Abstract: A high-mobility resource allocation system and method for simulated users are provided, including a base station, antennas and a simulated mobile-user generator. The simulated mobile-user generator organizes a test field by taking the base station as the circle center, and measures and records multiple channel information based on the detection signals of the antennas. The machine learning module is used to perform simulation channel training according to channel information to generate simulated user channels that approximates real-world scenarios. A mobile-user organizing module organizes multiple simulated mobile users and their mobile channel information in the test field based on the simulated user channel, generates multiple simulated mobile user signals, and uses a resource-allocation optimizing module based on simulated mobile user signals, channel interference information upon moving, etc. to maximize the number of simulated mobile users while considering the quality of service.

Abstract: A high-mobility resource allocation system and method for simulated users are provided, including a base station, antennas and a simulated mobile-user generator. The simulated mobile-user generator organizes a test field by taking the base station as the circle center, and measures and records multiple channel information based on the detection signals of the antennas. The machine learning module is used to perform simulation channel training according to channel information to generate simulated user channels that approximates real-world scenarios. A mobile-user organizing module organizes multiple simulated mobile users and their mobile channel information in the test field based on the simulated user channel, generates multiple simulated mobile user signals, and uses a resource-allocation optimizing module based on simulated mobile user signals, channel interference information upon moving, etc. to maximize the number of simulated mobile users while considering the quality of service.

Abstract: A communication system includes a base station. The base station includes a plurality of distributed units (DUs), a central unit (CU) and a main processor. The main processor selects a mode corresponding to user equipment from a centralized control mode (CM) and a distributed control mode (DM) according to the current communication quality of the user equipment, wherein a functional split mode (FSM) includes the centralized control mode (CM) and the distributed control mode (DM).

Abstract: A method for allocating communication network resources assigns beam to multiple items of user equipment based on beam-closed manner, and allocates the beam to the item of user equipment with the smallest value if more than one item of the user equipment is assigned to the same beam Then, the method uses a rate control to the multiple items of user equipment according to rate closure manner, and determines whether all items of user equipment is assigned to the beam. Hence, the beam is emitted by the base station and the direction of the user ability align for efficient data transfer.

Abstract: A communication system includes a base station. The base station includes a plurality of distributed units (DUs), a central unit (CU) and a main processor. The main processor selects a mode corresponding to user equipment from a centralized control mode (CM) and a distributed control mode (DM) according to the current communication quality of the user equipment, wherein a functional split mode (FSM) includes the centralized control mode (CM) and the distributed control mode (DM).

Abstract: An AI decision based multiple telecommunication endpoints system is provided, including a telecommunication endpoint device and an artificial intelligence decision controller. The telecommunication endpoint device performs a communication test with the radio frequency communication device under test. The artificial intelligence decision controller is electrically connected to the telecommunication terminal device to control the communication test, and performs an efficiency analysis on the result of the communication test, and generates a decision instruction according to the result of the efficiency analysis.

Abstract: A system and method of emulating radio device includes a multi-radio unit, a multi-radio unit controller and an under-test radio system. The multi-radio unit includes multiple radio circuits, in which the radio circuits are configured to generate multiple radio emulated signals. The multi-radio unit controller coupled to the multi-radio unit is configured to generate multiple control signals to the multi-radio unit, in which the control signals are configured to control the radio emulated signals sent by the multi-radio unit. The under-test radio system is configured to receive the radio emulated signals generated by the multi-radio unit, and configured to generate multiple data corresponding to the radio emulated signals.

Abstract: A method and system for allocating communication network resources is disclosed. The method comprises: initializing network parameters and collecting queue status information and channel status information; assigning beam to multiple items of user equipment based on beam-closed manner; allocating the beam to the item of user equipment with the smallest value if more than one item of the user equipment is assigned to the same beam; using a rate control to the multiple items of user equipment according to rate closure manner; and determining whether all items of user equipment is assigned to the beam.

Abstract: Two algorithms for different functional-split network models are provided, which are the CU-based (central unit based) beam allocation and admission control algorithm (CU-BAACA) and the DU-based (distributed unit based) beam allocation and admission control algorithm (DU-BAACA). Difference between the CU-BAACA and DU-BAACA includes whether the algorithm is implemented at the CU site or at the DU site. Proposed algorithms aim to optimize DUs' quality of experience (QoE) by admission control to determine the amount of data from application layer entering into traffic queue and allocating beam in physical layer at the fronthaul network between CU and DUs. On the other hand, queueing delay and queue stability are taken into consideration to maintain the system steadiness. Simulation results compare performance of two functional split models to find the appropriate scenario for each function split option, which provide technical requirement and applicability of the proposed algorithms for practical system.

Abstract: Two algorithms for different functional-split network models are provided, which are the CU-based (central unit based) beam allocation and admission control algorithm (CU-BAACA) and the DU-based (distributed unit based) beam allocation and admission control algorithm (DU-BAACA). Difference between the CU-BAACA and DU-BAACA includes whether the algorithm is implemented at the CU site or at the DU site. Proposed algorithms aim to optimize DUs' quality of experience (QoE) by admission control to determine the amount of data from application layer entering into traffic queue and allocating beam in physical layer at the fronthaul network between CU and DUs. On the other hand, queueing delay and queue stability are taken into consideration to maintain the system steadiness. Simulation results compare performance of two functional split models to find the appropriate scenario for each function split option, which provide technical requirement and applicability of the proposed algorithms for practical system.

Abstract: Network device selection or handover schemes enable higher network capacity based on partially-observable Markov decision processes. Unavailable cell loading information is observed and/or predicted from non-serving base stations and actions are taken to maintain an active base station set or network device candidate data for selection in routing communications of a mobile device in a mobile device cell selection or handover procedure. A reward function is considered in the selection based on various parameters comprising system capacity, handover times, and mobility of a mobile device or mobile station.

Abstract: Network device selection or handover schemes enable higher network capacity based on partially-observable Markov decision processes. Unavailable cell loading information is observed and/or predicted from non-serving base stations and actions are taken to maintain an active base station set or network device candidate data for selection in routing communications of a mobile device in a mobile device cell selection or handover procedure. A reward function is considered in the selection based on various parameters comprising system capacity, handover times, and mobility of a mobile device or mobile station.

Abstract: A device in a base station of a mobile communication system for power management can include a storage device to store a group of parameter sets associated with a user equipment (UE) of the mobile communication system. The device can include a processor to calculate a corresponding objective function associated with each entity of the group of parameter sets. The processor can be configured to select an entity of the group of parameter sets based on the corresponding objective function, and the selected entity of the group of parameter sets facilitates the power management for the UE.

Abstract: Network device selection or handover schemes enable higher network capacity based on partially-observable Markov decision processes. Unavailable cell loading information is observed and/or predicted from non-serving base stations and actions are taken to maintain an active base station set or network device candidate data for selection in routing communications of a mobile device in a mobile device cell selection or handover procedure. A reward function is considered in the selection based on various parameters comprising system capacity, handover times, and mobility of a mobile device or mobile station.

Abstract: Network device selection or handover schemes enable higher network capacity based on partially-observable Markov decision processes. Unavailable cell loading information is observed and/or predicted from non-serving base stations and actions are taken to maintain an active base station set or network device candidate data for selection in routing communications of a mobile device in a mobile device cell selection or handover procedure. A reward function is considered in the selection based on various parameters comprising system capacity, handover times, and mobility of a mobile device or mobile station.

Abstract: The disclosed subject matter relates to a wireless communications environment. Femto-assisted location estimation schemes can determine UE location information based on information from either macro base stations or femto base stations in a LTE-A heterogeneous network. Positions information for a femto base station can be employed even though the exact positions of fBS may not be available. The femto base station position information can be depicted as a probabilistic distribution. Bayesian estimation based on TDOA and utilization of a particle filter can facilitate determining UE location information. Furthermore, a simplified scheme can reduce computational complexity.

Abstract: The disclosed subject matter relates to a wireless communications environment. Femto-assisted location estimation schemes can determine UE location information based on information from either macro base stations or femto base stations in a LTE-A heterogeneous network. Positions information for a femto base station can be employed even though the exact positions of fBS may not be available. The femto base station position information can be depicted as a probabilistic distribution. Bayesian estimation based on TDOA and utilization of a particle filter can facilitate determining UE location information. Furthermore, a simplified scheme can reduce computational complexity.

Abstract: Exemplary embodiments of the present disclosure provide several packet scheduling methods and communication apparatuses using the same. The packet scheduling method is different from the conventional packet scheduling method, since it does not schedule the sleep interval of the mobile subscriber station based upon the connections as the conventional packet scheduling method. Instead, the packet scheduling method considers the quality of service, and aggregates the data bursts to the frame corresponding to the frame index to which the data bursts are shifted based upon the individual packet, such that a total time length of the sleep intervals of the mobile subscriber station is maximized, and the maximum power saving efficiency is obtained correspondingly.

Abstract: A device in a base station of a mobile communication system for power management can include a storage device to store a group of parameter sets associated with a user equipment (UE) of the mobile communication system. The device can include a processor to calculate a corresponding objective function associated with each entity of the group of parameter sets. The processor can be configured to select an entity of the group of parameter sets based on the corresponding objective function, and the selected entity of the group of parameter sets facilitates the power management for the UE.

Abstract: Exemplary embodiments of the present disclosure provide several packet scheduling methods and communication apparatuses using the same. The packet scheduling method is different from the conventional packet scheduling method, since it does not schedule the sleep interval of the mobile subscriber station based upon the connections as the conventional packet scheduling method. Instead, the packet scheduling method considers the quality of service, and aggregates the data bursts to the frame corresponding to the frame index to which the data bursts are shifted based upon the individual packet, such that a total time length of the sleep intervals of the mobile subscriber station is maximized, and the maximum power saving efficiency is obtained correspondingly.