Antenna Offset Learning for Perception-Aided Wireless Communication
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Solution Overview
Problem
Existing wireless communication systems face challenges in accurately determining the antenna position of user equipment, especially in scenarios where the antenna is small and the vehicle is moving, leading to issues with beam alignment and communication quality.
Innovation Solution
A processor-implemented method that uses a stream of inputs from sensors, such as cameras and GPS, to generate a dynamic segmentation mask and determine a trajectory for the sensors. This information is then used to predict the antenna position for wireless communication, enabling more accurate beam alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to determine antenna position, then the system is simpler to implement, but beam alignment accuracy deteriorates
Solution Approach 1:
The patent introduces sensors (cameras, GPS, IMU) as intermediary devices to observe and track the vehicle and antenna position. These sensors provide indirect measurement data that is processed to determine accurate antenna position, resolving the contradiction between measurement precision and device complexity by adding specialized intermediary components rather than complicating the core determination system
Solution Approach 2:
The system performs preliminary actions by continuously tracking the vehicle's trajectory and position using sensors before the actual beam alignment is needed. This pre-acquisition of position data allows for more accurate antenna position determination when required, improving measurement precision without adding complexity to the critical alignment moment
2Volume of moving object
If the antenna is made smaller to reduce device size, then the device becomes more compact, but beam alignment accuracy deteriorates
Solution Approach 1:
Sensors serve as intermediaries to track the precise position and trajectory of the compact antenna. By using external observation devices (cameras, GPS) to monitor the antenna's location rather than relying on the antenna's physical size for alignment, the system maintains beam alignment accuracy despite the reduced antenna volume
Solution Approach 2:
The patent replaces mechanical alignment methods that would require large physical antennas with sensor-based trajectory tracking and computational prediction. This substitution allows small antennas to achieve accurate beam alignment through software-based position determination rather than mechanical adjustment or large physical dimensions
3Measurement precision
If sensor-based trajectory tracking is implemented, then beam alignment accuracy improves, but energy consumption increases
Solution Approach 1:
The sensors implemented in the vehicle (cameras, GPS, IMU) serve multiple functions: they track the vehicle's overall position, monitor the antenna's movement, and provide data for beam alignment prediction. By making these sensors multi-functional rather than dedicating separate devices solely for antenna tracking, the patent improves beam alignment accuracy without proportionally increasing energy consumption
Solution Approach 2:
The system uses the vehicle's existing sensor infrastructure to serve the additional function of antenna position tracking. Rather than adding dedicated high-power tracking devices, the patent repurposes already-present sensors to provide trajectory information, reducing the incremental energy cost while maintaining improved alignment accuracy
Data Source
AI summary
A processor-implemented method for learning an antenna offset for perception-aided wireless communication includes receiving a stream of inputs from one or more sensors. A dynamic segmentation mask corresponding to an object observed by the one or more sensors is generated based on the stream of inputs. A trajectory for the one or more sensors is determined based on the dynamic segmentation mask. An antenna position for the object is predicted based on the trajectory.