V2X mmWave Radio Beam Alignment via Cloud Feedback Database
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Solution Overview
Problem
Existing solutions for beam alignment in millimeter wave (mmWave) communication, such as beam training by beam sweeping, are inadequate for vehicular applications due to their inability to complete the alignment process in a timely manner, which is essential for high mobility environments like vehicle communications.
Innovation Solution
A modification system and feedback system that collect and analyze beam alignment data from various vehicles to build a database of optimal settings, allowing an ego vehicle to quickly determine and implement the best beam alignment settings for mmWave communication with other vehicles based on real-time scenario data, using a connected computing device like a cloud server.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If beam training by beam sweeping is used for mmWave communication, then beam alignment can be achieved, but the alignment process takes too much time for high mobility vehicular applications
Solution Approach 1:
The system performs beam alignment preparation in advance by collecting and storing beam pair data from multiple vehicles in different scenarios before the actual communication occurs. When communication is needed, the system queries the database for pre-computed optimal beam settings based on current scenario conditions, eliminating the need for time-consuming real-time beam sweeping.
Solution Approach 2:
Instead of performing actual beam sweeping in real-time, the system uses copied beam alignment data from previous similar scenarios stored in the database. The modification system retrieves beam pair settings that were successfully used in analogous situations and applies them to the current communication scenario, significantly reducing alignment time.
2Reliability
If existing beam training solutions are used, then mmWave communication can be established, but they do not work effectively in high mobility vehicular environments
Solution Approach 1:
The feedback system collects beam alignment performance data from multiple vehicles in various scenarios and feeds this information back to build and update the database. The modification system then uses this feedback information to determine optimal beam settings for current scenarios, enabling adaptive beam alignment that works effectively in high mobility vehicular environments.
Solution Approach 2:
The system dynamically adapts beam alignment settings based on real-time scenario conditions by querying the database for appropriate beam pairs. The modification system adjusts beam settings according to current vehicle positions, speeds, and environmental conditions, making the system flexible and adaptable to changing vehicular scenarios.
Data Source
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AI summary
The disclosure includes embodiments for modifying a vehicle-to-everything (V2X) radio of a first endpoint based on beam alignment feedback data. In some embodiments, a method for the first endpoint includes detecting an intention of the first endpoint to exchange a first millimeter wave (mmWave) message with a second endpoint. The method includes determining scenario data describing a scenario of one or more of the first endpoint and the second endpoint. The method includes requesting a recommended beam alignment setting from a connected computing device based on the scenario data. The method includes receiving feedback data describing the recommended beam alignment setting from the connected computing device. The method includes modifying an operation of the V2X radio of the first endpoint based on the recommended beam alignment setting so that the V2X radio of the first endpoint exchanges the mmWave message with the second endpoint using the recommended beam alignment setting.