V2X Radio mmWave Beam Alignment via Predictive Scheduling
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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 high mobility environments like vehicle applications, as they take too long to complete the beam alignment process, making mmWave communication impractical for vehicles.
Innovation Solution
A modification system and feedback system that optimize mmWave communication scheduling by collecting and analyzing data from vehicles to determine optimal beam alignment settings, using a machine learning module to generate a transmission schedule and modify V2X radio settings in real-time for efficient beam alignment, allowing for consistent mmWave communication in vehicular environments.
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 process takes too long for high mobility vehicle applications
Solution Approach 1:
The system performs preliminary beam alignment actions by predicting future beam directions based on current vehicle trajectory and speed. The beam alignment is prepared in advance for upcoming communication needs, allowing the vehicle to skip lengthy beam sweeping processes during actual communication events.
Solution Approach 2:
The beam alignment system dynamically adapts to vehicle motion by continuously updating beam directions based on real-time trajectory predictions. The system adjusts beamforming parameters dynamically to track moving vehicles, maintaining alignment without requiring repeated beam sweeping.
2Productivity
If fixed rule scheduling is used for mmWave communication, then communication can occur, but it is not optimal for moving vehicles
Solution Approach 1:
The scheduling system transitions from fixed rules to dynamic scheduling that adapts to vehicle motion. Beam directions, timing, and resource allocation are continuously adjusted based on predicted vehicle trajectories and communication requirements, optimizing efficiency for moving vehicles.
Solution Approach 2:
The system implements feedback mechanisms where communication outcomes and vehicle position data are used to refine future scheduling decisions. The scheduler learns from past communication successes and failures to optimize beam alignment timing and direction for subsequent vehicle interactions.
Data Source
AI summary
The disclosure includes embodiments for modifying a vehicle-to-everything (V2X) radio of an ego vehicle that is a connected vehicle. In some embodiments, a method includes analyzing, by a machine learning module executed by a processor, a local dynamic map generated by the ego vehicle to determine schedule data describing a schedule for the ego vehicle to transmit a millimeter wave (mmWave) message to a remote vehicle. The method includes transmitting a V2X message including the schedule data for receipt by the remote vehicle so that the remote vehicle has access to the schedule. The method includes modifying an operation of the V2X radio of the ego vehicle based on the schedule so that the V2X radio transmits the mmWave message to the remote vehicle in compliance with the schedule. The method includes transmitting the mmWave message to the remote vehicle in compliance with the schedule.