Antenna Beam Management Using Spatial Temporal Predictions
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Solution Overview
Problem
Current antenna beam management in wireless communication systems is inefficient, consuming battery power and increasing latency due to the need to periodically measure all beams, especially in high-mobility scenarios where optimal beams change frequently, leading to suboptimal selection and limited data rates.
Innovation Solution
A system assisted by spatial and temporal measurements using an inertial measurement unit (IMU), geolocation and time unit (GTU), and an application processor (AP) that compensates for phase changes without sweeping through all beams, allowing for rapid and power-efficient beam selection and switching based on predicted movements and geolocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If beamforming is used to improve signal strength and directional transmission, then antenna gain and spatial selectivity are improved, but battery power consumption increases due to periodic measurements on all beams
Solution Approach 1:
The system performs preliminary actions by using IMU and GTU sensors to predict future terminal positions and orientations before beam measurements are needed. This allows the device to pre-calculate which beams will be optimal in the near future, avoiding the need to measure all beams periodically and thus reducing power consumption while maintaining high antenna gain.
Solution Approach 2:
The system dynamically adapts beam selection based on real-time spatial and temporal measurements from IMU and GTU sensors. Instead of static periodic measurements on all beams, the system continuously updates beam predictions based on terminal movement dynamics, ensuring optimal antenna gain is maintained while minimizing unnecessary measurements and power consumption.
2Measurement precision
If periodic measurements on all beams are performed to identify optimal beam, then beam selection accuracy is improved, but latency increases
Solution Approach 1:
The system performs preliminary beam identification using IMU and GTU data to predict optimal beams before actual communication needs arise. By pre-calculating beam selections based on predicted terminal movement, the system eliminates the latency associated with periodic full-beam measurements while maintaining accurate beam selection through predictive modeling.
Solution Approach 2:
The system skips the time-consuming process of measuring all beams by using spatial and temporal predictions to directly identify the optimal beam. This allows the system to rush through the beam selection process by jumping directly to the predicted optimal beam based on IMU and GTU data, significantly reducing latency while maintaining measurement precision.
3Reliability
If measurements on all beams are performed to ensure optimal beam selection, then transmission quality is improved, but data rate is limited due to time spent searching for optimal beam
Solution Approach 1:
The system performs preliminary identification of optimal beams using IMU and GTU sensors to predict terminal movement and orientation. This allows the system to have the optimal beam ready before data transmission begins, ensuring high transmission quality without the need to spend time searching through all beams during actual data transfer, thus maximizing data rate.
Solution Approach 2:
The system maintains continuous useful action by continuously tracking terminal movement via IMU and GTU sensors and continuously updating beam predictions. This ensures that the optimal beam is always available without interruption to data transmission, maintaining high transmission quality and maximizing data rate by eliminating idle search time.
4Productivity
If narrow beams are used to maximize data rate, then directional signal strength is improved, but beam selection becomes more critical and complex
Solution Approach 1:
The system performs preliminary beam selection using IMU and GTU sensors to predict optimal narrow beam directions before data transmission. This pre-selection process simplifies the complexity of narrow beam selection by using sensor data to directly identify the correct beam, eliminating the need for complex real-time search algorithms while maintaining the high data rates enabled by narrow beams.
Solution Approach 2:
The system uses IMU and GTU sensors as intermediaries to bridge the complexity of narrow beam selection. These sensors provide spatial and temporal data that mediates the beam selection process, translating complex physical movement into simple beam direction predictions. This intermediary approach maintains the benefits of narrow beams for high data rates while reducing selection complexity through sensor-based prediction.
Data Source
AI summary
A method performed by a beam management system that obtains spatial and temporal measurements of a mobile terminal and geolocation information of an associated base station on a wireless communication network. The system determines a phase compensation value for the mobile terminal based on the obtained information, which is used to calculate or select coefficients for antenna elements that form an optimal antenna beam. Thus, the system can perform beam switching quickly and efficiently based on predicted spatial or temporal characteristics of the mobile terminal without needing to wait for actual beam measurements.