Multi-Antenna UE Selection Using Grip and AoA Estimation
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Solution Overview
Problem
Existing cellular network communication systems face challenges in optimizing antenna selection for data transmission and reception, particularly when the angle of arrival (AoA) of signals is unknown, leading to suboptimal performance and increased power consumption.
Innovation Solution
A method involving antenna and tuner state selection techniques, including user grip determination and AI model training, to identify the optimal antenna and tuner settings based on signal angle of arrival approximations and reference signal measurements, enhancing data throughput and reducing power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If antenna selection is performed without knowing the angle of arrival (AoA), then device complexity is reduced, but data throughput deteriorates
Solution Approach 1:
The system performs preliminary actions by determining user grip state and estimating AoA before antenna selection. The UE determines its grip state (e.g., held in hand, pocket, table) and uses this information to estimate the AoA of incoming signals. This preliminary estimation allows the antenna selection algorithm to choose the optimal antenna without requiring complex real-time AoA measurement hardware, thus maintaining low device complexity while improving data throughput through informed antenna selection.
2Productivity
If multiple antennas are continuously monitored for optimal selection, then data throughput is improved, but power consumption increases
Solution Approach 1:
Instead of continuously monitoring all antennas at full capacity, the system applies partial action by selecting only the necessary number of antennas based on user grip state and AoA estimation. The antenna selection algorithm determines the optimal subset of antennas to activate, thereby maintaining high data throughput through selective monitoring while reducing overall power consumption by keeping fewer antennas in active monitoring mode.
3Productivity
If antenna selection is optimized based on user grip and AoA estimation, then data throughput is improved, but device complexity increases
Solution Approach 1:
The antenna selection system operates autonomously by self-determining the user grip state using sensors already present in the UE (e.g., accelerometers, gyroscopes) and automatically estimating AoA based on this grip information. The system serves itself by utilizing existing hardware resources for grip detection and applying pre-stored antenna selection criteria corresponding to different grip states, eliminating the need for complex external control mechanisms while achieving optimized data throughput.
Data Source
AI summary
The present application relates to selecting an antenna optimization parameter of a UE. In an example, grip data indicating a user grip of the UE is generated. The grip data is used in a look-up of antenna data indicating an antenna selection from the plurality of antennas and/or a tuner state from a plurality of tuner states. In another example, reference signal measurements can be generated, each corresponding to one of the antennas. The reference signal measurements can be input to artificial intelligence model that outputs the antenna selection, the tuner state, or a predicted angle of arrival. The predicted angle of arrival can be used in a look-up of antenna data to determine the antenna selection and/or the tuner state.