Spatial-Temporal Signal Adaptation for Connected Vehicle Communications
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Solution Overview
Problem
Connected vehicles face challenges in maintaining reliable and effective communication links due to high-speed movements, requiring more accurate and timely forecasting of conditions impacting vehicular operations, which current reactive operations fail to address effectively.
Innovation Solution
The system processes spatial and temporal data from multiple cameras to predict signal characteristics and adjust transmitter and receiver parameters, ensuring signal strength exceeds thresholds by leveraging environmental data and machine learning algorithms to adapt communication qualities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive operations based on real-time detected events are used, then the system responds to current conditions, but the response time is too slow due to high vehicle speeds leaving small time margins
Solution Approach 1:
The system performs preliminary forecasting of communication conditions using machine learning models to predict future signal strength and interference levels before they actually occur. This allows the connected vehicles to proactively adjust communication parameters in advance, rather than reacting after conditions deteriorate, thereby resolving the time margin issue caused by high-speed movement.
2Reliability
If more frequent transmissions are performed to maintain communication reliability at high speeds, then communication reliability improves, but energy consumption and interference increase
Solution Approach 1:
The system dynamically changes communication parameters such as transmission power, modulation scheme, and coding rate based on forecasted channel conditions. When poor conditions are predicted, parameters are adjusted to maintain reliability; when conditions are good, parameters are optimized to reduce energy consumption, thereby resolving the contradiction between reliability and energy use.
3Reliability
If transmission power is increased to maintain signal strength at high speeds, then signal detection reliability improves, but interference with other vehicles increases
Solution Approach 1:
The system applies different transmission power levels and signal characteristics to different spatial directions and time intervals based on local channel conditions forecasted by the machine learning model. This allows each vehicle to optimize its transmission locally rather than using uniform high power, maintaining signal detection reliability while minimizing interference to other vehicles in different locations.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, obtaining a plurality of inputs, wherein the plurality of inputs includes a first image captured by a first camera at a first point in time, processing the plurality of inputs to generate a first prediction regarding a first characteristic of a first signal associated with a first vehicle that is to be detected by a receiver at a second point in time that is subsequent to the first point in time, and modifying, based on the first prediction, a first parameter of a transmitter that emits the first signal, a second parameter of the receiver, or a combination thereof. Other embodiments are disclosed.