Ai-ML 5g TSN flow modeling

Abstract

A communication network, configured to update, based on an Artificial Intelligence (Al) function, a control law of a device for the transmission of the control signal. Sending a control signal configured to support time-sensitive deterministic communication based on a time-sensitive network (TSN) mechanism. A processor sends quality of service requirement information for transmission of the control signal to an end device, poll components of the communication network for operating parameter information, send the control signal based on the quality of service requirement information to the end device, receive operating parameter information from the components of the communication network in response to the poll for operating parameters, and receive feedback information from the end device; and update, based on an Artificial Intelligence (Al) function, the control law of the device for the transmission of the control signal to compensate for delays in transmission of the control signal.

Description

AI-ML 5G TSN Flow ModelingCROSS-REFERENCE TO RELATED APPLICATION

[0001] The present disclosure claims benefit to U.S. Provisional Application Serial No. 63 / 583,811, entitled “AI-ML 5G TSN Flow Modeling,” filed on September 19, 2023, the disclosure of which is incorporated herein by reference in its entirety.TECHNICAL FIELD

[0002] The present description relates generally to a device and / or system interacting with a wireless communication network system which is integrated with a time-sensitive network (TSN) mechanism, and, more particularly, for example, to configuration of the device to simultaneously optimize a control law of a controller to control operation of a target industrial system and TSN schedule based on 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 wireless communication network in accordance with 3GPP-defined specifications (e.g., 5G, 6G, etc.), which provides a high reliability service, such as an ultra-reliable low latency communication (URLLC) service. Thus, the controllers generate and transmit control signals to control operation of end devices (e.g., actuator, sensor) in a target industrial system (e.g., a factory, plant, etc.) via the wireless communication network.SUMMARY

[0004] Accordingly, there is a need for devices and methods for sending a control signal via a communication network configured to support time-sensitive deterministic communication. In one aspect, some implementations include a device for sending a control signal via a communication network configured to support time-sensitive deterministic communication based on a time-sensitive network (TSN) mechanism. The device includes a processor and a memory communicatively coupled with the processor. The memory stores one or more instructions that, when executed by the processor, cause the processor to: send quality of service requirement information for transmission of the control signal to an end device, the control signal configured to control operation of the end device in a system associated with the communication network, poll components of the communication network for operating parameter information related to operating parameters of the communication network, send the control signal based on the quality of service requirement information to the end device, wherein the control signal is based on a control law, receive operating parameter information from the components of the communication network in response to the poll for operating parameters, and receive feedback information from the end device, and update, based on an Artificial Intelligence (Al) function, the control law of the device for the transmission of the control signal based on the operating parameters information and feedback information to compensate for delays in transmission of the control signal.

[0005] In another aspect, some implementations include a method for sending a control signal via associated with a communication network configured to support time-sensitive deterministic communication based on a time-sensitive network (TSN) mechanism, including: sending quality of service requirement information for transmission of the control signal to an end device, the control signal configured to control operation of the end device in a system associated with the communication network, polling components of the communication network for operating parameter information related to operating parameters of the communication network, sending the control signal based on the quality of service requirement information to the end device, wherein the control signal is based on a control law, receiving operating parameter information from the components of the communication network in response to the poll for operating parameters, and receive feedback information from the end device, and updating, based on an Artificial Intelligence (Al) function, the control law of the device for thetransmission of the control signal based on the operating parameter information and feedback information to compensate for delays in transmission of the control signal.

[0006] In another aspect, a system configured to perform any of the above methods is provided, according to some implementations.

[0007] In another aspect, a non-transitory computer-readable storage medium has one or more processors and memory storing one or more programs executable by the one or more processors. The one or more programs include instructions for performing any of the above methods.BRIEF DESCRIPTION OF THE DRAWINGS

[0008] 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.

[0009] Fig. 1 illustrates an example of a conventional integrated TSN-5G system in accordance with one or more implementations.

[0010] Fig. 2 illustrates a block diagram of an example integrated TSN-5G system architecture in accordance with one or more implementations.

[0011] Fig. 3 illustrates an example of an integrated TSN-5G system in accordance with one or more implementations.

[0012] Fig. 4 illustrates a block diagram of an example TSN block in accordance with one or more implementations.

[0013] Fig. 5 illustrates an example set of parameters for an example TSN block in accordance with one or more implementations.

[0014] Figs. 6A, 6B illustrate a block diagram of an example integrated TSN-5G system architecture including MEC in accordance with one or more implementations.

[0015] Fig. 7 illustrates an electronic system with which one or more implementations of the subject technology may be implemented.

[0016] Fig. 8 illustrates a block diagram of an example integrated TSN-5G system architecture in accordance with one or more implementations.

[0017] Fig. 9 illustrates an example of a control system in accordance with one or more implementations.

[0018] Fig. 10 illustrates a flow diagram of example operations of a control system in accordance with one or more implementations.

[0019] Fig. 11 illustrates an example of operations of an AI / ML system in accordance with one or more implementations.

[0020] Fig. 12 illustrates an example of a computational graph of a Neural Network (NN) in accordance with one or more implementations.

[0021] Fig. 13 illustrates a flow diagram of example operations of a control system in accordance with one or more implementations.

[0022] Fig. 14 illustrates block diagrams of an example control system in accordance with one or more implementations.DETAILED DESCRIPTION

[0023] 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.

[0024] 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 communication network (such a wireless network or a network compatible with a 3GPP standard, such as a 5G network 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 includes various 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 for 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.

[0025] 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 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 eachconfiguration module may be responsible to communicate with, and configure, one or more TSN blocks.

[0026] 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 URLLC 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 block may 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.

[0027] 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 cycles 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 componentsto 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.

[0028] 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 without explicit command 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 a 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.

[0029] In some implementations, the present disclosure provides a communication network, e.g., a wireless network such as a fifth-generation (5G) or sixth-generation (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 time-sensitive network (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 orchannels. The various components of the network include modules and channel s / links implemented in hardware, software, and in a combination of hardware and software.

[0030] 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.

[0031] The plurality of discrete CN components of the 5G network may include a user equipment (UE), a radio access network (RAN), a user plane function (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.

[0032] 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 communication network compatible with a 3GPP standard.

[0033] 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 TSNparameters 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.

[0034] 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 communication network (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 time-sensitive 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 implementation, 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.

[0035] 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 communication network (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 time-sensitive 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 datacommunication 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 implementation, 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.

[0036] The industrial system may be controlled by control signals provided by a control system and signaled via such wireless communication network in accordance with 3 GPP-defined standard (e.g., 5G, 6G, etc.). While the wireless communication network provides the communication (typically assumed to be ultra-reliable low latency communications (URLLC)) required to interconnect control system components, it does not have inherent control system knowledge or capabilities for user applications. Most control systems assume communication latency is negligible. As discussed above, the wireless communication network may be integrated with a Time-Sensitive Network (TSN) system to provide bounded, predetermined latency (scheduled traffic flows) for the control system to prevent the control system from becoming unstable. The stability of a control system is defined as the ability of any system to provide a bounded output when a bounded input is applied to it. If the messages experience the variable latency or delay in communication, the controller’s reaction will occur either too early or too late causing the system to become unstable (i.e., lose control of the system without hope of regaining control). For the wireless communication network — TSN — to be successful, it must ultimately keep the control system stable in wireless environment that is susceptible to noise and greater challenges causing the variable latency or delay. However, such latency or delay is not accounted for a control law of the controller which is a mathematical formula used by the controller to determine the control signals so that the controller’s performance can suffer. Thus, solutions how to compensate for variable latency or delay are required.

[0037] To address the above-discussed issues, the subject technology provides a control system which leverages an Artificial intelligence / Machine Learning (AI / ML) system associated with the wireless communication network. Thus, in some implementations, the control system may include a device for sending a control signal via a communication network configured tosupport time-sensitive deterministic communication based on a time-sensitive network (TSN) mechanism comprising a processor and a memory communicatively coupled with the processor and one or more instructions stored in the memory which when executed by the processor cause the processor to: send quality of service requirement information for transmission of the control signal to an end device, the control signal configured to control operation of the end device in a system associated with the communication network, poll components of the communication network for operating parameter information related to operating parameters of the communication network, send the control signal based on the quality of service requirement information to the end device, wherein the control signal is based on a control law, receive operating parameter information from the components of the communication network in response to the poll for operating parameters, and receive feedback information from the end device and update, based on an Artificial Intelligence (Al) function, the control law of the device for the transmission of the control signal based on the operating parameter information and feedback information to compensate for delays in transmission of the control signal.

[0038] 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 areexecuted 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.

[0039] 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 are compliant 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.

[0040] 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 [D1.0, final comment disposition (vl)]. 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.

[0041] High availability, as a result of ultra-reliability, may be provided by Frame Replication and Elimination for Reliability (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(802.1 Qci) improves reliability by protecting against bandwidth violation, malfunctioning and malicious behavior. Further, the time synchronization in the TSN system may be defined by the generalized Precision Time Protocol (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).

[0042] 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 on system 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 schedule for TSN data traffic is discussed in U.S. Application No. 17 / 100,356, which is incorporated herein in its entirety by reference.

[0043] Time-critical communication between end devices or nodes (e.g., the VO 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 VO 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 bebetween 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.

[0044] 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 the data 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 be 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.

[0045] 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.

[0046] 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 assigneddifferent paths that meet the requirements. In some implementations of the subject invention, 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.

[0047] 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 camera that 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.

[0048] 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.

[0049] 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. Thecontroller 104 may define or determine the data flows and data flow characteristics in the TSN system 100.

[0050] 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 User Plane Function (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 incorporated herein in entirety. As shown, the 5G system 106 may include various communication network (CN) components such as, in the 5G user plane, User Equipment (UE) 118, RAN (gNB) 120, User Plane Function (UPF) 122, and in the 5G control plane, application function (AF) 124, and session management function (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 ultra-reliable low latency communication (URLLC) service. The 5G system 106 based on the New Radio (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.

[0051] 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.

[0052] 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 provide 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 protocol data unit (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.

[0053] 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.

[0054] 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 5Gdomains. 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.

[0055] 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 102 and 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.

[0056] 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 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, oneor 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 which 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 wire-less 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.

[0057] 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 destination device 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.

[0058] 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, Multi-access Edge Computing (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.

[0059] 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 translators (TT)-l 412-1, TT-2412- 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.

[0060] 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) may include 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 param eter(s) may include at least packet error rate. The link bandwidth parameters) may include at least the available bandwidth in bits per second.

[0061] 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.

[0062] 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 controllist 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.

[0063] 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 of devices from multiple vendors and of varying characteristics.

[0064] 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. These set of parameters enable scheduling and configuration of a mixed capability network wherein end nodes and switching nodes have varying degree of support (including no support) for the desired TSN features and version.

[0065] Further non-limiting examples of the set of parameters for a TSN block 202 device may include, but is 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 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 to operate in response to, or in accordance with, a schedule or a configuration for the TSN system 200.

[0066] Further, each TSN block 202 may include at least one internal configuration interface (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.

[0067] 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.

[0068] 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.

[0069] 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 5G component 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 802.1 Qbv 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.

[0070] 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.

[0071] 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 CM215-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.

[0072] 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 some implementations, 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.

[0073] 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 cycles 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 202, and information to request or respond to requests for resource allocations. In some implementations, one or more ofthe 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.

[0074] 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 to the 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 a maximum allowable latencies, data rate, data frame sizes (“payload”), data frame destinations, band allocation gaps, the like, or a combination thereof.

[0075] 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 executes this solution and transmits 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 modulotheory (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.

[0076] 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 scheduled transmissions may be based on 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 cycles 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 continuous set of cycle times. In another implementation, set of cycle times are 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.

[0077] 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 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.

[0078] 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 which 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 though 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.

[0079] 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 a specific transmission information for 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 communicatively coupling 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).

[0080] 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 adata 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).

[0081] 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 service provider 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.

[0082] 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 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 in the monitoring metrics by the recording module 410.

[0083] 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 downlinktransmissions 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 of 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 a 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.

[0084] 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 not limited to a logical channel, transmission power, and UE specific slot duration. In some implementations, a static assignment of fixed / determini Stic 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 the transmission 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 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.

[0085] 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 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. Inthis 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 messages over different frequencies in order to increase the probability of meeting the transmission schedule and / or deadline.

[0086] 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 paths between 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.

[0087] 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(LLDP)). 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 (NEF). 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.

[0088] 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 conforming to 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 a 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.

[0089] DETNET is comprised 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: (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 underlyingnetwork (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.

[0090] 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), TTL, and acquire routing latency and jitter. TSN traffic shapers, time-aware shaping, and network calculus may be used to achieve the required level of determinism in the 5G DETNET.

[0091] 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 n.

[0092] 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.

[0093] 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 a 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, comprised 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.

[0094] 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 leakybucket 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 this invention, 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.

[0095] 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, a 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 the MEC application may migrate. The TSN scheduler, i.e., the Centralized Network Configurator (CNC) or Distributed Network Configurator (DNC), may be a MEC application offering 5G TSN scheduling-as-a- service and configuration-as-a-service, useful for dynamic rescheduling, where low latency is required.

[0096] 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 beused 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.

[0097] The subject disclosure also provides additional new aspects including: (1) TSN- scheduled 5G core functions; (2) Ensuring the CPU 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., timebased event processing; (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: / / www.rfc-editor.org,zrfc / pdfrfc / rfc7758.txt.pdf for example of YANG scheduled operation).

[0098] Further, a LinkDelayStatistic (implemented as a YANG model) cumulative distribution function data model may be implemented within the TSN-5G system 106 or 200. In such an implementation, wireless links may collect and feed information to the CNC’s scheduler which 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.

[0099] 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 (TaaS). 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 theTSN 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 processing delay. TSN input provides a clear arrival curve. Processor execution time provides a service curve (we assume 5G equipment has a well-characterized processing time).

[0100] 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 a 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 PTP clock, wherein 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 the synchronized 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 PTP client connected to the host via a time aware bridge.

[0101] 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, a 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, a 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 MECshall 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 a MEC host requires a IP stream identification in a MEC bridge.

[0102] 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. A 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 and 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.

[0103] 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 a Multi-access Edge Computing (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.

[0104] 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 a5G backbone, private network tunnel or other wide area networks. In this subject invention, 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 centralized configuration utility (CNC) 112. In some other implementations, the CMs 215 between the two TSN-5G systems 805, 810 may communicate directly. In some implementations, multiple CUC’s and CNC’s 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.).

[0105] 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, and / or 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.

[0106] 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 or more 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.

[0107] 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 amagnetic or optical disk and its corresponding disk drive) may be used as the permanent storage device 702.

[0108] 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.

[0109] 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 of images 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.

[0110] 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 networkinterface(s) 716. In this manner, the electronic system 700 can be a part of a network of computers (such as a LAN, a wide area network (“WAN”), or 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.

[0111] 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.

[0112] 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 (CD-ROM), recordable compact discs (CD- R), rewritable compact discs (CD-RW), read-only digital versatile discs (e.g., DVD-ROM, duallayer DVD-ROM), a variety 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.

[0113] While the above discussion primarily refers to microprocessor or multi -core processors that execute software, some implementations are performed by one or more integratedcircuits, such as application specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs). In some implementations, such integrated circuits execute instructions that are stored on the circuit itself.

[0114] 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 means 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.

[0115] 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 CRT (cathode ray tube) or LCD (liquid crystal display) 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 by sending 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.

[0116] 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 datacommunication, e.g., a communication network. Examples of communication networks include a local area network (“LAN”) and a wide area network (“WAN”), an inter-network (e g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks).

[0117] 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 time-sensitive network (TSN) mechanism. The wireless network may include a plurality of discrete communication network (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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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 time-sensitive network (TSN) mechanism is provided. The wireless network may include a plurality of discrete communication network (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 hierarchical arrangement, a mesh arrangement, a star arrangement, a tree arrangement, a combination thereof.

[0122] 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.

[0123] 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 P802.1Qdj [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 N3 interface according to the configuration in the Status group.

[0124] 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.

[0125] 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 communication network (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 thesecond wireless 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.

[0126] 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.

[0127] 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.

[0128] 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 at least 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.

[0129] A wireless communication network in accordance with 3GPP-defined standard (e.g., 5G, 6G, etc.) can transmit information quickly at low latency. The industrial system may be controlled by control signals provided by a control system and signaled via such wirelesscommunication network. The control system typically requires r (reference input - target output), e (output error), u (control parameter state), and y (actual output). The control system may include a user (entity controlling the reference input), a controller (the system that decides what action to take), a target industrial system (e.g., a plant, a factory, etc.), and sensors which may be remotely located from one another. Additionally, a model, usually in the form of differential equations, relates the rate of change of the target industrial system to changes in control parameters. While the wireless communication network provides the communication (typically assumed to be ultra-reliable low latency communications (URLLC)) required to interconnect control system components, it does not have inherent control system knowledge or capabilities for user applications. Most control systems assume communication latency is negligible. However, the performance of the control system depends critically upon control information providing an accurate, real-time state of the control system. For example, components of the control system are distributed over the wireless communication network, messages between components could arrive with variable latency or delay. As discussed above, the wireless communication network may be integrated with a Time-Sensitive Network (TSN) system to provide bounded, predetermined latency (scheduled traffic flows) for the control system to prevent the control system from becoming unstable. The stability of a control system is defined as the ability of any system to provide a bounded output when a bounded input is applied to it. If the messages experience the variable latency or delay in communication, the controller’s reaction will occur either too early or too late causing the system to become unstable (i.e., lose control of the system without hope of regaining control). For the wireless communication network — TSN — to be successful, it must ultimately keep the control system stable in wireless environment that is susceptible to noise and greater challenges causing the variable latency or delay. However, such latency or delay is not accounted for in a control law of the controller which is a mathematical formula used by the controller to determine the control signals so that the controller’s performance can suffer. Thus, solutions that compensate for variable latency or delay are required.

[0130] To address the above-discussed issues, various solutions such as making the communication network provide low, deterministic latency or increasing the sophistication of the control law to compensate for variable communication delay or a combination of both areprovided. One technique to solve this is to allow wireless communication network — TSN— to become a more active participant in the control system by jointly developing the control law and the TSN schedule, rather than developing them separately. Thus, the subject technology provides a control system which leverages an Artificial intelligence / Machine Learning (AI / ML) system associated with the wireless communication network. The AI / ML system can minimize operational complexity by utilizing efficient algorithms. The AI / ML system can dynamically update a control law of the controller to increase the sophistication of the control law. The control law is a mathematical formula used by the controller to determine the output u that is sent to the target industrial system (e g., a plant). In a feedback control scenario, the output u can lead to robustness to uncertainty, and can be used to design system dynamics. The control law, which is usually part of the controller, takes the output of a process one wants to control as input. This input is then used in an algorithm that the controller uses to determine u, which is the input to the target industrial system. For example, in a cruise control system, the control law compares the reference speed to the current speed and uses this error (v _r - v) in a PID control algorithm to determine the signal u that is then sent to end devise (e.g., actuators) to decrease the error between the desired and actual speed.

[0131] In addition, the AI / ML system can simultaneously learn corresponding Time- Sensitive Network (TSN) flow schedules that support it over the communication network and create the required TSN flow within network constraints based on the updated control law. Thus, in some implementations, the control system may include a device for sending a control signal via a communication network configured to support time-sensitive deterministic communication based on a time-sensitive network (TSN) mechanism comprising a processor and a memory communicatively coupled with the processor and one or more instructions stored in the memory which when executed by the processor cause the processor to: send quality of service requirement information for transmission of the control signal to an end device, the control signal configured to control operation of the end device in a system associated with the communication network, poll components of the communication network for operating parameter information related to operating parameters of the communication network, send the control signal based on the quality of service requirement information to the end device, wherein the control signal is based on a control law, receive operating parameter information from thecomponents of the communication network in response to the poll for operating parameters, and receive feedback information from the end device and update, based on an Artificial Intelligence (Al) function, the control law of the device for the transmission of the control signal based on the operating parameter information and feedback information to compensate for delays in transmission of the control signal.

[0132] Fig. 9 illustrates an example of a control system in accordance with one or more implementations.

[0133] A control system 900 in Fig. 9 depicts a block diagram of an example implementation of an integrated TSN-wireless communication network system similar to the system 100 and the system 200 described above, and also includes similar physical components as in the system 100 and the system 200 discussed above. In addition, the control system 900 includes an Artificial Intelligence / Machine Learning (AI / ML) control application 910 which is configured to interact with the wireless communication network 106 and end devices 102 (e.g., an actuator, a sensor, etc.). As shown in Fig. 9, the AI / ML control application 910 includes a control law optimizer 920 which is configured to provide an AI / ML function to optimize a control law of a controller (e.g., a controller 104). In some implementations, the control law optimizer 920 may be referred to as an AI / ML system.

[0134] As discussed above, the control system 900 typically requires r (reference input - target output), e (output error), u (control parameter state), and y (actual output), the control system 900 includes a controller which decides what action to take. The control law is a mathematical formula used by the controller to determine the output u that is sent to a target system (or an end device). The control law may take the output of a process one wants to control as input to determine u to lead to robustness to uncertainty which can be used to design system dynamics. The determined u can be an input to the target system (plant). For example, in a cruise control system, the control law compares the reference speed to the current speed and uses this error (v r - v) in a PID control algorithm to determine the signal u that is then sent to the target system (e.g., actuators) to decrease the error between the desired and actual speed. For example, the control law could be enhanced to take sensing delay into account to mitigate any dangerous oscillatory behaviors (e.g., obstacle in front of a car), but only if the control law were intelligent enough to learn the sensing delay and take it into account. The type of control law could change, for example, as follows: 1.Dynamic programming, Bellman equations, optimal value functions, value and policy iteration, shortest paths, Markov decision processes; Hamilton-Jacobi-Bellman equations, and approximation methods; Pontryaginis maximum principle, ODE and gradient descent methods, relationship to classical mechanics; Linear-quadratic-Gaussian control, Riccati equations, iterative linear approximations to nonlinear problems; Optimal recursive estimation, Kalman Olter, Zakai equation; Duality of optimal control and optimal estimation (including new results). Additionally, configurations and parameters of the control law could also change.

[0135] In some implementations, closing the control loop over wireless channels results in noise and is subject to message loss in the control system. Proper allocation of limited resources, e.g., transmission power, MIMO configuration, beam pointing, resource blocks, bandwidth, and scheduling, is critical for maintaining reliable operation. Many AI / ML techniques, including deep reinforcement learning techniques may be used to find neural network-based resource allocation policies. Model-free policy gradient methods may be used to directly learn continuous communication allocation policies without knowledge of plant dynamics or communication models. In some implementations, the control system 900 may have many distributed controllers that need to be allocated 5GS resources, including TSN scheduling. In some implementations the AI / ML system 920 may learn how to allocate such distributed controllers.

[0136] While interacting with the wireless communication network, the control system 900 may expose its control law requirements to the wireless communication network 106 and allow the communication network 106 to perform tradeoffs and allocate resources as needed. For example, the control system sensors may receive low but variable latency while the reference r and actual output y signals may receive higher but more deterministic latencies, or any combination may be possible. In some implementations, the AI / ML can be used to control all types of control systems. There are many types of control systems. An often-used type controller is a proportional- integral-derivative controller (PID controller or three-term controller) and is a control loop mechanism employing feedback that is widely used in industrial control systems and a variety of other applications requiring continuously modulated control. A PID controller continuously calculates an error value as the difference between a desired setpoint (SP) and a measured process variable (PV) and applies a correction based on proportional, integral, and derivative terms (denoted P, I, and D respectively), hence the name. In practical terms, the PID controller automatically applies an accurate and responsive correction to a control function. An everydayexample is the cruise control on a car, where ascending a hill would reduce speed if constant engine power were applied. The controller's PID algorithm restores the measured speed to the desired speed with minimal delay and overshoot by increasing the power output of the engine in a controlled manner. In some implementations, the AI / ML system 920 may learn to adjust PID configuration parameters. For example, the AI / ML system 920 learns to optimize the K_p, K i, K_d coefficients of the PID controller.

[0137] More generally, the stability of the control system 900 is defined as the ability of the system to provide bounded output when a bounded input is applied. Stability allows the system to reach steady-state and remain in that state for the given input after all variation in parameters of the system. The relative stability of the feedback loop is described in terms of the gain and the phase margins. Gain and phase margins measure the tolerance of a control loop to variations in the open-loop system response. One of the main goals of the AI / ML system 920 is to provide sufficient wireless communication network capability to maintain required gain and phase margins. Other control system performance aspects include damping ratio, settling time, bandwidth, and system sensitivity. The damping ratio is defined as the number of oscillations in a system that can decay or restrain after an interruption, and it is a dimensionless measurement. The control system 900 may exhibit an oscillatory mode when they are interrupted or disturbed from their initial position. Settling time is the time required for a response to become steady. Steady-state error is the difference between actual output and desired output at an infinite range of time. Response time is the time that the control system 900 takes to reach its steady state value if it maintains the initial rate of change (after delay time). Delay time is the time required for the response to reach half the final (target) value (for the first time in the case of oscillation around the target value). In most systems this would become the dominant time constant. The bandwidth of a closed-loop control system is defined as the frequency range such that the magnitude of the closed loop gain does not fall below -3dB. Sensitivity characterizes the impact of changes in process dynamics. Controller parameters are typically matched to the process characteristics and since the process may change, it is important that the controller parameters are chosen in such a way that the closed loop system is not sensitive to variations in process dynamics.

[0138] The AI / ML system 920 associated with the wireless communication network 106 should ingest information about the control system 900 (e.g., the control law) and information about wireless communication network instantiation and return the configuration that best satisfiesboth the wireless communication network 106 (consumes a minimal amount of network resources) and the control system 900 (meets the previously discussed control system performance characteristics). The AI / ML system 920 should sample both network resources and control system parameters during its learning process.

[0139] A framework for the AI / ML system 920 is defined by 3GPP 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 (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 (2023-03, Release 18) which are incorporated herein by reference in their entirety.

[0140] The wireless communication network 106 is similar to the 5G system 106 as discussed above. The wireless communication network 106 includes a plurality of communication network (CN) components, e.g., components 110, 118, 120, 122, 124, 126, 140, 190, etc.,. The wireless communication network 106 can be referred to as a communication network. As shown in Figure 9, the AI / ML control application 910 can send a control input 912 to the wireless communication network 106. As described above, the control input 912 includes a control signal which is generated based on a control law of a controller to control operations of end devices (e.g., a sensor, an actuator, etc.). Such control signal is transmitted to the end devices. Each CN component configured to provide a discrete functionality for transmission of control signal across the communication network to the end device. In some implementations, at least one of the plurality of CN components is configured to provide network functions 126 in accordance with 3GPP standards. The network functions 126 include control plane functions and user plane functions. In some implementations, control plane functions may be referred to as 5G core control functions. The network functions 126 include, but are not limited to, Artificial Intelligence- Application Function (AI-AF), Access and Mobility Management Function (AMF), Session Management Function (SMF), Network Data Analytics Function (NWDAF), Network Slice Selection Assistance Information (NSSAI) and User Plane Function (UPF). The AI-AF specifies how the wireless communication network 106 interacts with the control system 900. The AMF is a component of the 3 GPP core network architecture that manages user equipment registration, authentication, identification, and mobility. The SMF is a fundamental element of the 5G Service-Based Architecture (SB A) and is primarily responsible for interacting with the decoupled data plane, creating updating and removing Protocol Data Unit (PDU) sessions and managing session context with the UPF. The NWDAF is a 3GPP standard method used to collect data from user equipment, network functions, and operations, administration, and maintenance (OAM) systems, etc. from the 5G Core, Cloud, and Edge networks that can be used for analytics. The NSSAI is the interface for determined the resources and availability or creating a network slice. The UPF connects the data coming over the Radio Area Network (RAN) to the required endpoints. QoS is the Quality of Service and quantifies both the latency and determinism of message transport times with respect to the requirements of the real-time control system.

[0141] In some implementations, the AI / ML control application 910 can interact with AMF in the communication network 106. For example, as shown in Fig. 9, the AI / ML control application 910 can send a request for a communication network status to the communication network 106 via AMF. The request for a communication network status includes a request to poll components of the communication network for operating parameter information related to operating parameters of the communication network. Thus, the AI / ML control application 910 may poll components of the communication network for operating parameter information related to operating parameters of the communication network for data path provision to transmit the control signal. In some implementations, the AI / ML control application 910 polls components of the communication network for operating parameter information related to operating parameters of the communication network for bandwidth, latency, forecasted load along a path to the end devices 102.

[0142] In some implementations, in response to the request for the communication network status, at least one of the plurality of CN components may be configured to provide at least one of the network functions 126 to collect data and send a response including the communication network status 930 to the AI / ML control application 910. In some implementations, the response including the communication network status 930 includes data from NWDAF 126, YANG models related to network functions such as AMF, SMF, and UPF. The response 930 from the communication network 106 may be provided as input data (or features) to the AI / ML system 920. In some implementations, the data from NWDAF 126 is provided as a primary input data to know the status and forecasted load of the wireless communication network 106 while YANG models related to network functions such as AMF, SMF, and UPF is provided as a secondary input data.It is assumed that sufficient network time synchronization is implemented to support TSN and that all sampled data used for AI / ML features is accurately timestamped. In machine learning and pattern recognition, each AI / ML feature is an individual measurable property or characteristic of a phenomenon.

[0143] As shown in Fig. 9, in some implementations, the wireless communication network 106 transmit the control input 912 to the end devices 102 and receive feedback information 935 from the end devices 102. In some implementations, the feedback information 935 may include sensor values (e.g., speed in the cruise control system). In some implementations, the feedback information 935 may include flight control information in aircraft control system, laser control for additive manufacturing, video quality in a video codec, channel state estimation or beam pointing in wireless systems, or any other desired real-time control parameter. The wireless communication network 106 transmit the feedback information 935 to the AI / ML control application 910. In some implementations, the feedback information 935 is also provided as input data to the AI / ML system 920.

[0144] The AI / ML control application 910 may receive the response 930 including the communication network status information and the feedback information 935 from the wireless communication network 106 and provide them as input data to the AI / ML system 920. The AI / ML system 920 learn a control law of the controller and optimize the control law to compensate for delays in the data path for transmission of the control signal. The output of the AI / ML system 920 includes one or more polices that will be used to configure a Network slice and MFC application. The one or more polices can define a TSN schedule, Network Slice Information, and network access priority. For example, the one or more policies may define additional constraints such as traffic load, energy usage, RF interference, computational load or priority to higher level emergency traffic flows. In some implementations, the AI / ML control application 910 is designed to send out a control signal which should be received at the sensor every predetermined time (e.g., 20ms). Based on the feedback information 935 from the wireless communication network 106, the AI / ML system 920 learns that it the wireless communication network 106 could support 15ms ~40ms with a jitter of 10ms. With this learned information, one of the AI / ML model in the AI / ML system 920 may output a policy that could optimize the control law to transmit control signals at AI / ML model determined intervals such that the end device always receives control signal at 20ms.The joint optimization of Control law and network policy will try to ensure the sensor receives the control signal at 20ms by compensating for the network jitter.

[0145] In some implementations, the control system 900 may include a TSN scheduler (e.g., configuration module 215) which determines the TSN schedule. At least one of the plurality of CN components is configured to provide a TSN-Application Function (TSN-AF) 124 to manage configuration of the TSN based on the TSN schedule. As discussed above the TSN is to provide bounded, predetermined latency (scheduled traffic flows) for the control system 900 to prevent the control system 900 from becoming unstable. The TSN schedule determines how long messages can take among the controller, sensors 102, and actuators 102. Thus, the AI / ML system 920 can reposition or regroup microservices of the AI / ML control application 910 based on the updated control law and the received response 930 and feedback information 935. Each microservice provides each function of the AI / ML control application 910 and can be implemented over resources of the wireless communication network 106. The AI / ML system 920 may split at least one of the microservices or combine at least one of the microservices to reposition or regroup the microservices over the resources of the wireless communication network 106. Thus, TSN scheduling and configuration must change accordingly. In some implementations, the TSN scheduler updates the TSN schedule based on the updated control law so that each of the CN components is provided to TSN-AF function to change configuration of TSN blocks based on the updated TSN schedule. Thus, the control system 900 can autonomously and simultaneously update the control law and the TSN schedule.

[0146] Fig. 10 illustrates a flow diagram of example operations of a control system in accordance with one or more implementations.

[0147] A flow diagram 1000 illustrates operations of the control system 900 as discussed above. The control system 900 includes a control application host that provides virtualization environment an AI / ML control application 910. In operation 1010, the control application host may send data path requirement information for data path requirements as Network slice SLA to a wireless communication network 106 and instantiates provisioning using network functions 126 such as AMF and SMF and TSN-AF 124. In some implementations, the data path requirement information includes computational latency information and communication latency information required for the transmission of the control signal. In some implementations, the wireless communication network 106 provides network functions 126 to configure data path provision fortransmission of a control signal generated by a controller based on a control law based on the data path requirements. As discussed in Fig. 9, the AI / ML control application 910 can send a request for a communication network status 915 to the communication network 106 via AMF. The request for a communication network status 915 includes a request to poll components of the communication network for operating parameter information related to operating parameters of the communication network. In some implementations the operating parameter information related to operating parameters of the communication network includes network status information for a bandwidth, latency and forecasted load along the data path to the end device. Thus, in operation 1020, the AI / ML control application 910 polls the wireless communication network status for the data path provision. In operation 1030, the AI / ML control application 910 polls the wireless communication network status for bandwidth, latency, forecasted load along the data path to end devices 102 such as a sensor or actuator. In operation 1040, the AI / ML control application 910 sends a control signal (or control signals) to the end devices 102 via the wireless communication network 106. In operation 1050, the AI / ML control application 910 collects feedback information 935 from the end devices 102 and a response to the poll 930 from the wireless communication network 106. As discussed in Fig. 9, the AI / ML control application 910 provide the feedback information 935 and the response 930 as input data to the AI / ML system 920. Thus, as shown in operation 1060, the AI / ML control application 910 optimizes a control law of the controller to compensate for delays in the data path. In operation 1070, the AI / ML control application 910 may request new data path requirements to the control host. The new data path requirements include, but are not limited to, Network Slice, and Network Access Priority (Network Slice SLA). The operations 1010-1070 may periodically loop to adopt to change.

[0148] Fig. 11 illustrates an example of operations of an AI / ML system in accordance with one or more implementations.

[0149] In some implementations, the AI / ML system 1100 in Fig. 10 may be implemented as the AI / ML system 920 in Fig. 9 for updating a control law of the controller as discussed above. As shown in Fig. 11, the AI / ML system 1100 may include a Data Collection module 1110, a Model Training module 1120, a Model Interference module 1130 and an Actor module 1140. The operations of the AI / ML system 1100 may be implemented in a distributed manner across the wireless communication network 106.

[0150] In some examples, the Data Collection module 1110 provides input data to the Model Training module 1120 and the Model Interference module 1130. As discussed in Fig. 9 and Fig.10, the input data may include data from NWDAF 126 and YANG models related to network functions such as AMF, SMF, and UPF. In some implementations, the data from NWDAF 126 is provided as a primary input data to know the status and forecasted load of the wireless communication network 106 while YANG models related to network functions such as AMF, SMF, and UPF is provided as a secondary input data. In some embodiments, AI / ML algorithm specific data preparations (e.g., data pre-processing and cleaning, formatting, and transformation) are not carried out in the Data Collection module 1110. In some implementations, the input data may include measurements from various CN components such as UEs or different network entities, feedback 1145 from the Actor module 1140, and / or data output from an AI / ML model.

[0151] In some implementations, based on the input data, the Data Collection module 1110 may provide training data 1112 to the Model Training module 1120 during a training stage of the AI / ML system 1100; and provide inference data 1114 to the Model Interference module 1130 during an inference stage of the AI / ML system 1100. In some implementations, the training data 1112 is data needed as input for training, validating and testing an AI / ML model at the Model Training module 1120; and the inference data 1114 is data needed as input for performing an inference function using a trained AI / ML model at the Model Interference module 1130.

[0152] In some implementations, the AI / ML system 1100 may be in a training stage, or an inference stage, or both in real-time. One purpose of the training stage is to train (and validate and test) a function or model, e g. by learning some optimal parameters or weights in the function or model based on some optimization metric, given known input and known output of the function or model from the training data. One purpose of the inference stage is to apply a trained function or model on some known input from the inference data, to infer output of the trained function or model based on the learned optimal parameters or weights in the trained function or model.

[0153] During a training stage of the AI / ML system 1100, the Model Training module 1120 performs the AI / ML model training, validation, and testing which may generate modelperformance metrics as part of the model testing procedure. In some embodiments, the Model Training module 1120 may be also responsible for data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on the training data 1112 delivered by the Data Collection module 1110, if needed or required. At the end of the training stage, the Model Training module 1120 can generate a model deployment / update 1122 to initially deploy a trained, validated, and tested AI / ML model to the Model Interference module 1130 or to deliver an updated AI / ML model to the Model Interference module 1130.

[0154] During an inference stage of the AI / ML system 1100, the Model Interference module 1130 applies the trained or re-trained (updated) AI / ML model on the inference data 1114 to generate an output 1135 which may include e.g., predictions or decisions. The Model Interference module 1130 may provide model performance feedback 1132 based on predictions or decisions to the Model Training module 1120 when applicable. In some embodiments, the Model Interference module 1130 may be also responsible for data preparation (e.g., data preprocessing and cleaning, formatting, and transformation) based on the inference data 1114 delivered by the Data Collection module 1110, if needed or required. The model performance feedback 1132 may be used by the Model Training module 1120 to monitor the performance of the AI / ML model, when available. The output 1135 may comprise the inference output of the AI / ML model after being applied by the Model Interference module 1130. In some implementations, the operation of the Model Interference module 1130 can be implemented in a distributed manner across the wireless communication network 106 including processors for both internal (UE, RAN, MEC, core) and external (customer processors connected via the wireless communication network 106) to a wireless communication network instance in accordance with 3GPP standard specifications (e.g., 3GPP TR 37.817 V17.0.0 (2022-04) is incorporated herein in its entirety).

[0155] In some implementations, the Actor module 1140 receives the output 1135 from the Model Interference module 1130 and triggers or performs corresponding actions. For example, the corresponding actions may include update of the control law of the controller as discussed above.

[0156] The Actor module 1140 may trigger actions directed to other entities or to itself. The feedback 1045 may include information that may be needed to derive additional training data 1112, additional inference data 1114 or to monitor the performance of the AI / ML model and its impact to the network through updates of key performance indicators (KPIs) and performance counters.

[0157] In some implementations, a trained AI / ML model can mimic cognitive functions that humans associate with other human minds. In particular, by training based on enough training data, the trained AI / ML model is able to adapt to new circumstances and to detect and extrapolate patterns.

[0158] In some implementations, parameters of a trained AI / ML model can be adapted by means of training. In particular, a combination of supervised training, semi-supervised training, unsupervised training, reinforcement learning and / or active learning can be used. Furthermore, representation learning (an alternative term is “feature learning”) can be used. In particular, the parameters of the trained AI / ML model can be adapted iteratively by several steps of training.

[0159] In some implementations, a trained AI / ML model can comprise a neural network, a support vector machine, a decision tree and / or a Bayesian network. The trained AI / ML model can be based on k-means clustering, Q-learning, genetic algorithms and / or association rules. In particular, a neural network can be a deep neural network, a convolutional neural network, or a convolutional deep neural network. Furthermore, a neural network can be an adversarial network, a deep adversarial network and / or a generative adversarial network.

[0160] Fig. 12 illustrates an example of a computational graph of a Neural Network (NN) in accordance with one or more implementations.

[0161] In some implementations, an Artificial Intelligence / Machine Learning (AI / ML) system (e.g., AI / ML system 902, AL / ML system 1100) may be implemented as Neural Networks (NNs), but is not limited thereto. A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In some implementations, the AI / ML system may use Neural Network Exchange Format (NNEF) to exchange information about trained neural networks. The goal ofNNEF is to provide a standard platform for connecting accelerated neural network execution engines and available deep learning tools. Ideally, neural networks trained in deep learning frameworks would be exported to NNEF, and neural network accelerator libraries could consume it without worrying about compatibility with all deep learning frameworks. For an application programmer, NNEF is a standardized way to store and transfer neural networks. Given a neural network in NNEF, and a driver or library that is able to import it, the application programmer need not worry about where the network came from or what kind of underlying hardware will execute it, as long as it has the capabilities to do so. The application programmer may query the driver of the underlying hardware whether it is capable of executing the given network.

[0162] As shown in Fig. 12, the computational graph 1200 describes the NN. The computational graph is a directed graph that has two types of components: data nodes (nodes correspond to variables) and operation nodes (nodes correspond to operations). The data nodes can feed their values representing multi-dimensional arrays (such as vectors, matrices, or arrays of higher dimension)) into operation nodes, and operation nodes can feed their output into other operation nodes. This way, every node in the computational graph 1200 defines a function of the variables. The data nodes are represented as squares 1210, 1212, 1214, 1214-1, 1214-5, 1216 and the operation modes are represented as ellipses 1220, 1220-1, 1220-2 in Fig. 12. In some implementations, the squares 1210, 1212, 1214, 1214-1, 1214-5, 1216 denote the values (E: external 1210, C: constant 1212, T: regular tensor 1214, 1214-1, 1214-5, V: variable 1216) which are fed into the operation nodes and / or come out of the operation nodes. The values are called as tensors or tensor data. The computational graph 1200 starts from data nodes (e.g., squares 1210) that represent externally provided data or constant or variable tensors internal to the computational graph 1200. In order to make the description uniform, such data nodes are the results of special tensor introducing operations (e.g., ellipses 1220). Thus, the whole computational graph 1200 is described by a set of operations (e.g., ellipses 1220), interconnected by the data nodes (e.g., squares 1210).

[0163] The computational graph 1200 in Fig. 12 depicts a linear graph describing a simple multi-layer feedforward network as an example of the NN, but is not limited thereto, starting from an input data node (e.g., squares 1210). Each layer corresponds to an operation node (e.g.,an ellipse 1220-1) producing a new intermediate data node (e.g., a square 1214-1). The operation node (e.g., an ellipse 1220-2) may take intermediate data represented by the new intermediate data node (e.g., a square 1214-1) as input, and finally produces some output data (e.g., a square 1214-5). A directed edge (e.g., an arrow 1230) from a data node (e.g., the square 1210) to the operation node (e.g., the ellipse 1220) represents that operation represented by the operation node may take corresponding data represented by the data node as its input. A directed edge (e.g., an arrow 1240) from an operation node (e.g., the ellipse 1220) to a data node (e.g., the square 1214) means that operation represented by the operation node may produce corresponding data represented by the data node as its output. Edges from data node to data node or from operation node to operation node are not allowed.

[0164] The components of the computational graph 1200 may reside within remotely distributed processing elements of the wireless communication network 106. For example, the components of the computational graph 1200 may be distributed over a specific instance of the wireless communication network 106 and within a UE, RAN, multi-access edge computing (MEC), Core, and Cloud processors so that the input data and operations are positioned and interconnected in the wireless communication network 106 and over Time Sensitive Network (TSN) flows. Remote Direct Memory Access (RDMA) over Converged Ethernet (RoCE) is a network protocol that leverages Remote Direct Memory Access (RDMA) capabilities to accelerate communications between applications hosted on clusters of servers and storage arrays and may comprise interconnections. TSN over RDMA is an anticipated possibility for better coordination of when messages arrive, and operations occur. Thus, the computational graph 1200 requires attributes that denote a network location of each component and communication channel interconnecting the components as described in Neural Network Exchange Format, the Khronos NNEF Working Group, Version 1.0.5, Revision 1, 2022-02-16 which is incorporated herein in its entirety. In some implementations, the computational graph 1200 has an associated graphexecution time, which is analogous to run-time for programming languages. When many such computational graphs are integrated, efficiency will necessitate that data arrives at operators simultaneously so that data input to an operator is not blocked waiting for the other input data to arrive. This synchronization problem becomes acute over longer channels such as the wireless communication network (e.g., 5G, 5G+, etc.) interconnections among operations. As discussedabove, the TSN system is provided to ensure that all data arrives for processing at operation nodes at the optimal time. In some implementations, the computation graph can be provided as an additional input to a TSN scheduler (e.g., configuration module 215).

[0165] Fig. 13 illustrates a flow diagram of example operations of a control system in accordance with one or more implementations.

[0166] A flow diagram 1300 illustrates operations of a control system (the control system 900) as discussed above. In some implementations, the operations of the control system can be implemented by a device for sending a control signal (e.g., the control input 912) via a communication network (e.g., the wireless communication network 106) configured to support deterministic communication based on a time-sensitive network (TSN) mechanism. In some implementations, the device may include a processor and a memory communicatively coupled with the processor. In some implementations, one or more instructions stored in the memory which when executed by the processor cause the processor to perform following operations as shown in the flow diagram 1300.

[0167] The operations include operation 1310 to send quality of service requirement information for transmission of the control signal to an end device (e.g., the end device 102). As discussed in Fig.10, the operation 1310 may be performed by a control application host (e.g., the operation 1010). The quality of service requirement information may include data path requirement information as Network slice SLA to a wireless communication network 106 and instantiates provisioning using network functions 126 such as AMF and SMF and TSN-AF 124. In some implementations, the quality of service requirement may include TSN gate control. In some implementations, the quality of service requirement information includes computational latency information and communication latency information required for the transmission of the control signal. In some implementations, the wireless communication network 106 provides network functions 126 to configure data path provision for transmission of a control signal generated by a controller based on a control law based on the data path requirements. In some implementations, the control signal is configured to control operation of the end device in a system associated with the communication network. As discussed above, the control signal isgenerated based on a control law of a controller to control operations of end devices (e.g., a sensor, an actuator, etc.).

[0168] The operations include operation 1320 to poll components of the communication network for operating parameter information related to operating parameters of the communication network. As discussed in Fig.10, the operation 1320 may be performed by an AI / ML control application 910 (e.g., the operations 1020 and 1030). In some implementations, the operating parameter information related to operating parameters of the communication network includes network status information for a bandwidth, latency and forecasted load along the data path to the end device.

[0169] The operations include operation 1330 to send the control signal based on the quality of service requirement information to the end device. In some implementations, the communication network may configure a data path for sending the control signal based on the quality of service requirement. The control signal is based on a control law. As discussed in Fig.10, the operation 1330 may be performed by the AI / ML control application 910 (e.g., the operations 1040).

[0170] The operations include operation 1340 to receive operating parameter information from the components of the communication network in response to the poll for operating parameters, and receive feedback information (e.g., the feedback information 935) from the end device. As discussed in Fig.10, the operation 1340 may be performed by the AI / ML control application 910 (e.g., the operations 1050).

[0171] The operations include operation 1350 to update, based on an Artificial Intelligence (Al) function, the control law of the device for the transmission of the control signal based on the operating parameter information and feedback information to compensate for delays in transmission of the control signal. As discussed in Fig.10, the operation 1350 may be performed by an AI / ML system 920 (e.g., the operations 1060).

[0172] As discussed in Fig. 10, in some implementations, the operations further include operation to generate new data path requirement based on the updated control law by the AI / ML control application 910 (e.g., the operations 1070).

[0173] As discussed above, in some implementations, the communication network includes: a plurality of communication network (CN) components, e.g., components 110, 118, 120, 122, 124, 126, 140, 190, etc. Each CN component configured to provide a discrete functionality forthe transmission of control signal across the communication network to the end device. In some implementations, at least one of the plurality of CN components is configured to support timesensitive deterministic communication as a TSN block in a TSN network in accordance with a TSN schedule of the TSN mechanism.

[0174] As discussed above, in some implementations, the communication network includes: a TSN configuration module (e.g., the TSN scheduler, the configuration module 215) is configured to update the TSN schedule based on the updated control law. Each of the plurality of CN components is configured to as the TSN block with the updated TSN schedule.

[0175] In some implementations, the communication network includes a wireless network configured in accordance with a 3 GPP-defined standard.

[0176] In some implementations, a Neural Network Exchange Format (NNEF) is used in the Al function as discussed in Fig. 12.

[0177] As discussed above, in some implementations, the Al function is distributed and implemented over resources of the communication network. In some implementations, the resources may include a User Equipment (UE), Radio Access Network (RAN), Multi-access edge computing (MEC), Core and Cloud processors.

[0178] As discussed in Fig. 11, in some implementations, the Al function is provided by: a data collection module (e.g., the Data Collection Module 1110) to collect input data including the information related to operating parameters of the communication network and the feedback information from the end device and generate training data and inference data based on the input data; a model training module (e.g., the Model Training Module 1120) to train an Al model based on the training data; a model inference module (e.g., the Model Inference Module 1130) to use the trained Al model to predict data based on the inference data; and an actor module (the Actor Module 1140) to output feedback data based on the predicted data.

[0179] The operations discussed above may periodically loop to adopt to change.In some implementations, AI / ML trustworthiness metrics can be used. The AI / ML trustworthiness metrics may be an indicator of how much one would trust the control system, which may significant for life-critical control systems such as cars and airplanes. Trust in a control system may be determined from performance feedback metrics, such as variation around the target value, rise time, settling time, and percent overshoot. Trust would depend upon how these metrics change as a function of different inputs and environmental conditions. Thus, theoperations depicted in Fig. 13 may be applied to 3GPP-defined real-time control scenarios such as "Al-assisted signaling," predicting wireless channel state, MIMO beam management, and localization which are provided by following study items in 3GPP: SA working group 1 (SAI): Study item and work item on AI / ML model transfer in 5G; SA working group 2 (SA2): Study item on 5G system support for AI / ML-based services; SA working group 3 (SA3): Study item on security and privacy of AI / ML-based services and applications; SA working group 4 (SA4): Study item on AI / ML for media; SA working group 5 (SA5): Study item on AI / ML management; RAN working group 3 (RAN3): Work item on AI / ML for next-generation RAN (NG-RAN); and RAN working groups 1, 2, and 4 (RANI, RAN2, and RNA4): Study item on AI / ML for new radio (NR) air interface.

[0180] Fig. 14 illustrates block diagrams of an example control system in accordance with one or more implementations.

[0181] As discussed in Fig. 13, the control system can be implemented by a device 1400 for sending a control signal (e.g., the control input 912) via a communication network (e.g., the wireless communication network 106) configured to support deterministic communication based on a time-sensitive network (TSN) mechanism. As shown in Fig. 14, the device 1400 includes a processor 1410 and a memory 1420 communicatively coupled with the processor 1410. As discussed in Fig. 13, one or more instructions stored in the memory 1420 which when executed by the processor 1410 cause the processor 1410 to perform following operations as shown in the flow diagram 1300. The processor 1410 may be a microprocessor or multi-core processor, an integrated circuit, a field programmable gate array, etc. that executes instructions (stored in memory device 1420, for example) to update, based on an Artificial Intelligence (Al) function, a control law of the device 1400. The memory 1420 stores 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).

[0182] In some implementations, a device (e.g., the device 1400) for sending a control signal via a communication network configured to support time-sensitive deterministic communication based on a time-sensitive network (TSN) mechanism, comprises: a processor (e.g., the processor 1410); and a memory (e.g., the memory 1420) communicatively coupled with the processor; and one or more instructions stored in the memory which when executed by the processor cause theprocessor to: send quality of service requirement information for transmission of the control signal to an end device, the control signal configured to control operation of the end device in a system associated with the communication network; poll components of the communication network for operating parameter information related to operating parameters of the communication network; send the control signal based on the quality of service requirement information, wherein the control signal is based on a control law; receive operating parameter information from the components of the communication network in response to the poll for operating parameters, and receive feedback information from the end device; and update, based on an Artificial Intelligence (Al) function, the control law of the device for the transmission of the control signal based on the operating parameter information and feedback information to compensate for delays in transmission of the control signal.

[0183] In some implementations, a method for sending a control signal via associated with a communication network configured to support time-sensitive deterministic communication based on a time-sensitive network (TSN) mechanism, comprises: sending quality of service requirement information for transmission of the control signal to an end device, the control signal configured to control operation of the end device in a system associated with the communication network; polling components of the communication network for operating parameter information related to operating parameters of the communication network; sending the control signal based on quality of service requirement information to the end device, wherein the control signal is based on a control law; receiving operating parameter information from the components of the communication network in response to the poll for operating parameters, and receive feedback information from the end device; and updating, based on an Artificial Intelligence (Al) function, the control law of the device for the transmission of the control signal based on the operating parameters information and feedback information to compensate for delays in transmission of the control signal.

[0184] In some implementations, a non-transitory computer readable medium storage configured to one or more instructions which, when executed by a processor, cause the processor to perform the method described above.

[0185] 3GPP-specificed components may be the plant (target element controlled by the control system). As one example, the 3GPP-specificed RAN may be controlled in such a manner as to provide deterministic operation (reduce frame delay variation and jitter). Specifically, Technical Specification (TS) 38.322 Radio Link Control (RLC) protocol specification-defined parameters controlling: Transfer of upper layer PDUs, Sequence numbering , Error Correction through ARQ, Segmentation and re-segmentation of RLC SDUs, Reassembly of SDU, Duplicate Detection, RLC SDU discard, RLC re-establishment, and error detection may be dynamically adjusted during operation to yield stable, periodic latency as requested by the TSN scheduler for specific TSN flows through the 3 GPP system

[0186] 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.

[0187] 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.

[0188] 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 genericprinciples 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.

[0189] 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.

[0190] 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.

[0191] 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 viceversa. 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.

[0192] 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 of ordinary 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 device for sending a control signal via a communication network configured to support time-sensitive deterministic communication based on a time-sensitive network (TSN) mechanism, comprising: a processor; and a memory communicatively coupled with the processor, the memory storing one or more instructions that, when executed by the processor, cause the processor to: send quality of service requirement information for transmission of the control signal to an end device, the control signal configured to control operation of the end device in a system associated with the communication network; poll components of the communication network for operating parameter information related to operating parameters of the communication network; send the control signal based on the quality of service requirement information to the end device, wherein the control signal is based on a control law; receive operating parameter information from the components of the communication network in response to the poll for operating parameters, and receive feedback information from the end device; and update, based on an Artificial Intelligence (Al) function, the control law of the device for the transmission of the control signal based on the operating parameters information and feedback information to compensate for delays in transmission of the control signal.

2. The device according to claim 1, wherein the processor is further configured to: generate new data path requirement based on the updated control law.

3. The device according to claim 1, wherein the quality of service requirement information includes computational latency information and communication latency information required for the transmission of the control signal.

4. The device according to claim 1, wherein the operating parameter information related to operating parameters of the communication network includes network status information for a bandwidth, latency and forecasted load along the data path to the end device.

5. The device according to claim 1, wherein the communication network includes: a plurality of communication network (CN) components, each CN component configured to provide a discrete functionality for the transmission of control signal across the communication network to the end device, at least one of the plurality of CN components configured to support time-sensitive deterministic communication as a TSN block in a TSN network in accordance with a TSN schedule of the TSN mechanism.

6. The device according to claim 5, wherein the communication network includes: a TSN configuration module configured to update the TSN schedule based on the updated control law, each of the plurality of CN components is configured to as the TSN block with the updated TSN schedule.

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

8. The device according to claim 1, wherein the Al function is distributed and implemented over resources of the communication network.

9. The device according to claim 8, wherein the resources include a User Equipment (UE), Radio Access Network (RAN), Multi-access edge computing (MEC), Core and Cloud processors.

10. The device according to claim 1, wherein a Neural Network Exchange Format (NNEF) is used in the Al function.

11. The device according to claim 1, wherein the Al function is provided by: a data collection module to collect input data including the information related to operating parameters of the communication network and the feedback information from the end device and generate training data and inference data based on the input data; a model training module to train an Al model based on the training data; a model inference module to use the trained Al model to predict data based on the inference data; and an actor module to output feedback data based on the predicted data.

12. A method for sending a control signal via associated with a communication network configured to support time-sensitive deterministic communication based on a timesensitive network (TSN) mechanism, comprising: sending quality of service requirement information for transmission of the control signal to an end device, the control signal configured to control operation of the end device in a system associated with the communication network; polling components of the communication network for operating parameter information related to operating parameters of the communication network; sending the control signal based on the quality of service requirement information to the end device, wherein the control signal is based on a control law; receiving operating parameter information from the components of the communication network in response to the poll for operating parameters, and receive feedback information from the end device; and updating, based on an Artificial Intelligence (Al) function, the control law of the device for the transmission of the control signal based on the operating parameter information and feedback information to compensate for delays in transmission of the control signal.

13. The method according to claim 12, comprising: generating new data path requirement based on the updated control law.

14. The method according to claim 12, wherein the information related to the data path requirement includes computational latency information and communication latency information required for the transmission of the control signal.

15. The method according to claim 12, wherein the information related to operating parameters of the communication network includes network status information for a bandwidth, latency and forecasted load along the data path to the end device.

16. The method according to claim 12, wherein the communication network includes: a plurality of communication network (CN) components, each CN component configured to provide a discrete functionality for the transmission of control signal across the communication network to the end device, at least one of the plurality of CN components configured to support time-sensitive deterministic communication as a TSN block in a TSN network in accordance with a TSN schedule of the TSN mechanism.

17. The method according to claim 16, wherein the communication network includes: a TSN configuration module configured to update the TSN schedule based on the updated control law, each of the plurality of CN components is configured to as the TSN block with the updated TSN schedule.

18. The method according to claim 12, the communication network includes a wireless network configured in accordance with a 3GPP-defmed standard.

19. The method according to claim 12, wherein the Al function is distributed and implemented over resources of the communication network.

20. A non-transitory computer readable medium storage storing one or more instructions that, when executed by a processor, cause the processor to:send quality of service requirement information for transmission of the control signal to an end device, the control signal configured to control operation of the end device in a system associated with the communication network; poll components of the communication network for operating parameter information related to operating parameters of the communication network; send the control signal based on the quality of service requirement information to the end device, wherein the control signal is based on a control law; receive operating parameter information from the components of the communication network in response to the poll for operating parameters, and receive feedback information from the end device; and update, based on an Artificial Intelligence (Al) function, the control law of the device for the transmission of the control signal based on the operating parameter information and feedback information to compensate for delays in transmission of the control signal.

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