Neural Network RF Receiver for Interoperable Signal Processing
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Solution Overview
Problem
Existing wireless receivers face interoperability challenges due to the complexity of hosting all necessary processing for various waveforms, making it difficult for radios from different vendors using different technologies to communicate effectively.
Innovation Solution
The implementation of a neural network in a receiver device to process radio frequency signals, which simplifies the architecture, reduces size, weight, and power consumption, and enables flexible interoperability by classifying and processing modulation schemes, replacing traditional signal processing components such as timing recovery and demodulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional signal processing components are used to process all waveform types, then processing capability is improved, but device complexity increases
Solution Approach 1:
The patent implements a universal neural network processor that can handle multiple waveform types and modulation schemes through a single unified architecture. The neural network is trained to perform various signal processing functions including timing recovery, frequency offset correction, and demodulation for different waveforms, replacing the need for multiple dedicated processing components.
Solution Approach 2:
The patent replaces traditional mechanical signal processing components with a neural network-based system. Specifically, conventional signal processing blocks such as timing recovery circuits, frequency offset correctors, and demodulators are substituted with a trained neural network that performs these functions through learned patterns rather than fixed algorithms.
2Adaptability or versatility
If multiple processing components are included to support different waveforms, then interoperability is improved, but size and weight increase
Solution Approach 1:
The patent merges multiple separate signal processing functions into a single integrated neural network processor. Functions that were traditionally implemented as separate components (timing recovery, frequency offset correction, demodulation) are combined into one unified neural network that processes all these tasks sequentially or in parallel within a single device.
Solution Approach 2:
The neural network processor is designed as a universal platform capable of handling multiple waveform types and modulation schemes, eliminating the need for multiple dedicated processing units for different radio technologies.
3Measurement precision
If traditional signal processing components are used, then processing accuracy is improved, but power consumption increases
Solution Approach 1:
The patent replaces power-intensive traditional signal processing components with a neural network-based system. The neural network, once trained, performs signal processing tasks using learned patterns that require less computational power compared to conventional algorithms, thereby reducing overall power consumption while maintaining processing accuracy.
Data Source
AI summary
A method of processing a radio frequency signal includes: receiving the radio frequency signal at an antenna of a receiver device; processing, by a radio frequency front-end device, the radio frequency signal; converting, by an analog-to-digital converter, the analog signal to a digital signal; receiving, by a neural network, the digital signal; and processing, by the neural network, the digital signal to produce an output.