Radio Frequency Receiver Pulse Detection for Low-Power Signal Classification
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Solution Overview
Problem
Software-defined radios consume excessive power due to high energy demands from radio frequency-to-digital converters and unnecessary signal sampling, particularly in applications like the Internet of Things where energy efficiency is crucial.
Innovation Solution
A method and device using an artificial neural network to determine the nature of a radio frequency signal by analyzing pulse trains, avoiding unnecessary sampling and demodulation, and employing a wake-up system to activate power-consuming components only when the signal is compatible.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-frequency sampling clocks are used to obtain accurate digital signals, then measurement precision is improved, but energy consumption increases
Solution Approach 1:
The system performs preliminary classification of radio frequency signals using a wake-up receiver and neural network before activating the main sampling and demodulation components. This preliminary action identifies irrelevant signals early, preventing unnecessary activation of high-power sampling clocks and processing units, thus resolving the contradiction between maintaining measurement precision and reducing energy consumption.
2Reliability
If signal sampling and demodulation are performed for all received signals, then reliability of signal processing is improved, but energy consumption increases
Solution Approach 1:
The signal processing system is segmented into two distinct parts: a low-power wake-up receiver that performs initial signal classification, and a main receiver that performs full sampling and demodulation only when needed. This segmentation allows the system to maintain reliability for relevant signals while dramatically reducing energy consumption by avoiding processing of irrelevant signals.
Solution Approach 2:
A neural network classifier acts as an intermediary between the wake-up receiver and the main signal processing components. This intermediary analyzes pulse train characteristics and determines whether incoming signals require full processing, thereby preventing unnecessary activation of high-power components and resolving the contradiction between reliability and energy consumption.
3Productivity
If full signal processing is performed on all radio frequency signals, then productivity of signal analysis is improved, but energy consumption increases
Solution Approach 1:
The system performs preliminary classification of radio frequency signals using a wake-up receiver and neural network before activating the main sampling and demodulation components. This preliminary action identifies irrelevant signals early, preventing unnecessary activation of high-power sampling clocks and processing units, thus resolving the contradiction between maintaining measurement precision and reducing energy consumption.
Data Source
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AI summary
According to one aspect, a radio frequency receiver device is proposed comprising: - an input interface (ANT, AMP) configured to receive a radio frequency signal of a given nature and convert it into an electrical signal, - detection means (CP1, CP2, CP3) configured to detect at least one voltage level in the electrical signal, - a pulse generator (IG) configured to develop at least one representative pulse train (TI1, TI2, TI3) of the detected voltage levels, o a processing unit (UT) configured to determine the nature of the radio frequency signal from said at least one pulse train (TI1, TI2, TI3).