Abstract: A Quality of Experience (QoE) optimization system and method are provided. An electronic device inputs key performance indicators (KPIs) and system control parameters collected from a core network, a base station and a user equipment (UE) into a QoE optimization model. The QoE optimization model then optimizes the system control parameters based on the KPIs and a user QoE fed back from the UE to output optimized system control parameters. Furthermore, a strategy emulator controls at least one of a base station emulator and a UE emulator, so as to emulate the QoE optimization model using the at least one of the base station emulator and the UE emulator. Non-real-time optimization adjustments to the QoE optimization model are made based on the result of the emulation performed by the at least one of the base station emulator and the UE emulator.

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 Quality of Experience (QoE) optimization system and method are provided. An electronic device inputs key performance indicators (KPIs) and system control parameters collected from a core network, a base station and a user equipment (UE) into a QoE optimization model. The QoE optimization model then optimizes the system control parameters based on the KPIs and a user QoE fed back from the UE to output optimized system control parameters. Furthermore, a strategy emulator controls at least one of a base station emulator and a UE emulator, so as to emulate the QoE optimization model using the at least one of the base station emulator and the UE emulator. Non-real-time optimization adjustments to the QoE optimization model are made based on the result of the emulation performed by the at least one of the base station emulator and the UE emulator.

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 system for synthesizing signal of user equipment and a method thereof are provided. The system includes a physical channel modeler and a physical channel training module. The physical channel modeler receives geo information of a field under test of and a sparse real physical field channel feature to build a physical channel model. The physical channel modeler estimates a plurality of predefined positions of the geo information to obtain a plurality of simulated physical field channel features corresponding to the predefined positions. The physical channel training module receives and performs training on the geo information, the sparse real physical field channel feature and the simulated physical field channel features by using an AI algorithm to perform an inference of a fully real physical field channel feature.

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 for synthesizing signal of user equipment and a method thereof are provided. The system includes a physical channel modeler and a physical channel training module. The physical channel modeler receives geo information of a field under test of and a sparse real physical field channel feature to build a physical channel model. The physical channel modeler estimates a plurality of predefined positions of the geo information to obtain a plurality of simulated physical field channel features corresponding to the predefined positions. The physical channel training module receives and performs training on the geo information, the sparse real physical field channel feature and the simulated physical field channel features by using an AI algorithm to perform an inference of a fully real physical field channel feature.

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.