Time synchronization compensation in a communication network

Abstract

Systems, methods, techniques, and other embodiments are disclosed to compensate, by applying an artificial intelligence / machine learning (AI / ML) model, a time synchronization error in a communication network, e.g. a wireless network. In one example, a system associated with the communication network configured to support time-sensitive deterministic communication, comprises a processor configured to; obtain time synchronization data of a plurality of communication network (CN) components in the communication network, wherein the time synchronization data indicates a time synchronization error in the communication network; apply an artificial intelligence (Al) model on the time synchronization data to adjust at least one control parameter; and compensate for the time synchronization error in the communication network using the adjusted at least one control parameter.

Description

TIME SYNCHRONIZATION COMPENSATION IN A COMMUNICATION NETWORKCROSS-REFERENCE TO RELATED APPLICATION

[0001] The present disclosure claims benefit to U.S. Provisional Application Serial No. 63 / 583,809, entitled “Time Synchronization Compensation in a Communication Network,” 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 communication network and, more particularly, for example, to time synchronization compensation in a communication network supporting time-sensitive deterministic communication by applying an artificial intelligence (Al) model.BACKGROUND

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

[0004] For time synchronization within a network using TSN, knowledge of current time is required for every entity or component in the network. This includes all network devices as well as all hosts and processors that desire to participate in TSN scheduled activities. Different oscillators may have different time stabilities and sensitivities to their environment, which could cause time synchronization error and degrade communication quality. But there is no effective and efficient method for minimizing time synchronization error throughout the network.BRIEF DESCRIPTION OF THE DRAWINGS

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

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

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

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

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

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

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

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

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

[0014] Fig. 9 illustrates a diagram of a communication network using an AI / ML -based system for time synchronization compensation, in accordance with one or more implementations.

[0015] Fig. 10 illustrates an example of operations of an AI / ML-based time synchronization compensation system, in accordance with one or more implementations.

[0016] Fig. 11 illustrates a flow chart showing a method for compensating time synchronization error in a communication network, in accordance with one or more implementations.DETAILED DESCRIPTION

[0017] 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.

[0018] 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 transmissionpaths 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.

[0019] 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 each configuration module may be responsible to communicate with, and configure, one or more TSN blocks.

[0020] 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.

[0021] 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 components to affect the operation thereof. A controller module may include any known processor, microcontroller, or logic device, including, but not limited to: field programmable gate arrays (FPGA), an application specific integrated circuit (ASIC), a full authority digital engine control (FADEC), an aviation system, a proportional controller (P), a proportional integral controller (PI), a proportional derivative controller (PD), a proportional integral derivative controller (PID controller), a hardware-accelerated logic controller (e.g. for encoding, decoding, transcoding, etc.), the like, or a combination thereof.

[0022] 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 aNE with minimum human intervention. Thiscan 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.

[0023] 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 or channels. The various components of the network include modules and channel s / links implemented in hardware, software, and in a combination of hardware and software.

[0024] 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.

[0025] 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 transportnetwork 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.

[0026] 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.

[0027] In some implementations, the present disclosure provides a communication network, e.g., a wireless network such as a 5G network, includes a plurality of configuration modules interconnected in a certain arrangement and configured to provide a unique set of TSN parameters to each of the plurality of CN components such that each of the plurality of CN components supports time-sensitive deterministic communication as a corresponding TSN block in a TSN network in accordance with the respective unique set of TSN parameters. The plurality of configuration modules may be interconnected in a hierarchical arrangement, a mesh arrangement, a star arrangement, a tree arrangement, or a combination thereof.

[0028] 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 accordancewith 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.

[0029] A wireless communication network in accordance with 3 GPP-defined specifications (e.g., 5G, 6G, etc.) and an artificial intelligence / machine learning (AI / ML) system may be combined to improve time synchronization performance, e.g. by adjusting time, frequency, or PID controller parameters to compensate for time synchronization error. The AI / ML system is a system utilizing an AI / ML model, which can be applied to time synchronization over TSN mechanism supported by the wireless communication network, e.g. by addressing and controlling issues such that mobility, temperature, and other environmental factors that could impact latency and TSN determinism. An AI / ML model or Al model here refers any model based on machine learning, neural network, support vector machine, decision tree and / or Bayesian network. In some embodiments, the AI / ML system can consistently monitor one or more measurements related to environmental conditions, e.g., vibration, temperature, etc., in order to determine how to compensate for their impacts on changes to TSN message latency.

[0030] In some implementations, the present disclosure provides a system associated with a communication network configured to support time-sensitive deterministic communication based on a TSN mechanism. The system comprises a processor configured to: obtain time synchronization data of a plurality of communication network (CN) components in the communication network, wherein the time synchronization data indicates a time synchronization error in the communication network; apply an artificial intelligence (Al) model on the time synchronization data to adjust at least one control parameter; and compensate for the time synchronization error in the communication network using the adjusted at least one control parameter.

[0031] 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 TSNsystem 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. IQbu), scheduled traffic (802.1Qbv), and redundancy management (Frame Replication and Elimination for Reliability (FRER) IEEE 802.1CB). These standards work together at the Ethernet layer-2 to ensure that control and safety functions are executed while meeting their respective deadlines and constraints. As another implementation, similar integrated system may be configured to implement TSN techniques over a wireless local area network (wireless LAN) such as a Wi-Fi network, e.g., based on Wi-Fi 6 and other common wireless LANs.

[0032] 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. I Q that are used to prioritize different types of data traffic.

[0033] 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 aredefined 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.

[0034] 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 (802.1Qci) improves reliability by protecting against bandwidth violation, malfunctioning and malicious behavior. Further, the time synchronization in the TSN system may be defined by the 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).

[0035] 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 isdiscussed in U.S. Application No. 17 / 100,356, which is incorporated herein in its entirety by reference.

[0036] Time-critical communication between end devices or nodes (e.g., the I / O devices 102 and the controller 104) in TSNs includes “TSN flows” also known as “data flows” or simply, “flows.” For example, data flows can comprise datagrams, such as data packets or data frames. Each data flow is unidirectional, going from a first originating or source end device (e.g., the 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 be between the interconnected devices. For the set of data flows, various subsets or permutations of the dataflows can additionally be defined. Further, time-critical communication between end devices or nodes in TSNs includes “TSN streams” or “streams,” where each TSN stream may originate at a specific talker node intended to be communicated to one or more listener nodes. As such, each TSN stream may include one or more data flows, where each data flow is between the talker node (where the TSN stream originated) and a listener node.

[0037] 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.

[0038] 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.

[0039] TSN traffic may be tagged using a variety of mechanisms, including VLAN tag Ethernet address IP header information, and a combination of VLAN tag Ethernet address and IP header information. Traffic may be identified and tagged anywhere in the system before protocol data unit (PDU) identification is required. A TSN Talker may create multiple TSN flows (streams) with different TSN latency and determinism requirements and may be assigned different paths that meet the requirements. In some 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.

[0040] 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.

[0041] 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, medicalimaging 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.

[0042] In some implementations, the controller 104 can communicate a variety of data between or among the I / O end devices 102 via the TSN 100. For example, the control system 104 can communicate the command data to one or more of the devices 102 or receive data, such as status data or sensor data, from one or more of the devices 102. Accordingly, the controller 104 may be configured to control operations of the I / O devices 102 based on data obtained or generated by, or communicated among the I / O devices 102 to allow for, e.g., automated control of the VO devices 102 and provide information to operators or users of the I / O devices 102. The controller 104 may define or determine the data flows and data flow characteristics in the TSN system 100.

[0043] 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, vl7.1.1 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 5Guser 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.

[0044] 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.

[0045] 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.

[0046] 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.

[0047] For the 5G system 106 to be integrated into the TSN system 100, requirements of a TSN stream can be fulfilled only when resource management allocates the network resources for each hop along the whole path. In line with TSN configuration (802.1Qcc), this is achieved through interactions between the 5G system 106 and a configuration controller, e.g., a centralized configuration controller 110 (including the CUC 114 and the CNC 112) and / or a set of decentralized controller modules (e.g., as discussed below with respect to Fig. 2). The interface between the 5G system 106 and the CNC allows for the CNC 112 to learn the characteristics of the 5G virtual bridge, and for the 5G system 106 to establish connections with specific parameters based on the information received from the CNC 112. Bounded latency requires deterministic delay from 5G as well as QoS alignment between the TSN and 5G domains. For instance, if a 5G virtual bridge acts as a TSN bridge, then the 5G system 106 emulates time-controlled packet transmission in line with Scheduled Traffic per 802.1Qbv for example. For the 5G control plane, the TT in the AF 124 receives the transmission time information of the TSN traffic classes from the CNC 112. In the 5G user plane, the TT at the UE 118 and the TT at the UPF 122 may regulate the time-based packet transmission accordingly. The different TSN traffic classes may be mapped to different 5G QoS Indicators (5QIs) in the AF 124 and the PCF 126 as part of the QoS alignment between the TSN and 5G domains, and the different 5QIs are treated according to their QoS requirements.

[0048] 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.

[0049] 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-5Gsystem 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, one or more of the configuration modules 215-a to 215-f may be implemented within or as part of a function (e.g., SMF 126) of the control plane of the 5G system 106. As used herein, a “topology” can refer to one or more arrangement(s) of a network 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.

[0050] 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 thecontroller 104. Although not shown, the system 200 may also include TSN bridges 108 and other TSN components.

[0051] 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.

[0052] 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.

[0053] 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 parameter(s) may include at leastpacket error rate. The link bandwidth parameters) may include at least the available bandwidth in bits per second.

[0054] 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.

[0055] In some implementations, the set of parameters for a TSN block 202 may define a worst case time synchronization error, a worst case gate operation error, a maximum gate control list size, a maximum cycle time, a maximum gate interval duration, a transmission start delay, the like, or a combination thereof. The set of parameters may include a discrete set of deterministic parameters that may vary by the type of traffic handled by the TSN block 202. The set of parameters may be at least partially utilized to generate a TSN Schedule, configuration, or the like, that is realizable on the hardware of the TSN block 202. In the absence of such set of parameters, a TSN scheduling module has to take a lowest common denominator approach, wherein all devices of the system 200 including TSN blocks 202 are assumed to have the most limiting characteristics resulting in a sub-optimal solution. In this sense, the set of parameters enable better scheduling solution in the TSN system 200 resulting in improved performance metrics including latency jitter, packet delay variation, and bandwidth utilization.

[0056] 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 specificconfiguration 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.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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 usingclassical 802.1Qbv style gate control, the UE 118 corresponding to the TSN block 202-1 may use a phase offset (in reference to a cycle time) to align the transmission of a TSN data stream with the assigned schedule. In this example, instead of the UE 118 getting this phase offset from the CNC, the UE 118 may get the phase offset as a special command from the RAN 120.

[0063] 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.

[0064] Referring back to Fig. 2, the subject technology provides for a plurality of distributed configuration modules 215 in a distributed controller 210 in the TSN-5G system 200. The configuration modules 215-a to 215-f may be interconnected in one or more topologies (mesh, star, tree) and each configuration module (CM) 215 may be responsible to communicate with and configure one or more TSN blocks 202. For example, the CM 215-a may be responsible for and operatively and communicatively connected to the TSN blocks 202-1 and 202-2. The CM 215-b may be responsible for and operatively and communicatively connected to the TSN blocks 202-3. The CM 215-c may be responsible for and operatively and communicatively connected to the TSN blocks 202-3 and 202-N. As such, the TSN block 202-3 may be configured and controlled by both the CM 215-b and the CM 215-c. For example, a portion of functionalities (e.g., with respect to a first type of TSN applications) at the TSN block 202-3 may be configured and controlled by the CM 215-b, and another portion of functionalities (e.g., with respect to a second type of TSN applications) at the TSN block 202-3 may be configured and controlled by the CM 215-c.

[0065] 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 levelhigher 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.

[0066] 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 of the configuration modules 215 may be configured as the CNC 112 and / or the CUC 114 of the TSN controller 110 discussed above. In some implementations, one or more of the configuration modules 215-a to 215-f may be implemented within or as part of a function (e.g., SMF 126) of the control plane and / or an element or function (e.g., the 5G transport network 202-3) of the user plane of the 5G system 106. For example, the SMF 126 may be configured to support functionalities of the CUC 114 and / or the CNC 112. In some implementations, the 5G transport network 202-3 may be configured to support functionalities of the CNC 112.

[0067] 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.

[0068] 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 modulo theory (SMT) solver may be used, wherein the data flows and their requirements are expressed as constraints and a linear programming methodology is used to solve for a feasible solution. The solution from a lower level of the CM tree is input as used resources (represented again by constraints) at the one-level higher on the CM tree. This process is repeated until the top-most level of the CM tree is reached, where a global solution is determined.

[0069] 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 thecorresponding 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.

[0070] 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.

[0071] 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, networkslices 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.

[0072] 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 respectivedata 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).

[0073] In a conventional TSN system, the TSN schedule is expressed as an absolute time offset in a periodic cycle at which a TSN block is instructed to transmit that data. However, that may be too restrictive for a 5G system 106 composed of components from multiple vendors. In some implementations, a deadline-based schedule is determined and provided with the configuration data by a CM 215. The deadline-based schedule may instruct the TSN block 202 to transmit the data of a configured data flow no later than a deadline (which is expressed in terms of absolute time in a periodic cycle). In some implementations, the delay budget based approach would instruct the TSN block to transmit the data frame of a configured data flow within a delay budget. As such, under the delay budget based approach, the TSN block 202 is required to send a data frame arriving at its ingress port within a certain duration to its egress port. Such a scheme would not require every TSN block 202 to be time synchronized. In some implementations in which the TSN block 202 is configured under a rate constraint, the TSN block 202 is configured to transmit the data frames of a given data flow in a manner that the average or peak transmission rate (in bits per second) does not exceed a configured value (per the configuration data from the CM 215).

[0074] 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 profilemay exist. Device specific operating / required resource sharing mode may be available to the CNC 112 through TSN-AF.

[0075] 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.

[0076] In some implementations, with respect to scheduled transmissions between the TSN block 202-1 (corresponding to the UE 118) and the TSN block 202-2 (corresponding to the RAN 120) may involve “enhanced” allocation (assignment and transmission) of uplink and downlink transmissions between the UE 118 and the RAN 120 in order meet the schedule assigned by the CM 215-a to the TSN block 202-1 corresponding to the UE 118. In some implementations, the CM 215-a may take into account 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.

[0077] 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 / deterministic uplink and downlink slots for a given UE 118 may be made such that, e.g., all UEs connected to a given RAN slice are given a scheduled transmission slot. 5G native over- the-air scheduling may be used to determine if the transmission from the UE 118 will meet the transmission deadline. If not, the UE 118 may request elevated access to the RAN 120 to achievethe 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.

[0078] 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. In this case, the 5G scheduler differs from a “traditional” TSN Ethernet port in that multiple messages may egress simultaneously if transmitted on different frequencies. In some aspects, under the presence of poor RF channel conditions, the 5G system 106 may send multiple copies of a messages over different frequencies in order to increase the probability of meeting the transmission schedule and / or deadline.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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 underlying network (e.g., by providing explicit routes and congestion protection) to DetNet flows and encapsulating the TSN Ethernet frames. DETNET routing incorporates IP headers are modified per standard router behavior, e.g., time-to-live (TTL handling), where TTL specifies the maximum time the routable IP message is allowed to survive, clearly related to the TSN maximum latency requirement.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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 complexityof 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 leaky bucket and may use any number of optimization techniques or network calculus to determine feasible schedules. Thus, a complete flow for a TSN application will include not only an end-to- end path through the network for a specific message, but also the complete processing path through all computational TSN blocks (microservices) acting on the information in the message. IEEE 802.1CB redundant paths can be configured through either redundant or parallel computational TSN blocks (microservices). One should be able to visualize the complete realtime processing activity encoded in the TSN schedule in 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.

[0088] 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 todynamically 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.

[0089] A TSN application may transmit a timing performance profile-query microservice request with the goal of determining statistical information regarding processing time. When responding to such a request, the microservice will execute the request and return both the result and a timing performance profile. The timing performance profile may contain at least the network start and end time of the microservice call, and may optionally contain the start-time and end-time of all called subfunctions. The information may also contain an average of the MEC processor load during the timing performance profile. The timing performance profile may be used by the TSN application to refine TSN scheduling where microservices are required to process TSN traffic flow. The timing performance profile may be obtained during live operation, where output from the profile is used normally, or as a special test sample prior to live operation. The timing performance profile information may be used to determine which MEC hardware to employ for current and future operations and as constraint information for the TSN scheduler.

[0090] 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 example of YANGscheduled operation).

[0091] 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 schedulerwhich 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.

[0092] 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 the TSN schedule. The service is part of the delay, but it may have more variance than a communication link. In such an implementation, the service is the link in this integration of microservices with TSN scheduling. Network calculus may be used to incorporate 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).

[0093] 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 synchronizedtime 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.

[0094] 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 802.1 Qcw 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 MEC shall support identification of data streams in collaboration with the OS and applications. The specific functionality varies depending on the TSN awareness of MEC components. TSN unaware applications on a MEC host requires a IP stream identification in a MEC bridge.

[0095] TSN fronthaul / backhaul (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 MECplatforms 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.

[0096] 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.

[0097] Referring to Fig. 8, an application data stream may span multiple integrated TSN-5G systems 805 and 810. In this case, TSN blocks may be geographically distributed interconnecting more than one 5G systems using a TSN compatible transport 820 including but not limited to a 5G backbone, private network tunnel or other wide area networks. In 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.).

[0098] 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.

[0099] 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.

[0100] The ROM 710 stores static data and instructions that are needed by the one or more processing unit(s) 712 and other modules of the electronic system 700. The permanent storage device 702, on the other hand, may be a read-and-write memory device. The permanent storage device 702 may be a non-volatile memory unit that stores instructions and data even when the electronic system 700 is off. In one or more implementations, a mass-storage device (such as a magnetic or optical disk and its corresponding disk drive) may be used as the permanent storage device 702.

[0101] 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.

[0102] 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 714may 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.

[0103] Finally, as shown in FIG. 7, the bus 708 also couples the electronic system 700 to one or more networks and / or to one or more network nodes through the one or more network interface(s) 716. In this manner, the electronic system 700 can be a part of a network of computers (such as a 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.

[0104] 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.

[0105] 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-readablemedia 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.

[0106] While the above discussion primarily refers to microprocessor or multi-core processors that execute software, some implementations are performed by one or more integrated circuits, such as application specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs). In some implementations, such integrated circuits execute instructions that are stored on the circuit itself.

[0107] 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.

[0108] 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 witha 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.

[0109] Aspects of the subject matter described in this specification can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the subject matter described in this specification, or any combination of one or more such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a 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).

[0110] 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 setof 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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 ofCN 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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 the second 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] As discussed above, a wireless communication network in accordance with 3GPP- defined specifications (e.g., 5G, 6G, etc.) and an artificial intelligence / machine learning (AI / ML) system may be combined to improve time synchronization performance, e.g. by adjusting time, frequency, or PID controller parameters to compensate for time synchronization error. The AI / ML system can be applied to time synchronization over TSN mechanism supported by the wireless communication network, e.g. by addressing and controlling issues such that mobility, temperature, and other environmental factors that could impact latency and TSN determinism. In some embodiments, the AI / ML system can consistently monitor one or more measurements related to environmental conditions, e.g., vibration, temperature, etc., in order to determine how to compensate for their impacts on changes to TSN message latency.

[0123] In some implementations, the present disclosure provides a system associated with a communication network configured to support time-sensitive deterministic communication based on a TSN mechanism. The system comprises a processor configured to: obtain time synchronization data of a plurality of communication network (CN) components in the communication network, wherein the time synchronization data indicates a time synchronization error in the communication network; apply an artificial intelligence (Al) model on the time synchronization data to adjust at least one control parameter; and compensate for the time synchronization error in the communication network using the adjusted at least one control parameter.

[0124] In various embodiments, an AI / ML system can be used for communication network planning, automation of network operations (e.g., provisioning, optimization, fault prediction, security, fraud detection), network slicing, reducing operating costs, time synchronization and improving both the quality of service and customer experience based on chatbots, recommender systems, and techniques such as robotic process automation (RPA). The AI / ML system can also be used for optimizing use of system resources, autoscaling, anomaly detection, predictive analytics, prescriptive policies, and so on. Besides the above, the AI / ML system can be used for customer experience management and business support systems to support a multitude of emerging applications (e.g., AR / VR, Industrial loT, autonomous vehicles, drones, Industry 4.0 initiatives, Smart Cities, Smart Ports).

[0125] As discussed above, the communication network may be a wireless communication network which is defined in accordance with 3GPP standard (e.g., 5G, 6G, etc.). In some implementations, the communication network is integrated with a TSN system and associated with a TSN-wireless communication system such as the 5G-TSN system 100, the system 200 as described above. The AI / ML system may learn to compensate synchronization error in the communication network, by adjusting time, frequency, PID controller parameters, or other standardized clock parameters. After the compensation, the AI / ML system can minimize time synchronization error through the communication network. The AI / ML system may be referred to as an AI / ML application, ML application, or Al application.

[0126] A framework for AI / ML application is defined by 3GPP TS 22, 23 and 28 specifications such as 3GPP TS 22.261 (2022-12, Release 19), 3GPP TS 23.288 (2022-12, Release 18), 3GPP TR 22.874 (2021-12, Release 18), 3GPP TR 22.876 (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), 3 GPP TS 28.104 (2022-12, Release 17), 3 GPP 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.

[0127] In some embodiments, the AI / ML system may be applied to all oscillators in all network devices and components that are acting as clocks that need to be synchronized with thenetwork. This may apply particularly to TSN gate control oscillators. There is a master-slave relationship among all network clocks being synchronized to a single grandmaster clock.

[0128] For example, in the TSN-5G system 100 in Fig. 3, the plurality of communication network (CN) components (e.g., CN components 202-1, 202-2 ... 202-N in Fig. 3) may include 3GPP-defined components and user components such as UE (on-device), MEC, RAN, Core and Cloud processors. While each of the CN components 202-1, 202-2 .. . 202-N inside the 5G system 106 needs to be synchronized with the 5G internal system clock 190, each of the end devices 102 and the bridges 108 outside the 5G system 106 (e.g. in the TSN external network of the TSN-5G system 100) needs to be synchronized with the external system clock 116.

[0129] In some implementations, the AI / ML system includes a plurality of Al components that are configured to provide operations, processes and / or functions of the AI / ML application. For example, each of the CN components 202-1, 202-2 . . . 202-N, the end devices 102 and the bridges 108 may include a corresponding Al component for compensating time synchronization error.

[0130] In some implementations, the Al components are implemented as data models to collect training data and be trained based on the training data. In some implementations, the collected training data and output result may be provided to other Al components which are configured to perform subsequent operations, processes and / or functions. Also, the training data and output result from Al components may be provided to the AI / ML application to control and improve performance of the AI / ML application. In some embodiments, respective CN components to which respective Al components are distributed may include a processing circuit, a processor and memory configured to implement the respective Al components to perform various operations, e.g. time synchronization compensation. The respective CN components each include a communication interface that facilitates communications among the respective CN components. The memory can include one or more devices (e.g., RAM, ROM, etc.) or storing data and / or computer code for completing or facilitating the various processes and / or functions of the AI / ML application.

[0131] In some implementations, the CN components of the communication network may include control plane components and user plane components of the communication network(e g., the control plane of the communication network system such as a 5G system 106 in Fig. 3). The control plane components and user plane components may interact with each other via a service interface of the communication network in accordance with the 3GPP-defmed standard. For example, the CN components include AI-AF (AL Application Function), TSN-AF (Time Sensitive Network-Application Function) 124, NWDAF (Network Data Analytics Function), NSSAI (Network Slice Selection Assistance Information), and UPF 122.

[0132] Fig. 9 illustrates a diagram of a communication network 900 using an AI / ML-based system for time synchronization compensation, in accordance with one or more implementations. The communication network 900 may be implemented as any TSN-wireless communication system such as the 5G-TSN system 100, the 5G-TSN system 200 as described above. As shown in Fig. 9, the communication network 900 comprises a 5G system 904 and an external network formed by end stations 902-1, 902-2, and bridges 906. The 5G system 904 comprises a deviceside TSN translator (DS-TT) 910, a UE 920, a RAN (gNB) 930, a UPF 950 including a networkside TSN translator (NW-TT) 960, and a TSN block 940 including any component between the RAN 930 and the UPF 950. Ideally, each of these components and devices in the 5G system 904 is time synchronized with the same 5G grand master (GM) clock 970 in the 5G system 904. That is, the 5G system 904 is timing system according to the 5G GM clock 970. For example, each of the components in the 5G system 904 may have an associated 5G clock 994 to be synchronized with the 5G GM clock 970. In some embodiments, the 5G system 904 can be considered as an 802. 11 AS time-aware system or IEEE 1588 Boundary or Transparent clock.

[0133] Similarly, the external network outside the 5G system 904 forms another timing system according to a generalized Precision Time Protocol (gPTP) GM clock 908. As shown in Fig. 9, the external network comprises end stations 902-1, 902-2, and bridges 906. Ideally, each of these components and devices in the external network is time synchronized with the same gPTP GM clock 908. For example, each of the components in the external network outside the 5G system 904 may have an associated gPTP clock 992 to be synchronized with the gPTP GM clock 908.

[0134] The 5G system 904 and the external network form two decoupled timing systems, with two different clock systems running independently. Each of the DS-TT 910 and the NW-TT960 is a TSN translator configured to translate time between the two timing systems. In some embodiments, the TSN Translator (TT) is a 3GPP component that converts user network time to 5G network time because the user and 5G system are assumed to be synchronized to different grand master clocks. As such, each of the DS-TT 910 and the NW-TT 960 can be considered as a device holding two clocks, one of which synchronized to the 5G GM clock 970, and the other one of which synchronized to the gPTP GM clock 908.

[0135] For example, the data coming in the 5G system 904 from the bridges 906 are originally time stamped with those timestamps according to the gPTP GM clock 908. As the data is traveling through the 5G system 904, the data is under the time synchronization control of the 5G GM clock 970. But when the data exits on the other side of the 5G system 904 via the DS-TT 910, the data will be converted back to be associated with timestamps according to the gPTP GM clock 908. When a concerned component is mobile, conditions like vibration, temperature, location and speed will impact the quality of the clocks, and an AI / ML model can help to compensate the time synchronization error, which may occur during time synchronization between any two CN components, e.g. the DS-TT 910 and the UPF 950, or between any CN component and the 5G GM clock 970. In some embodiments, different AI / ML systems may be applied to the clocks of the CN components in the 5G system 904 and the user clocks in the user network outside the 5G system 904, to minimize time synchronization error within the 5G system 904 and within the user network respectively. In some embodiments, one same AI / ML system may be applied as a service to the clocks of the CN components in the 5G system 904 and the user clocks in the user network outside the 5G system 904, to minimize time synchronization error within the 5G system 904 and within the user network.

[0136] In some embodiments, the communication network 900 may include multiple node branches or network slices being time synchronized in parallel. For example, the two branches of CN components 202-1, 202-2 . . . 202-N inside the 5G system 106 in Fig. 3 can be time synchronized in parallel based on respective time synchronization signals.

[0137] As shown in Fig. 9, for time synchronization within the 5G system 904, time synchronization signals may be transmitted and propagated from the 5G GM clock 970 to the RAN 930 and then to other CN components in the 5G system 904, through a multi-hop process.At each hop, a time synchronization signal is transmitted from a master node to a slave node, to get the slave node time synchronized to the master node. For example, in the hop from the RAN 930 to the UE 920, the RAN 930 is considered as a master node having a master clock, and the UE 920 is considered as a slave node having a slave clock. By time synchronizing each masterslave clock pair following each master-slave relationship in the 5G system 904, the clocks in the 5G system 904 can form a clock spanning tree, where the 5G GM clock 970 is located at the root of the clock spanning tree. In a connected pair of two nodes, a node located closer to a leaf of the clock spanning tree may be considered a slave node, while a node located closer to the root of the clock spanning tree may be considered a master node.

[0138] Similarly in Fig. 9, for time synchronization in the external network outside the 5G system 904, time synchronization signals may be transmitted and propagated from the gPTP GM clock 908 to other TSN components through a multi-hop process. At each hop, a time synchronization signal is transmitted from a master node to a slave node, to get the slave node time synchronized to the master node. For example, in the hop from the bridges to the NW-TT 960, the bridges are considered as a master node having a master clock, and the NW-TT 960 is considered as a slave node having a slave clock. By time synchronizing each master-slave clock pair following each master-slave relationship in the external network, the clocks in the external network outside the 5G system 904 can form a second clock spanning tree, where the gPTP GM clock 908 is located at the root of the second clock spanning tree.

[0139] While all CN components and TSN components in Fig. 9 desire and expect to get ideal time synchronization, the TSN performance depends on the ability of all clock systems to maintain synchronization. More often than not, time synchronization approaches experience noise due to variation in location, temperature, vibration, and ultimately quantum effects. The present teaching utilizes AVML service or system to mitigate such noise and improve TSN performance.

[0140] For example, the 5G system 904 in Fig. 9 includes an AI / ML time synchronization compensation system 980 configured to compensate for any time synchronization noise or error in the time domain of the communication network 900. In some embodiments, the AVML time synchronization compensation system 980 may be located in TSN-AF (Time Sensitive Network-Application Function) of the 5G system 904. After the compensation, all CN components and TSN components in Fig. 9 can obtain AI / ML corrected time synchronization. One purpose of the embodiments shown in Fig. 9 is to increase the determinism of TSN, particularly mobile TSN when location and other radio information are included as input to the AI / ML time synchronization compensation system 980.

[0141] In some embodiments, the AI / ML time synchronization compensation system 980 may include multiple AI / ML components residing at various locations within the clock spanning tree, including locations at end-stations. Each AI / ML component may apply an AI / ML model to compensate and correct time synchronization error, by adjusting one or more control parameters related to time synchronization. In various embodiments, the AI / ML time synchronization compensation system 980 may be implemented as hardware, or as a service or software that could offer more flexibility and be another virtualized component of a 5G system.

[0142] Depending on different use cases and sizes of a platform the communication network 900 is running, the AI / ML time synchronization compensation system 980 may include one or more sensors configured to detect environmental conditions (e.g., vibration, temperature, etc.) of the communication network 900. In some use cases, e.g. when the communication network 900 is running in an airport or large-scale metropolitan area, each respective component in the communication network 900, particularly each CN component in the 5G system 904, can have sensors for sensing environmental conditions like vibration, temperature, location, etc. of the respective component. For example, these sensed conditions for each clock or oscillator at each respective component can be utilized by the AI / ML time synchronization compensation system 980 to compensate time synchronization error for the respective component.

[0143] In some use cases, e.g. the communication network 900 is running in an aircraft or a drone, where the temperature and vibration impact the whole object more or less uniformly. Then, all components in the communication network 900, particularly all CN component in the 5G system 904, can have common sensors for sensing environmental conditions like vibration, temperature, location, for the entire network. These sensed conditions for the entire network can be utilized by the AI / ML time synchronization compensation system 980 to compensate time synchronization error for all components in the network.

[0144] Fig. 10 illustrates an example of operations of an AI / ML-based time synchronization compensation system 1000 in accordance with one or more implementations. In some embodiments, the AI / ML-based time synchronization compensation system 1000 may be implemented as the AI / ML time synchronization compensation system 980 in Fig. 9 for compensating time synchronization error in the communication network using a AI / ML-based time synchronization compensation model. As shown in Fig. 10, the AI / ML-based time synchronization compensation system 1000 may include a Data Collection module 1010, a Time Synchronization Compensation Model Training module 1020, a Time Synchronization Compensation Model Inference module 1030 and a Time Synchronization Compensation Actor module 1040.

[0145] In some examples, the Data Collection module 1010 collects data from various sources, and provides input data to the Time Synchronization Compensation Model Training module 1020 and the Time Synchronization Compensation Model Inference module 1030. The collected data may include measurements from various CN components such as UEs or different network entities, some sensors, feedback 1045 from the Time Synchronization Compensation Actor module 1040, and / or data output from the time synchronization compensation model.

[0146] As shown in FIG. 10, the Data Collection module 1010 collects AI / ML input data 1002, optional sensor input data 1004, and label data 1006. In some embodiments, the AI / ML input data 1002 may include time series data related to: a source clock (e.g. a grand master clock), an offset that represents the difference in time between the source clock and a target clock (e.g. a clock to be synchronized with the source clock), a controller state, a frequency difference of a slave clock compared to a master clock (in terms of fractional frequency offset), and / or a path delay estimation (either symmetric or asymmetric). The controller state may indicate a state of a controller, e.g. a proportional integrated controller. The controller state may be any one of: sO indicating clocks not synchronized; si indicating a significant difference (e.g. when the difference is larger than a threshold T) between the source and target clocks; and s2 indicating only frequency of the controlled object or clock is changed. In some embodiments, the threshold T may be one of the parameters or weights the AI / ML-based time synchronization compensation model is trying to learn during training. Because the clock spanning tree includes a chain of clocks with multiple hops, different time synchronization signals may take differentamounts of time to send messages over those hops. This generates different path delay values, which can be taken into account when compensating the time synchronization error. In some embodiments, the optional sensor input data 1004 may include sensor detected data related to: vibration, temperature, and / or location (including velocity and / or acceleration). In some embodiments, the data collected by the Data Collection module 1010 may not be limited to the examples listed above. The AI / ML input data 1002 and the optional sensor input data 1004 may be put into training data for the AI / ML-based time synchronization compensation model during a training stage of the AI / ML-based time synchronization compensation model, and / or put into inference data for the AI / ML-based time synchronization compensation model during an inference stage of the AI / ML-based time synchronization compensation model.

[0147] In some embodiments, the label data 1006 may include known or expected model output (e.g. labels or pseudo labels) to be put into the training data of the AI / ML-based time synchronization compensation model. The label data 1006 may be generated based on a measurement or estimate the jitter of arriving TSN frames or jitters of oscillators. The jitter is often due to time synchronization issues. The measured jitter can be used as label data, such that the AI / ML-based time synchronization compensation model is trained to minimize jitter. In some embodiments, the jitter is clock jitter. In some embodiments, the jitter is measured jitter of TSN frames when they arrive at their locations, which may be referred to as frame delay variation or packet delay variation.

[0148] The Data Collection module 1010 may provide time synchronization training data 1012 to the Time Synchronization Compensation Model Training module 1020 during a training stage of the AI / ML-based time synchronization compensation system 1000; and provide time synchronization inference data 1014 to the Time Synchronization Compensation Model Inference module 1030 during an inference stage of the AI / ML-based time synchronization compensation system 1000. The time synchronization training data 1012 may be data needed as input for training, validating and testing an AI / ML model at the Time Synchronization Compensation Model Training module 1020. The time synchronization inference data 1014 may be data needed as input for performing an inference function using a trained AI / ML model at the Time Synchronization Compensation Model Inference module 1030.

[0149] In the example shown in FIG. 10, the time synchronization training data 1012 may include one or more data from the AI / ML input data 1002, the optional sensor input data 1004 and the label data 1006 collected during or for a training stage. The time synchronization inference data 1014 may include one or more data from the AI / ML input data 1002 and the optional sensor input data 1004 collected during or for an inference stage. For example, the clock data collected by the Data Collection module 1010 during a training stage may be used as part of the time synchronization training data 1012; while the clock data collected by the Data Collection module 1010 during an inference stage may be used as part of the time synchronization inference data 1014.

[0150] In some implementations, the AI / ML-based time synchronization compensation system 1000 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.

[0151] During a training stage of the AI / ML-based time synchronization compensation system 1000, the Time Synchronization Compensation Model Training module 1020 performs model training, validation, and testing for the AI / ML-based time synchronization compensation model. Model performance metrics may be generated as part of the model testing procedure. In some embodiments, the AI / ML-based time synchronization compensation model may be trained based on any of the following optimization metrics: root mean squared error (RMSE), mean absolute error (MAE), or mean bias error (MBE). In some embodiments, the AI / ML-based time synchronization compensation model may be trained as a neural network.

[0152] The Time Synchronization Compensation Model Training module 1020 may also be responsible for data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on the time synchronization training data 1012 delivered by the Data Collection module 1010. At the end of the training stage, the Time SynchronizationCompensation Model Training module 1020 can generate a model deployment / update 1022 to initially deploy a trained, validated, and tested AI / ML model to the Time Synchronization Compensation Model Inference module 1030 or to deliver an updated AI / ML model to the Time Synchronization Compensation Model Inference module 1030. That is, the AI / ML-based time synchronization compensation model is trained, validated, tested and updated by the Time Synchronization Compensation Model Training module 1020, and sent to the Time Synchronization Compensation Model Inference module 1030 for data inference.

[0153] During an inference stage of the AI / ML-based time synchronization compensation system 1000, the Time Synchronization Compensation Model Inference module 1030 applies the trained or re-trained (updated) AI / ML model on the time synchronization inference data 1014 to generate AI / ML output data 1035 which may include e.g. predictions or decisions. The Time Synchronization Compensation Model Inference module 1030 may provide model performance feedback 1032 based on predictions or decisions to the Time Synchronization Compensation Model Training module 1020. The model performance feedback 1032 may be used by the Time Synchronization Compensation Model Training module 1020 to monitor the performance of the AI / ML model, when available. The AI / ML output data 1035 may comprise the inference output of the AI / ML model after being applied by the Time Synchronization Compensation Model Inference module 1030. In general, an AI / ML system may reside on any processor in a communication network. For example, the AI / ML system may be remotely in the 5G Core, in a cloud, on the Edge, or in a UE of a 5G network. In some embodiments, the AI / ML output data 1035 may include data or parameters for: frequency offset correction; optimization of the proportional integral (PI) servo’s parameters; optimization of the proportional integrated controller’s K_p, K i parameters; and / or improving topology or stratum of a clock spanning tree.

[0154] In some embodiments, the Time Synchronization Compensation Model Inference module 1030 may also be responsible for data preparation (e.g., data pre-processing and cleaning, formatting, and transformation) based on the time synchronization inference data 1014 delivered by the Data Collection module 1010, if needed or required.

[0155] In some embodiments, the Time Synchronization Compensation Actor module 1040 receives the AI / ML output data 1035 from the Time Synchronization Compensation ModelInference module 1030 and triggers or performs corresponding actions. For example, the corresponding actions may include redistribution of the Al components of the AI / ML system, or time synchronization compensation for devices in a communication network. The Time Synchronization Compensation Actor module 1040 may trigger actions directed to other entities or to itself. The feedback 1045 may include information that may be needed to derive additional time synchronization training data 1012, additional time synchronization inference data 1014 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. In some examples, the AI / ML- based time synchronization compensation system 1000 utilizes a learning process of the AI / ML- based time synchronization compensation model to adjust time synchronization parameters by mapping the identified inputs to outputs of the model, such that error is minimized between each pair of master and slave clocks.

[0156] In some embodiments, the AI / ML-based time synchronization compensation model may be trained to react in a manner to reduce timing error given input data about a network, e.g. the communication network 900. As discussed above, the input data may also include sensor input data 1004 for each node in the network. In some embodiments, after training an AI / ML model for time synchronization compensation based on the sensed data, when a node knows its location, temperature and / or vibration in this particular location, the node can learn its time synchronization error using AI / ML model and proactively compensate for it, even before the time synchronization error happens. This will smooth out the time synchronization process and increase TSN determinism.

[0157] In some embodiments, the AI / ML-based time synchronization compensation model trained by the AI / ML-based time synchronization compensation system 1000 is a trained AI / ML model that 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. In some embodiments, 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 some embodiments, the parameters of the trained AI / ML model can be adapted iteratively by several steps of training.

[0158] In some embodiments, the trained AI / ML model may 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.

[0159] As discussed above, an AI / ML system using a trained model like the AI / ML-based time synchronization compensation model trained and utilized by the AI / ML-based time synchronization compensation system 1000, may reside at various locations within the clock spanning tree of a communication network. In some embodiments, an RMS time synchronization jitter error is monitored while time parameters are adjusted. Based on AI / ML model training, some patterns due to temperature, usage, vibration, etc. can be learned and compensated to improve synchronization performance. Such patterns may occur over both small time scales and large time scales. The AI / ML system may learn to evolve different types of control laws and may monitor TSN frame delay variation to help improve its learning. In some embodiments, there may be a tradeoff in time to learn and the quality of the learning result.

[0160] In some embodiments, many parameters and values, like the clock spanning tree, RMS error values, and all other observables, can be obtained from YANG models. An exemplary control system based on YANG model can be used to represent or realize the time synchronization control system, e.g., the AI / ML time synchronization compensation system 980 in Fig. 9.

[0161] In some embodiments, data can be exchanged between Al and 5G interfaces for time synchronization in the communication network. For example, a 5G data analytics system can collect time synchronization performance information during normal network operation. The collected time synchronization performance information can be used to improve time synchronization performance, by comparing corresponding data related to temperature, vibration, etc. across different time synchronization performance scenarios.

[0162] In some embodiments, a system utilizes the following elements or modules within a 3GPP system to enable AI / ML time synchronization compensation: sources, collector, preprocessor, AI / ML model, policy, distributor, sink.

[0163] In some embodiments, sources may be e.g. NETCONF servers supporting the YANG 802. IAS module located on or near each clock, and can provide raw data related to performance measurements, key performance indicators (KPIs), UE measurements, configuration, and alarms to feed the AI / ML model. For example, a typical software tool is being developed to improve the performance of network time synchronization. All supporting information, including temperature, time, location, power quality, etc., would be helpful for improving time synchronization performance as well.

[0164] In some embodiments, a collector may be e.g. a NETCONF client, which coordinates the collection of data from various sources. In some examples, the collector must have knowledge of all network clocks, including their network locations. This could be obtained manually, based on link layer discovery protocol (LLDP), or the clock spanning tree may be inferred from IEEE 802. IAS.

[0165] In some embodiments, a pre-processor may be responsible for preparing the data to fit the AI / ML model by performing different data processing including data cleansing, formatting, and validation. Like all other elements, the pre-processor can be a service, which may reside in the collector or elsewhere. In some examples, the pre-processor may verify that the data collected matches the required format and the values lie within a reasonable range of values. Any abnormal values may be discarded, and / or an unsolicited alert is sent to the management system if repeated errors are found.

[0166] In some embodiments, an AI / ML model may represent an AI / ML-based logic or algorithm used for inference, e.g., for producing analytics output results based on “real-time” “live” data input. The AI / ML model may be software and may be located anywhere within or outside of the 5G system. In some embodiments, the input data for an AI / ML model may appear as a multidimensional time series and the AI / ML model may search for the PID control parameters and configurable IEEE 802. IAS settings that minimize time synchronization error throughout the system. The input data can be provided by the collector to the AI / ML model. Insome embodiments, different model / algorithms may be available for usage as the AI / ML model. In that case, multiple AI / ML models / algorithms may be trained simultaneously to find the best performing model based on one or more of the model performance metrics including: e.g. time to learn, time to react to apply compensation, ability to avoid over-training (sufficient breadth of learning), as well as overhead (how much input data is required to learn), etc.

[0167] In some embodiments, a policy can define rules for forwarding the output of the AI / ML model toward the corresponding sinks, while controlling the regularity and conditions upon which input data is collected. For example, according to a policy, a condition for input data collection may be precise, periodic sampling, making it a candidate for a TSN scheduled flow. In some examples, the policy may define rules to limit the amount of traffic generated by the AI / ML model, by limiting the sampling rate, limiting the amount of different types of data collected, or limiting the rate at which the AI / ML model adjusts the sinks.

[0168] In some embodiments, a distributor is in charge of identifying the sinks and distributing / forwarding the policy to the corresponding output of the AI / ML model. In some examples, the sources and the sinks (network clocks) are the same. Accordingly, the collector and the distributor will be very similar and may be implemented as part of the same component. For example, the Distributor may be a NETCONF client that takes the results of the AI / ML model and sets network clocks appropriately.

[0169] In some embodiments, a sink is a consumer or a target of the distributor. In some examples, the sink may be the same as the source. That is, the sink may comprise the target elements of the NETCONF server to be adjusted on each source clock.

[0170] In some embodiments, a concept drift is identified and mitigated during operations of an AI / ML system. For example, the time synchronization settings learned by the AI / ML system may perform worse as environmental conditions change. Then, some type of re-learning can take place to fix the concept drift. For example, a re-leaming or re-training of an AI / ML model may be triggered based on: a detected higher-than-threshold change of an environment condition, a sudden change of a PID parameter, and / or a pre-determined training period.

[0171] Fig. 11 illustrates a flow chart showing a method 1100 for compensating time synchronization error in a communication network, in accordance with one or more implementations. In some embodiments, the method 1100 may be performed by an AI / ML system, e g. the AI / ML time synchronization compensation system 980 in Fig. 9 or the AI / ML- based time synchronization compensation system 1000 in Fig. 10.

[0172] As discussed above, a system may comprise a processor configured for compensating time synchronization error in a communication network, which can support time-sensitive deterministic communication based on a time-sensitive network (TSN) mechanism.

[0173] At operation 1110, the processor is configured to obtain time synchronization data of a plurality of communication network (CN) components in the communication network, where the time synchronization data indicates a time synchronization error in the communication network. Examples of the CN components may include: the DS-TT 910, the UE 920, the RAN 930, the UPF 950, and the 5G GM clock 970 as shown in FIG. 9. Examples of the time synchronization data may include the AI / ML input data 1002 and the optional sensor input data 1004 as shown in FIG. 10.

[0174] At operation 1120, the processor is further configured to apply an artificial intelligence (Al) model on the time synchronization data to adjust at least one control parameter. Examples of the applied Al model may include: the AI / ML-based time synchronization compensation model 1020, 1030, as trained in the AI / ML-based time synchronization compensation system 1000 shown in FIG. 10. The at least one control parameter may include any parameter that can impact time synchronization in the network, e.g. the parameters in the AI / ML output data 1035 as shown in FIG. 10.

[0175] At operation 1 130, the processor is further configured to compensate for the time synchronization error in the communication network using the adjusted at least one control parameter. As discussed above, the time synchronization error may be computed based on one of the following metrics: root mean squared error (RMSE), mean absolute error (MAE), or mean bias error (MBE), as shown in FIG. 10.

[0176] In some implementations, the processor may operate in a control plane of the communication network as shown in Fig. 1 and Fig. 3. In some embodiments, the operation 1130 could periodically loop back to the operation 1110 to operate continuously, by treating the output of the Al model at the operation 1130 as a feedback to generate the updated time synchronization data at the operation 1110.

[0177] Although the methods described above are with reference to the illustrated flowcharts, it will be appreciated that many other ways of performing the acts associated with the methods can be used. For example, the order of some operations may be changed, and some of the operations described may be optional.

[0178] As discussed above, in some implementations, a system associated with a communication network configured to support time-sensitive deterministic communication based on a time-sensitive network (TSN) mechanism, comprises a processor configured to: obtain time synchronization data of a plurality of communication network (CN) components in the communication network, wherein the time synchronization data indicates a time synchronization error in the communication network; apply an artificial intelligence (Al) model on the time synchronization data to adjust at least one control parameter; and compensate for the time synchronization error in the communication network using the adjusted at least one control parameter.

[0179] In some implementations, the time synchronization data comprises at least one of: clock data of the plurality of CN components, wherein the clock data includes data related to: a source clock, a target clock, and a master clock; offset data representing a time difference between a source clock and a target clock; a frequency difference of a clock to a master clock; and a path delay estimation for sending a time synchronization signal. In some implementations, the time synchronization data further comprises a controller state that indicates one of the following states: there are clocks not synchronized; the time difference between the source clock and the target clock is larger than a threshold; or only frequency of a controlled clock is changed. Examples of the time synchronization data may include the AI / ML input data 1002 as shown in FIG. 10.

[0180] In some implementations, the system further comprises at least one sensor configured to detect data related to at least one of: vibration, temperature, usage or location of the plurality of CN components. The time synchronization data comprises the detected data of the at least one sensor. Examples of the time synchronization data may include the sensor input data 1004 as shown in FIG. 10.

[0181] In some implementations, the system further comprises a plurality of sensors, each associated with a corresponding CN component of the plurality of CN components and configured to detect data related to at least one of: vibration, temperature, usage or location of the corresponding CN component. The time synchronization data comprises the detected data of the plurality of sensors. Examples of the CN components may include: the DS-TT 910, the UE 920, the RAN 930, the UPF 950, and the 5G GM clock 970 as shown in FIG. 9. Examples of the time synchronization data may include the sensor input data 1004 as shown in FIG. 10.

[0182] In some implementations, the at least one control parameter is used for performing at least one of the following: correcting frequency offset in the communication network; optimizing a proportional integral derivative controller; or updating a clock topology including master clocks and slave clocks. The at least one control parameter may include any parameter that can impact time synchronization in the network, e.g. the parameters in the AI / ML output data 1035 as shown in FIG. 10.

[0183] In some implementations, the time synchronization error in the communication network is minimized after the compensation. In some implementations, the communication network includes a wireless network configured in accordance with a 3 GPP standard. In some implementations, the plurality of CN components of the wireless network includes at least: a user equipment (UE), a radio access network (RAN), and a user plane function (UPF). In some implementations, the at least one control parameter comprises a configurable setting defined in the 3GPP standard. An example of the wireless network is the communication network 900 shown in FIG. 9. Examples of the CN components may include: the DS-TT 910, the UE 920, the RAN 930, the UPF 950, and the 5G GM clock 970 as shown in FIG. 9.

[0184] In some implementations, the processor is further configured to: obtain training data based on measurements related to time synchronization in the communication network; train aplurality of Al models simultaneously based on the training data, wherein each of the plurality of Al models is trained to minimize one of: a root mean squared error, a mean absolute error, or a mean bias error; and generate the Al model based on the plurality of Al models. In some implementations, the measurements related to time synchronization comprise at least one of: a measurement of jitter of a TSN frame; or a measurement of jitter of a clock signal. Examples of the training data may include the time synchronization training data 1012 as shown in FIG. 10. In some implementations, the Al model is generated based on: ranking the plurality of Al models to generate a ranked list according to at least one of the following metrics: time to learn, time to react to apply compensation, ability to avoid over-training, or learning overhead; and selecting the top model in the ranked list as the Al model. The AI / ML-based time synchronization compensation model trained and utilized by the AI / ML-based time synchronization compensation system 1000 in FIG. 10 may be generated based on the plurality of Al models.

[0185] In some implementations, a method for compensating time synchronization error in a communication network configured to support time-sensitive deterministic communication based on a time-sensitive network (TSN) mechanism, comprises: obtaining time synchronization data of a plurality of communication network (CN) components in the communication network, wherein the time synchronization data indicates a time synchronization error in the communication network; applying an artificial intelligence (Al) model on the time synchronization data to adjust at least one control parameter; and compensating for the time synchronization error in the communication network using the adjusted at least one control parameter.

[0186] In some implementations, a non-transitory computer readable medium storage configured to one or more instructions which, when executed by a processor, causes the processor to perform the method as discussed above.

[0187] 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 theirfunctionality. 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.

[0188] 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.

[0189] The previous description is provided to enable any person skilled in the art to practice the various aspects described herein. The previous description provides various examples of the subject technology, and the subject technology is not limited to these examples. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects. Thus, the claims are not intended to be limited to the aspects shown herein, but is to be accorded the full scope consistent with the language claims, wherein reference to an element in the singular is not intended to mean “one and only one” unless specifically so stated, but rather “one or more.” Unless specifically stated otherwise, the term “some” refers to one or more. Pronouns in the masculine (e.g., his) include the feminine and neuter gender (e.g., her and its) and vice versa. Headings and subheadings, if any, are used for convenience only and do not limit the disclosure described herein.

[0190] 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.

[0191] 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.

[0192] A phrase such as an “aspect” does not imply that such aspect is essential to the subject technology or that such aspect applies to all configurations of the subject technology. A disclosure relating to an aspect may apply to all configurations, or one or more configurations. An aspect may provide one or more examples. A phrase such as an aspect may refer to one or more aspects and vice versa. A phrase such as an “embodiment” does not imply that such embodiment is essential to the subject technology or that such embodiment applies to all configurations of the subject technology. A disclosure relating to an embodiment may apply to all embodiments, or one or more embodiments. An embodiment may provide one or more examples. A phrase such as an “embodiment” may refer to one or more embodiments and vice versa. A phrase such as a “configuration” does not imply that such configuration is essential to the subject technology or that such configuration applies to all configurations of the subject technology. A disclosure relating to a configuration may apply to all configurations, or one or more configurations. A configuration may provide one or more examples. A phrase such as a “configuration” may refer to one or more configurations and vice versa.

[0193] 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 system associated with a communication network configured to support timesensitive deterministic communication, comprising: a processor configured to: obtain time synchronization data of a plurality of communication network (CN) components in the communication network, wherein the time synchronization data indicates a time synchronization error in the communication network; apply an artificial intelligence (Al) model on the time synchronization data to adjust at least one control parameter; and compensate for the time synchronization error in the communication network using the adjusted at least one control parameter.

2. The system of claim 1, wherein the time synchronization data comprises at least one of: clock data of the plurality of CN components, wherein the clock data includes data related to: a source clock, a target clock, and a master clock; offset data representing a time difference between a source clock and a target clock; a frequency difference of a clock to a master clock; and a path delay estimation for sending a time synchronization signal.

3. The system of claim 2, wherein the time synchronization data further comprises a controller state that indicates one of the following states: there are clocks not synchronized; the time difference between the source clock and the target clock is larger than a threshold; or only frequency of a controlled clock is changed.

4. The system of claim 1, further comprising: at least one sensor configured to detect data related to at least one of: vibration, temperature, usage or location of the plurality of CN components,wherein the time synchronization data comprises the detected data of the at least one sensor.

5. The system of claim 1, further comprising: a plurality of sensors, each associated with a corresponding CN component of the plurality of CN components and configured to detect data related to at least one of: vibration, temperature, usage or location of the corresponding CN component, wherein the time synchronization data comprises the detected data of the plurality of sensors.

6. The system of claim 1, wherein the at least one control parameter is used for performing at least one of the following: correcting frequency offset in the communication network; optimizing a proportional integral derivative controller; or updating a clock topology including master clocks and slave clocks.

7. The system of claim 1, wherein the time synchronization error in the communication network is minimized after the compensation.

8. The system of claim 1, wherein: the communication network includes a wireless network configured in accordance with a 3 GPP standard; the plurality of CN components of the wireless network includes at least: a user equipment (UE), a radio access network (RAN), and a user plane function (UPF); and the at least one control parameter comprises a configurable setting defined in the 3GPP standard.

9. The system of claim 1, wherein the processor is further configured to: obtain training data based on measurements related to time synchronization in the communication network;train a plurality of Al models simultaneously based on the training data, wherein each of the plurality of Al models is trained to minimize one of: a root mean squared error, a mean absolute error, or a mean bias error; and generate the Al model based on the plurality of Al models.

10. The system of claim 9, wherein the measurements related to time synchronization comprise at least one of: a measurement of jitter of a time-sensitive network (TSN) frame; or a measurement of jitter of a clock signal.

11. The system of claim 9, wherein the Al model is generated based on: ranking the plurality of Al models to generate a ranked list according to at least one of the following metrics: time to learn, time to react to apply compensation, ability to avoid overtraining, or learning overhead; and selecting the top model in the ranked list as the Al model.

12. A method for compensating time synchronization error in a communication network configured to support time-sensitive deterministic communication, comprising: obtaining time synchronization data of a plurality of communication network (CN) components in the communication network, wherein the time synchronization data indicates a time synchronization error in the communication network; applying an artificial intelligence (Al) model on the time synchronization data to adjust at least one control parameter; and compensating for the time synchronization error in the communication network using the adjusted at least one control parameter.

13. The method of claim 12, wherein the time synchronization data comprises at least one of: clock data of the plurality of CN components, wherein the clock data includes data related to: a source clock, a target clock, and a master clock;offset data representing a time difference between a source clock and a target clock; a frequency difference of a clock to a master clock; and a path delay estimation for sending a time synchronization signal.

14. The method of claim 13, wherein the time synchronization data further comprises a controller state that indicates one of the following states: there are clocks not synchronized; the time difference between the source clock and the target clock is larger than a threshold; or only frequency of a controlled clock is changed.

15. The method of claim 12, further comprising: detecting, by at least one sensor, data related to at least one of: vibration, temperature, usage or location of the plurality of CN components, wherein the time synchronization data comprises the detected data of the at least one sensor.

16. The method of claim 12, further comprising: detecting, by a plurality of sensors each associated with a corresponding CN component of the plurality of CN components, data related to at least one of: vibration, temperature, usage or location of the corresponding CN component, wherein the time synchronization data comprises the detected data of the plurality of sensors.

17. The method of claim 12, further comprising: obtaining training data based on measurements related to time synchronization in the communication network; training a plurality of Al models simultaneously based on the training data, wherein each of the plurality of Al models is trained to minimize one of: a root mean squared error, a mean absolute error, or a mean bias error; and generating the Al model based on the plurality of Al models.

18. The method of claim 17, wherein the measurements related to time synchronization comprise at least one of: a measurement of jitter of a time-sensitive network (TSN) frame; or a measurement of jitter of a clock signal.

19. The method of claim 17, wherein generating the Al model comprises: ranking the plurality of Al models to generate a ranked list according to at least one of the following metrics: time to learn, time to react to apply compensation, ability to avoid overtraining, or learning overhead; and selecting the top model in the ranked list as the Al model.

20. A non-transitory computer readable medium storage configured to store one or more instructions which, when executed by a processor, causes the processor to perform operations comprising: obtaining time synchronization data of a plurality of communication network (CN) components in the communication network, wherein the time synchronization data indicates a time synchronization error in the communication network; applying an artificial intelligence (Al) model on the time synchronization data to adjust at least one control parameter; and compensating for the time synchronization error in the communication network using the adjusted at least one control parameter.

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