Digital Radio Receiver Interference Modeling for EV DAB Noise
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Solution Overview
Problem
Electric Vehicles (EVs) produce diverse interference signals that degrade or prevent the reception of Digital Audio Broadcasting (DAB) data and audio streams, and existing DAB receivers often treat this interference as noise without effectively mitigating it.
Innovation Solution
A three-stage AI/ML-based interference rejection system that utilizes Bayesian probability theory and Hidden Markov Models (HMM) to construct, parameterize, and continuously fine-tune signal and interference models, incorporating trellis representations and neural networks for rapid adaptation to EV interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing DAB receivers treat EV interference as noise without mitigation, then device complexity is reduced, but reception quality deteriorates
Solution Approach 1:
The system performs preliminary interference modeling during off-device training stages, constructing signal and interference models before actual reception. This pre-processing of interference characteristics allows the receiver to quickly adapt to EV interference without requiring complex real-time processing, thus improving reception quality while controlling device complexity
Solution Approach 2:
The patent introduces an intermediary interference model that acts as a mediator between the received signal and the decoder. This model represents EV interference separately from the desired signal, allowing the system to distinguish and mitigate interference effects without requiring the entire receiver architecture to be fundamentally redesigned, thereby balancing reception quality improvement with acceptable device complexity
2Reliability
If a three-stage AI/ML-based interference rejection system is implemented, then reception quality improves, but device complexity increases
Solution Approach 1:
The interference rejection system is divided into three distinct stages: off-device training for signal modeling, on-device training for interference modeling, and real-time reception with model application. This segmentation allows complex AI/ML processing to be distributed, with computationally intensive tasks performed offline or during initialization, reducing the burden on the actual receiving device while maintaining high reception quality
Solution Approach 2:
The system uses parameterized signal and interference models that can be adapted to different EV interference conditions. By changing model parameters rather than restructuring the entire system, the patent enables flexible adaptation to various interference scenarios, improving reception quality across different conditions without requiring proportional increases in device complexity for each scenario
3Reliability
If the system rapidly adapts to real-time EV interference conditions, then reception quality is maintained, but processing time and complexity increase
Solution Approach 1:
The off-device training stage performs preliminary signal modeling before the device needs to receive signals. By pre-construcing signal models and storing them for later use, the system eliminates the need for time-consuming real-time signal analysis during actual reception, allowing rapid adaptation to interference conditions while minimizing processing time loss
Solution Approach 2:
The system implements feedback mechanisms where the received signal and known interference characteristics are used to update and refine the interference model in real-time. This feedback loop allows the system to rapidly adapt to changing EV interference conditions by continuously improving the model accuracy, maintaining reception quality without requiring excessive processing time for each adaptation cycle
Data Source
AI summary
One example discloses a digital radio signal receiver, including: wherein the receiver is configured to be coupled to a device; wherein the device is coupled to receive an RF signal; wherein the RF signal includes a desired signal and interference; wherein the receiver is configured to receive a first signal and interference model from an off-device training stage; an on-device training stage configured to construct a second signal and interference model based on the first signal and interference model and the RF signal received by the device; and a decoder configured to generate a set of data from the RF signal based on the second signal and interference model.