Sensor-Assisted mmWave Beam Management for OoC Recovery
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Solution Overview
Problem
Wireless devices using mmWave spectrum face high propagation loss and beam management challenges, particularly in out-of-coverage scenarios, leading to throughput drops and baseband interruptions, which affect user experience.
Innovation Solution
Implement sensor-assisted beam management and antenna selection using motion sensors like accelerometers and gyroscopes to detect static or mobile scenarios, optimizing beamforming and beam management by detecting out-of-coverage conditions and adjusting beam selection accordingly, thereby reducing the need for full beam acquisition processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If full beam acquisition process is performed in out-of-coverage scenarios, then beam management reliability is improved, but baseband interruption recovery time increases
Solution Approach 1:
The system performs preliminary beam management actions by detecting out-of-coverage scenarios using sensor data (accelerometer, gyroscope, magnetometer) before baseband interruptions occur. By identifying static versus mobile scenarios in advance and pre-determining appropriate beam management strategies, the system avoids the need for full beam acquisition processes during interruptions, thus reducing recovery time while maintaining reliability
Solution Approach 2:
The system dynamically adapts beam management strategies based on real-time sensor data indicating whether the device is in a static or mobile scenario. This dynamic adjustment allows the system to apply simplified beam management for static devices (reducing recovery time) while maintaining robust beam management for mobile devices (preserving reliability), resolving the contradiction between these two requirements
2Loss of time
If sensor-assisted beam management is implemented, then baseband interruption recovery time is reduced, but device complexity increases
Solution Approach 1:
The system uses sensor data (accelerometer, gyroscope, magnetometer) as an intermediary to infer device motion state and determine whether the device is static or mobile. This intermediary approach allows the system to make intelligent beam management decisions without directly complex beam processing, reducing recovery time while managing complexity through sensor-based classification
Solution Approach 2:
The system changes the parameter space for beam management by using sensor-derived motion state parameters (static vs. mobile classification) to select appropriate beam management strategies. This parameter transformation simplifies the decision-making process compared to traditional continuous beam sweeping, reducing recovery time while keeping device complexity manageable through discrete parameter-based strategy selection
3Device complexity
If traditional beam management is used without sensor assistance, then device complexity is minimized, but throughput drops in out-of-coverage scenarios
Solution Approach 1:
The system incorporates sensor feedback (accelerometer, gyroscope, magnetometer data) to continuously monitor device motion state and provide feedback for beam management decisions. This feedback mechanism enables the system to adapt beam management strategies in real-time based on actual device conditions, maintaining high throughput in out-of-coverage scenarios while keeping complexity manageable through feedback-driven adaptive control
Data Source
AI summary
This application describes systems and processes for sensor-assisted antenna and beam selection for wireless networks. The systems and processes are configured to detect out of coverage (OoC) scenarios and perform beam management in response to detecting the OoC scenario. The systems and methods are configured to perform beam management during baseband interruption scenarios. In each scenario, the device (e.g., user equipment UE) is configured to determine whether the UE is static or mobile.