial intelligence (Al) model. As discussed above, in some implementations, the communication network includes a plurality of discrete communication network (CN) components, e.g., components 110, 118, 120, 122, 124, 126, 140, 190, etc., as discussed above. Each CN component may be configured to provide a discrete functionality for the data communication from a source device across the communication network to a destination device. In some implementations, as discussed above, the operation 12020 further comprises operation 12022 to receive or obtain input data 10002, including configuration data of the communication network (model 10004) and the input query (e.g., input query 10006). The operation 12020 comprises operation 12024 to provide the configuration data and the input query to the Al model to generate an intermediate query (e.g., 10012). The operation 12020 comprises operation 12026 to obtain one or more query responses from the communication network based on the intermediate query. The operation 12020 comprises operation 12028 to optimize the intermediate query by removing redundant data based on the one or more query responses and outputting the optimized query. In some implementations, the operation 12020 further comprises determining a qualitative metric of the intermediate query by evaluating the one or more query responses for information redundancy. As discussed above, the information redundancy may be achieved or obtained by orthogonality measurement. In some implementations, the operation12020 further comprises reducing redundancy in the one or more query responses by selecting a set of query responses from the one or more query responses that are orthogonal (e.g., using SVD as described above with references to Figs. 10 and 11). In some embodiments, the Al model learns from the selected set of responses (e.g., the orthogonal responses). In some embodiments, the selected set of query responses (e.g., with reduced redundancy) are sent back to the controller (e.g., CNC 9012 in Fig. 9).

[0150] In some implementations, the communication network includes a wireless network configured in accordance with a 3 GPP-defined standard.

[0151] The operations include operation 12040 to retrieve the status information using the optimized query. In some implementations, the status information may be included in query responses sent in response to the optimized query. The operations include operation 12060 to generate a network schedule based on the status information such that at least one of the plurality of CN components supports time-sensitive deterministic communication as a network block based on the network schedule.

[0152] Fig. 13 illustrates a flow diagram of an example method for probing a wireless communication system using an AI / ML model, in accordance with one or more implementations. The flow diagram 13000 describes sub-operation of the operations as discussed above with respect to Fig. 12. As discussed above, the system not only queries the targe system (e.g., communication network), but also perturbs the target system such that an AI / ML model (e.g., 10010) is learning about the target system. For example, the system (or the AI / ML model 10010) may inject traffic, reduce QoS in some manner, or otherwise change the target system and the Al / ML model may learn reaction information (or response) from the target system. Such operation may be referred to as perturbation operation. The perturbation operation may include any operation to change the target system. Thus, as described in Fig. 13, the sub-operation comprises the operation 12024 and the operation 12026 as discussed above. The sub-operation further comprises operation 13020 to perform the perturbation operation such that the Al model learns reaction of the communication network in response to the perturbation operation. In some implementations, the reaction (information) may be included in, but is not limited to, the one or more query responses.

[0153] In some implementations, a system associated with a communication network configured to support time-sensitive deterministic communication, comprises: a processor configured to receive configuration data of the communication network and an input query for retrieving status information related to data communication over the communication network, the communication network including a plurality of communication network (CN) components, each CN component configured to provide a discrete functionality for the data communication from a source device across the communication network to a destination device, input the configuration data and the input query to an Artificial Intelligence (Al) model to generate an intermediate query, obtain one or more query responses from the communication network based on the intermediate query, and optimize the intermediate query by removing redundant data based on the one or more query responses to output an optimized query.

[0154] In some implementations, the processor is configured to retrieve the status information using the optimized query and generate a network schedule based on the status information such that at least one of the plurality of CN components supports the time-sensitive deterministic communication as a network block based on the network schedule.

[0155] In some implementations, a system associated with a communication network configured to support time-sensitive deterministic communication comprises: a processor configured to optimize an input query to generate an optimized query for retrieving status information related to data communication over the communication network based on an Artificial Intelligence (Al) model, the communication network including a plurality of communication network (CN) components, each CN component configured to provide a discrete functionality for the data communication from a source device across the communication network to a destination device, retrieve the status information using the optimized query, and generate a network schedule based on the status information such that at least one of the plurality of CN components supports the time-sensitive deterministic communication as a network block based on the network schedule.

[0156] In some implementations, the processor is configured to: receive configuration information of the communication network (e g., Yang model abstraction of the communication system, abstraction data of the model 10004) and the input query (input query 10006), input theconfiguration data and the input query to the Al model to generate an intermediate query (e g., the first optimized query 10012), obtain one or more query responses from the communication network based on the intermediate query and optimize the intermediate query by removing redundant data based on the one or more query responses to output the optimized query.

[0157] In some implementations, the processor is further configured to: determine a qualitative metric of the intermediate query by evaluating the one or more query responses for information redundancy. In some implementations, the processor is further configured to determine a quantitative metric of the intermediate query by comparing a size of the one or more query responses from the communication network relative to a respective amount of relevant information included in the one or more query responses. In some implementations, the processor is further configured to: generate the optimized query based on the qualitative metric and the quantitative metric of the intermediate query. In some implementations, the processor is further configured to: reduce redundancy in the one or more query responses by selecting a set of query responses that are orthogonal to each other from the one or more query responses. In some implementations, the communication network includes a wireless network configured in accordance with a 3GPP-defmed standard. In some implementations, the processor is configured to perform an perturbation operation such that the Al model learns reaction of the communication network in response to the perturbation operation.

[0158] In some implementations, a method for active probing a communication network configured to support time-sensitive deterministic communication, comprises: obtaining configuration data of the communication network and an input query for retrieving status information related to data communication over the communication network, the communication network including a plurality of communication network (CN) components, each CN component configured to provide a discrete functionality for the data communication from a source device across the communication network to a destination device; inputting the configuration data and the input query to an Artificial Intelligence (Al) model to generate an intermediate query; obtaining one or more query responses from the communication network based on the intermediate query; and optimizing the intermediate query by removing redundant data based on the one or more query response to output an optimized query. In some implementations, the method further comprises retrieving the status information using the optimized query andgenerating a network schedule based on the status information such that at least one of the plurality of CN components supports the time-sensitive deterministic communication as a network block based on the network schedule.

[0159] In some implementations, a method for active probing a communication network configured to support time-sensitive deterministic communication comprises: optimizing an input query to generate an optimized query for retrieving status information related to data communication over the communication network based on an Artificial Intelligence (Al) model, the communication network including a plurality of communication network (CN) components, each CN component configured to provide a discrete functionality for the data communication from a source device across the communication network to a destination device, retrieving the status information using the optimized query, and generating a network schedule based on the status information such that at least one of the plurality of CN components supports the timesensitive deterministic communication as a network block based on the network schedule.

[0160] In some implementations, the method comprises obtaining configuration data of the communication network and the input query, inputting the configuration data and the input query to the Al model to generate an intermediate query, obtaining one or more query responses from the communication network based on the intermediate query, and optimizing the intermediate query by removing redundant data based on the one or more query responses to output the optimized query. In some implementations, the method further comprises determining a qualitative metric of the intermediate query by evaluating the one or more query responses for information redundancy. In some implementations, the method further comprises determining a quantitative metric of the intermediate query by comparing a size of the one or more query responses from the communication network relative to a respective amount of relevant information included in the one or more query responses. In some implementations, the method further comprises generating the optimized query based on the qualitative metric and the quantitative metric of the intermediate query. In some implementations, the method comprises reducing redundancy in the one or more query responses by selecting a set of query responses that are orthogonal to each other from the one or more query responses. In some implementations, the communication network includes a wireless network configured in accordance with a 3GPP-defined standard. In some implementations, the method comprisesperforming an perturbation operation such that the Al model learns reaction of the communication network in response to the perturbation operation.

[0161] In some implementations, a non-transitory computer readable medium storage is configured to one or more instructions which, when executed by a processor, causes the processor to perform operations comprising: obtaining configuration data of a communication network and an input query for retrieving status information related to data communication over the communication network, the communication network including a plurality of communication network (CN) components, each CN component configured to provide a discrete functionality for the data communication from a source device across the communication network to a destination device; inputting the configuration data and the input query to an Artificial Intelligence (Al) model to generate an intermediate query; obtaining one or more query responses from the communication network based on the intermediate query; and optimizing the intermediate query by removing redundant data based on the one or more query response to output an optimized query.

[0162] In some implementations, the operations comprise: retrieving the status information using the optimized query and generating a network schedule based on the status information such that at least one of the plurality of CN components supports time-sensitive deterministic communication as a network block based on the network schedule. In some implementations, the operations comprise: determining a qualitative metric of the intermediate query by evaluating the one or more query responses for information redundancy. In some implementations, the operations comprise: determining a quantitative metric of the intermediate query by comparing a size of the one or more query responses from the communication network relative to a respective amount of relevant information included in the one or more query responses. In some implementations, the operations comprise: generating the optimized query based on the qualitative metric and the quantitative metric of the intermediate query. In some implementations, the operations comprise: reducing redundancy in the one or more query responses by selecting a set of query responses that are orthogonal to each other from the one or more query responses. In some implementations, the communication network includes a wireless network configured in accordance with a 3 GPP-defined standard. In some implementations, theoperations comprise performing an perturbation operation such that the Al model learns reaction of the communication network in response to the perturbation operation.

[0163] Those of skill in the art would appreciate that the various illustrative blocks, modules, elements, components, methods, and algorithms described herein may be implemented as electronic hardware, computer software, or combinations of both. To illustrate this interchangeability of hardware and software, various illustrative blocks, modules, elements, components, methods, and algorithms have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. The described functionality may be implemented in varying ways for each particular application. Various components and blocks may be arranged differently (e.g., arranged in a different order, or partitioned in a different way) all without departing from the scope of the subject technology.

[0164] It is understood that the specific order or hierarchy of steps in the processes disclosed is an illustration of example approaches. Based upon design preferences, it is understood that the specific order or hierarchy of steps in the processes may be rearranged. Some of the steps may be performed simultaneously. The accompanying method claims present elements of the various steps in a sample order, and are not meant to be limited to the specific order or hierarchy presented.

[0165] The previous description is provided to enable any person skilled in the art to practice the various aspects described herein. The previous description provides various examples of the subject technology, and the subject technology is not limited to these examples. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects. Thus, the claims are not intended to be limited to the aspects shown herein, but is to be accorded the full scope consistent with the language claims, wherein reference to an element in the singular is not intended to mean “one and only one” unless specifically so stated, but rather “one or more.” Unless specifically stated otherwise, the term “some” refers to one or more. Pronouns in the masculine (e.g., his) include the feminine and neuter gender (e.g., her and its) and vice versa. Headings and subheadings, if any, are used for convenience only and do not limit the disclosure described herein.

[0166] The predicate words “configured to”, “operable to”, and “programmed to” do not imply any particular tangible or intangible modification of a subject, but, rather, are intended to be used interchangeably. For example, a processor configured to monitor and control an operation, or a component may also mean the processor being programmed to monitor and control the operation or the processor being operable to monitor and control the operation. Likewise, a processor configured to execute code can be construed as a processor programmed to execute code or operable to execute code.

[0167] The term automatic, as used herein, may include performance by a computer or machine without user intervention; for example, by instructions responsive to a predicate action by the computer or machine or other initiation mechanism. The word “example” is used herein to mean “serving as an example or illustration.” Any aspect or design described herein as “example” is not necessarily to be construed as preferred or advantageous over other aspects or designs.

[0168] A phrase such as an “aspect” does not imply that such aspect is essential to the subject technology or that such aspect applies to all configurations of the subject technology. A disclosure relating to an aspect may apply to all configurations, or one or more configurations. An aspect may provide one or more examples. A phrase such as an aspect may refer to one or more aspects and vice versa. A phrase such as an “embodiment” does not imply that such embodiment is essential to the subject technology or that such embodiment applies to all configurations of the subject technology. A disclosure relating to an embodiment may apply to all embodiments, or one or more embodiments. An embodiment may provide one or more examples. A phrase such as an “embodiment” may refer to one or more embodiments and vice versa. A phrase such as a “configuration” does not imply that such configuration is essential to the subject technology or that such configuration applies to all configurations of the subject technology. A disclosure relating to a configuration may apply to all configurations, or one or more configurations. A configuration may provide one or more examples. A phrase such as a “configuration” may refer to one or more configurations and vice versa.

[0169] All structural and functional equivalents to the elements of the various aspects described throughout this disclosure that are known or later come to be known to those ofordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed by the claims. Moreover, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims. No claim element is to be construed under the provisions of 35 U.S.C. § 112(f), unless the element is expressly recited using the phrase “means for” or, in the case of a method claim, the element is recited using the phrase “step for”.

Claims

What is claimed is:

1. A system associated with a communication network configured to support time-sensitive deterministic communication, comprising: a processor configured to: receive configuration data of the communication network and an input query for retrieving status information related to data communication over the communication network, the communication network including a plurality of communication network (CN) components, each CN component configured to provide a discrete functionality for the data communication from a source device across the communication network to a destination device, input the configuration data and the input query to an Artificial Intelligence (Al) model to generate an intermediate query, obtain one or more query responses from the communication network based on the intermediate query, and optimize the intermediate query by removing redundant data based on the one or more query responses to output an optimized query.

2. The system of claim 1, wherein the processor is configured to: retrieve the status information using the optimized query; and generate a network schedule based on the status information such that at least one of the plurality of CN components supports the time-sensitive deterministic communication as a network block based on the network schedule.

3. The system of claim 2, wherein the processor is further configured to: determine a qualitative metric of the intermediate query by evaluating the one or more query responses for information redundancy.

4. The system of claim 3, wherein the processor is further configured to: determine a quantitative metric of the intermediate query by comparing a size of the one or more query responses from the communication network relative to a respective amount of relevant information included in the one or more query responses.

5. The system of claim 4, wherein the processor is further configured to: generate the optimized query based on the qualitative metric and the quantitative metric of the intermediate query.

6. The system of claim 1, wherein the processor is further configured to: reduce redundancy in the one or more query responses by selecting a set of query responses that are orthogonal to each other from the one or more query responses.

7. The system of claim 1, wherein the communication network includes a wireless network configured in accordance with a 3 GPP-defined standard.

8. The system of claim 1, wherein the processor is configured to perform an perturbation operation such that the Al model leams reaction of the communication network in response to the perturbation operation.

9. A method for actively probing a communication network configured to support timesensitive deterministic communication, comprising: obtaining configuration data of the communication network and an input query for retrieving status information related to data communication over the communication network, the communication network including a plurality of communication network (CN) components, each CN component configured to provide a discrete functionality for the data communication from a source device across the communication network to a destination device; inputting the configuration data and the input query to an Artificial Intelligence (Al) model to generate an intermediate query; obtaining one or more query responses from the communication network based on the intermediate query; and optimizing the intermediate query by removing redundant data based on the one or more query response to output an optimized query.

10. The method of claim 9, comprising: retrieving the status information using the optimized query; andgenerating a network schedule based on the status information such that at least one of the plurality of CN components supports the time-sensitive deterministic communication as a network block based on the network schedule.

11. The method of claim 10, further comprising: determining a qualitative metric of the intermediate query by evaluating the one or more query responses for information redundancy.

12. The method of claim 11, further comprising: determining a quantitative metric of the intermediate query by comparing a size of the one or more query responses from the communication network relative to a respective amount of relevant information included in the one or more query responses.

13. The method of claim 12, further comprising: generating the optimized query based on the qualitative metric and the quantitative metric of the intermediate query.

14. The method of claim 9, further comprising: reducing redundancy in the one or more query responses by selecting a set of query responses that are orthogonal to each other from the one or more query responses.

15. The method of claim 9, wherein the communication network includes a wireless network configured in accordance with a 3 GPP-defined standard.

16. The method of claim 9, further comprising: performing an perturbation operation such that the Al model learns reaction of the communication network in response to the perturbation operation.

17. A non-transitory computer-readable medium storage configured to store one or more instructions which, when executed by a processor, cause the processor to perform operations comprising: obtaining configuration data of a communication network and an input query for retrieving status information related to data communication over the communication network, the communication network including a plurality of communication network (CN) components, eachCN component configured to provide a discrete functionality for the data communication from a source device across the communication network to a destination device; inputting the configuration data and the input query to an Artificial Intelligence (Al) model to generate an intermediate query; obtaining one or more query responses from the communication network based on the intermediate query; and optimizing the intermediate query by removing redundant data based on the one or more query response to output an optimized query.

18. The non-transitory computer-readable medium storage of claim 17, wherein the operations comprise: retrieving the status information using the optimized query; and generating a network schedule based on the status information such that at least one of the plurality of CN components supports time-sensitive deterministic communication as a network block based on the network schedule.

19. The non-transitory computer-readable medium storage of claim 18, wherein the operations comprise: determining a qualitative metric of the intermediate query by evaluating the one or more query responses for information redundancy.

20. The non-transitory computer-readable medium storage of claim 19, wherein the operations comprise: determining a quantitative metric of the intermediate query by comparing a size of the one or more query responses from the communication network relative to a respective amount of relevant information included in the one or more query responses.

21. The non-transitory computer-readable medium storage of claim 20, wherein the operations comprise: generating the optimized query based on the qualitative metric and the quantitative metric of the intermediate query.

22. The non-transitory computer-readable medium storage of claim 17, wherein the operations comprising: reducing redundancy in the one or more query responses by selecting a set of query responses that are orthogonal to each other from the one or more query responses.

23. The non-transitory computer-readable medium storage of claim 17, wherein the communication network includes a wireless network configured in accordance with a 3 GPP- defined standard.

24. The non-transitory computer readable medium storage of claim 15, wherein the operations comprise: performing an perturbation operation such that the Al model learns reaction of the communication network in response to the perturbation operation.

25. A system associated with a communication network configured to support time-sensitive deterministic communication, comprising: a processor configured to: optimize an input query to generate an optimized query for retrieving status information related to data communication over the communication network based on an Artificial Intelligence (Al) model, the communication network including a plurality of communication network (CN) components, each CN component configured to provide a discrete functionality for the data communication from a source device across the communication network to a destination device, retrieve the status information using the optimized query, and generate a network schedule based on the status information such that at least one of the plurality of CN components supports the time-sensitive deterministic communication as a network block based on the network schedule.

26. The system of claim 25, wherein the processor is configured to: receive configuration data of the communication network and the input query, input the configuration data and the input query to the Al model to generate an intermediate query,obtain one or more query responses from the communication network based on the intermediate query, and optimize the intermediate query by removing redundant data based on the one or more query responses to output the optimized query.

27. The system of claim 26, wherein the processor is configured to perform an perturbation operation such that the Al model learns reaction of the communication network in response to the perturbation operation.

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