Abstract: The present invention relates to a method and device for distributing idle UE by a carrier in eNB of a multi-carrier based mobile communication system. The method of distributing idle UE in a multi-carrier based mobile communication system according to the present invention includes a process of determining a search rate by a carrier on the basis of information representing load on the carrier, a step of determining a cell reselection priority on the idle UE on the basis of the determined search rate, and a process of transmitting the determined cell reselection priority to the idle UE.

Abstract: This invention presents methods leveraging artificial intelligence and machine learning (AI/ML) models to enhance wireless communications efficiency in 5G/6G networks. The processes involve storing, configuring, and transferring AI/ML models within base stations and user equipment devices (UE), allowing for localized decision-making and improved network performance. Features include dynamic model activation/deactivation, model compression/decompression, and encoding/decoding method negotiation. Periodic or condition-driven model updates ensure responsiveness to network changes, while model replacements enable upgrades and iterations. The system facilitates seamless handovers between base stations, with information sharing about model capabilities and UE specifics. Model storage and configuration can also occur in the UE, empowering it for local decision-making in variable or challenging network conditions.

Abstract: In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus may be wireless equipment. The wireless equipment selects a first subset of beams to be utilized for beam management. The beams are from a set of first type of beams used for communication with a base station or a UE. The wireless equipment measures signals transmitted on a second subset of beams. The beams are from the set of first type of beams or from a set of second type of beams. The wireless equipment measures the signals over a time window. The wireless equipment inputs the measurements to a computational model. The wireless equipment receives predictions of channel measurements on the first subset of beams from the computational model.

Abstract: Apparatus and methods are provided for enhanced AM RLC for NB-IoT UE in the NTN. In one novel aspect, the UE is configured with HARQ disabled, and configured with fast polling and/or fast status report for AM RLC enhancement. In one embodiment, the UE is configured with shortened AM window size, and wherein the shortened window size is smaller than an AM window size for an NB-IoT device. In other embodiments, the fast polling is triggered based on PDU threshold or byte threshold, or triggered based on an NTN polling timer, or triggered based on a combination of an NTN polling timer and one or more threshold triggers. In one embodiment, UE is configured to autonomously send a status report when a reception failure of an RLC PDU is detected, and wherein the status report is sent by a semi-persistent scheduling (SPS) uplink resource.

Abstract: Solutions pertaining to spectrum sharing in coordination between a non-terrestrial network (NTN) and a terrestrial network (TN) are proposed. An apparatus implemented in a UE measures a signal strength of each of one or more TN cells and one or more NTN cells. The apparatus reports a result of the measuring to a TN. The apparatus also receives an indication from the TN configuring and activating a bandwidth part (BWP) that separates transmission by a base station of the TN from NTN downlink (DL) transmissions.

Abstract: Apparatuses, methods, and systems for optimizing vehicle route overlap in vehicle routing plans are disclosed. A method comprises selecting a first vehicle route and a second vehicle route of a plurality of vehicle routes using a plurality of overlap metrics, reallocating destination nodes of the first vehicle route and the second vehicle route to a first new route and a second new route using an ordered projection of the destination nodes onto a line passing through the first vehicle route and the second vehicle route, and determining an overlap-optimized vehicle routing plan including the first new route and the second new route.

Abstract: This disclosure relates generally to systems and methods for autonomous task composition of vision pipelines using an algorithm selection framework. The framework leverages transformer architecture along with deep reinforcement learning techniques to search an algorithmic space for unseen solution templates. In an embodiment, the present disclosure describes a two stage process of identifying the vision pipeline for a particular task. At first stage, a high-level sequence of the vision pipeline is provided by a symbolic planner to create the vision workflow. At second stage, suitable algorithms for each high-level task are selected. This is achieved by performing a graph search using a transformer architecture over an algorithmic space on each component of generated workflow. In order to make the system more robust, weights of embedding, key and query networks of a visual transformer are updated with a Deep Reinforcement Learning framework that uses Proximal Policy Optimization (PPO) as underlying algorithm.

Abstract: Methods of machine learning for system deployments without performance regressions are performed by systems and devices. A performance safeguard system is used to design pre-production experiments for determining the production readiness of learned models based on a pre-production budget by leveraging big data processing infrastructure and deploying a large set of learned or optimized models for its query optimizer. A pipeline for learning and training differentiates the impact of query plans with and without the learned or optimized models, selects plan differences that are likely to lead to most dramatic performance difference, runs a constrained set of pre-production experiments to empirically observe the runtime performance, and finally picks the models that are expected to lead to consistently improved performance for deployment. The performance safeguard system enables safe deployment not just for learned or optimized models but also for additional of other ML-for-Systems features.

Abstract: Examples pertaining to selection of a tracking area identifier (TAI) as a last visited registered TAI in a geographical location in mobile communications are described. A user equipment (UE) receives broadcast information from a network indicating a plurality of tracking area codes (TACs) for the network. The UE then determines one or more candidate TACs, including at least a first TAC, best representing a current geographical location of the UE from the plurality of TACs received by removing zero, one or more TACs of the plurality of TACs from consideration based on the broadcast information received over time.

Abstract: Methods of machine learning for system deployments without performance regressions are performed by systems and devices. A performance safeguard system is used to design pre-production experiments for determining the production readiness of learned models based on a pre-production budget by leveraging big data processing infrastructure and deploying a large set of learned or optimized models for its query optimizer. A pipeline for learning and training differentiates the impact of query plans with and without the learned or optimized models, selects plan differences that are likely to lead to most dramatic performance difference, runs a constrained set of pre-production experiments to empirically observe the runtime performance, and finally picks the models that are expected to lead to consistently improved performance for deployment. The performance safeguard system enables safe deployment not just for learned or optimized models but also for additional of other ML-for-Systems features.

Abstract: Methods for selectively enabling and disabling UL HARQ retransmission in NTN communications and communication apparatus utilizing the same. A method for selectively enabling and disabling uplink Hybrid Automatic Repeat request (HARQ) retransmission in Non-Terrestrial Network (NTN) communications includes: determining a set of logical channels that are allowed to transmit or a set of MAC CEs that are allowed to be transmitted using one or more specific uplink (UL) HARQ processes. The one or more specific UL HARQ processes include one or more UL HARQ processes for which the UL retransmission has been disabled, or one or more UL HARQ processes for which the UL retransmission has been enabled.

Abstract: Low Earth Orbit (LEO) satellites orbit around the earth at a high speed (mobility), but over a predictable or deterministic orbit. In this invention, an efficient mechanism to include a list of multiple Tracking Area Codes (TAC List) in LEO-NTN is proposed for efficient tracking area update (TAU). Each cell broadcasts a TAC list corresponding to all the tracking areas it covers as it moves, dynamically. A UE selects a cell and is registered with a TA during initial registration and cell selection. As the satellite moves, the UE reselects another cell during cell reselection. The UE triggers a TAU only if its current TA does not match with any tracking area code of the TAC List broadcasted by the reselected cell. By using the concept of TAC list, it significantly reduces the TAU signaling overhead in LEO-NTN.

Abstract: Methods for a New Radio (NR)-based, Low Earth Orbit (LEO) Non-Terrestrial Networks (NTN) are proposed to improve cell selection and reselection by using satellite assistance information. Different from traditional 5G New Radio systems, the LEO NTN can provide the next cell information along the satellite trajectory using System Information Broadcast (SIB). The assistance information can include satellite's long term ephemeris in the format of Position Velocity (PV) information or details of satellite's other orbital parameters. During TN-NTN join coverage, as TN cells are expected to have a better coverage then NTN cells, the network can assign higher priority to the TN cells over NTN cells. Similarly, for a mobility involving earth-fixed and earth-moving beams (cells), earth-fixed cells can be prioritized over earth-moving beams for cell reselection.

Abstract: Various solutions for uplink synchronization in non-terrestrial network (NTN) communications are proposed. An apparatus implemented in a user equipment (UE) receives a system information block (SIB) of a target cell via a non-terrestrial (NT) network node of the NTN. The apparatus obtains an explicit epoch time from the SIB of the target cell. Then, the apparatus performs an uplink (UL) synchronization with the target cell through adjusting an uplink transmit time according to the explicit epoch time.

Abstract: A method of multiplexing logical channels (LCHs) in configured grants (CG) to enable faster transmission of high priority data in 5G New Radio-Unlicensed (NR-U) is proposed. Two options of imposing a CAPC-based restriction rule on multiplexing a low priority LCH with a high priority LCH are provided. In a first option, a threshold CAPC value is configured via RRC signaling. Data belonging to LCH having a priority lower than the CAPC threshold is not allowed to be multiplexed with data belonging to LCH having a priority higher than the CAPC threshold. In a second option, each CAPC has a flag indicating if the CPAC can have reduced priority via RRC signaling. If the flag is set for the CAPC, then data belonging to LCH assigned with the CAPC cannot be multiplexed with data belonging to lower priority LCHs.

Abstract: This innovation describes methods for an NR-based, LEO Non-Terrestrial Networks (NTN) to improve handover process by sending broadcast or group-cast downlink handover (HO) Command message to all (or group of) UEs in the coverage area (cell or beam-spot) of the LEO satellite. Subsequently, on receiving the broadcast (or group-cast) HO Command message, all the UEs in the source cell transmit HO Complete message to the source cell of the LEO-NTN. In order to reduce or avoid heavy Random Access (RA), generating from all the UEs, the LEO-NTN can either send Contention Free Random Access (CFRA), or instruct the UE to perform a random backoff before sending the uplink HO Complete message. Moreover, improvements to configure Conditional Handover (CHO) and delaying the transmission of HO Complete message are also provided.

Abstract: Machine Learning (ML) models are deployed in digital platforms for data analytics. However, it is realized that there is growing trends of recognition that machine learning models expose new vulnerabilities in software systems, for instance training data poisoning, adversarial responses, model extraction, and the like. Embodiments of the present disclosure provide systems and methods for safeguarding training dataset by exploiting immutability feature and generating immutable machine learning models for data analytics. More specifically, immutable records of events are governed by smart contracts within highly secure permissioned distributed ledger. This dataset is used for training multiple machine learning models which are immutable in nature and further utilized for triggering actions for incoming request(s) from IoT platforms.

Abstract: A method includes receiving a measurement configuration from a serving cell of a user equipment (UE) in a non-terrestrial network (NTN) providing mobile communication service based on satellites. The satellites can be low Earth orbiting (LEO) satellites, geostationary Earth orbiting (GEO) satellites, and the like. The measurement configuration indicates a first synchronization signal block (SSB) based measurement timing configuration (SMTC) and a second SMTC. The first SMTC specifies first SMTC windows aligning with SSB signals from the serving cell of the UE. The second SMTC specifies second SMTC windows aligning with SSB signals from a first neighbor cell of the UE. The serving cell is associated with a first flying object, and the first neighbor cell is associated with a second flying object. The UE performs a measurement based on the first SMTC corresponding to the serving cell and the second SMTC corresponding to the first neighbor cell.

Abstract: A method for determining Listen Before Talk (LBT) type and Channel Access Priority Class (CAPC) for signaling message transmission over signaling radio bearers (SRBs) in 5G New Radio-Unlicensed (NR-U) is proposed. A user equipment (UE) generates an SRB message to be transmitted to a serving base station over a control channel. Before the uplink (UL) transmission of an UL PDU containing the SRB message, the UE performs an LBT procedure using a set of LBT parameters associated with a CAPC value. In one embodiment, the CAPC is provided by the base station via an UL grant contained in a downlink control information (DCI). In another embodiment, the CAPC is determined by the UE based on an SRB type of the SRB message.

Abstract: A method to improve paging process by including additional backup paging opportunities in new radio unlicensed (NR-U) systems is proposed. To cope with the downlink transmission impairments arising from possible listen-before-talk (LBT) failures, NR-U user equipment (UE) can listen the additional paging opportunities (POs) to get the paging message. In an efficient mechanism, additional backup paging opportunities are configured while minimizing the UE's power consumption. The invention also reduces UE's power consumption by configuring short and micro sleep cycles between successive backup paging opportunities. In addition, to avoid continuous listening to the backup paging opportunities, paging monitoring can be suspended for a UE if the LBT is successful and the paging is not intended for the UE.