Filter Bank Control for Rapid Strong RF Signal Attenuation
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Solution Overview
Problem
Existing methods for identifying the correct filters in an intrinsically switched multiplexing filter (ISM) to attenuate strong signals are time-consuming and inefficient, particularly due to varying radio frequency (RF) signal conditions.
Innovation Solution
A frequency control device utilizing a reinforcement learning (RL) model to automatically select and apply search algorithms based on environmental state parameters, optimizing the configuration of ISM filters to quickly attenuate strong signals by enabling or disabling specific filters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional filter searching methods are used to identify the correct filters in an ISM, then the filters can be configured to attenuate strong signals, but the process is time-consuming and inefficient
Solution Approach 1:
The system employs machine learning models that automatically analyze environmental state parameters and independently determine the optimal filter configuration without requiring manual intervention or iterative searching, enabling the system to self-identify the correct filters rapidly
Solution Approach 2:
The patent transforms the filter selection problem from a time-consuming search process into a rapid parameter-based decision by using machine learning models that process environmental state parameters (such as RF signal characteristics, temperature, humidity) to directly predict the optimal filter configuration
2Adaptability or versatility
If the ISM uses multiple filters with different frequency passbands to handle varying RF signal conditions, then the system can adapt to different frequencies, but the complexity of selecting the correct filter configuration increases
Solution Approach 1:
The patent introduces machine learning models as intermediary components that bridge the gap between environmental state parameters and filter configuration decisions, automatically translating complex RF conditions into optimal filter selections without requiring complex control logic
Solution Approach 2:
The system pre-trains machine learning models with extensive RF signal data and environmental parameters before deployment, so that when the system operates, the filter configuration has already been optimized through prior learning, eliminating the need for complex real-time decision-making
Data Source
AI summary
A frequency control device is provided. The frequency control device is configured to receive a plurality of environmental state parameters of an environment; and convert the plurality of environmental state parameters to an environmental state vector. The frequency control device is also configured to receive the environmental state vector and a search request from a filter bank device communicatively coupled to the frequency control device, the search request being triggered by a power level of a radio frequency (RF) signal at an input or an output of the filter bank device; and determine, based on the environmental state vector, a control signal corresponding to a configuration of disabling a selected filter in a plurality of filters in the filter bank device to attenuate the RF signal in the input of the filter bank device.