Machine Learning Inter-Frequency Signal Estimation
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Solution Overview
Problem
Existing wireless communication systems face inefficiencies in determining the optimal frequency band for communications, leading to delays, increased processing overhead, and power consumption due to frequent re-tuning of transceivers to measure signal properties on different frequency bands.
Innovation Solution
The use of machine learning models to predict the properties of signals on a second frequency band based on measured properties on a first frequency band, allowing devices to remain tuned to the initial frequency band until a transition is necessary, thereby reducing the need for frequent re-tuning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If devices frequently re-tune transceivers to measure signal properties on different frequency bands, then frequency band selection accuracy is improved, but latency and processing overhead increase
Solution Approach 1:
The system performs preliminary measurements of signal properties on a first frequency band and uses machine learning models to predict signal properties on second frequency bands before actually switching. This allows the device to prepare frequency selection decisions in advance without needing to physically re-tune the transceiver to measure each frequency band's actual signal properties, thereby reducing measurement time while maintaining selection accuracy.
Solution Approach 2:
Machine learning models serve as an intermediary between the measured signal properties on the first frequency band and the predicted signal properties on second frequency bands. Instead of directly measuring all frequency bands, the ML model acts as a mediator that translates available measurement data into predictions about unmeasured frequency bands, eliminating the need for direct transceiver re-tuning to each frequency.
2Measurement precision
If devices frequently re-tune transceivers to measure signal properties on different frequency bands, then frequency band selection accuracy is improved, but power consumption increases
Solution Approach 1:
The system performs preliminary signal property measurements only on the current first frequency band and uses these measurements to predict properties on second frequency bands via machine learning. This eliminates the need to physically switch the transceiver to measure each candidate frequency band, significantly reducing the number of times the transceiver must be re-tuned and thus lowering overall power consumption while maintaining accurate frequency selection.
Solution Approach 2:
Machine learning models act as an intermediary that replaces the energy-intensive physical process of transceiver re-tuning. Instead of consuming power to actually measure signal properties on multiple frequency bands through hardware switching, the system uses computational predictions based on ML models, which consume far less energy while achieving the same frequency selection objective.
3Measurement precision
If devices frequently re-tune transceivers to measure signal properties on different frequency bands, then frequency band selection accuracy is improved, but processing overhead increases
Solution Approach 1:
The system performs signal property measurements only once on the first frequency band and then uses machine learning models to predict properties on second frequency bands. This preliminary measurement approach eliminates the need to repeatedly re-tune and measure across multiple frequency bands, significantly reducing processing overhead while maintaining the accuracy needed for effective frequency selection.
Solution Approach 2:
Machine learning models serve as an intermediary that replaces the complex, repeated process of transceiver re-tuning and measurement. Instead of directly performing multiple physical measurements which generate high processing overhead, the system uses ML models to translate single measurement results into predictions about multiple frequency bands, simplifying the overall processing required for frequency selection.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques and apparatus for inter-frequency signal property prediction using machine learning techniques. An example method generally includes determining one or more properties of a received signal from a wireless device on a first frequency band. An estimate of the one or more properties of signals on a second frequency band is generated, while a transceiver is tuned to the first frequency band, using a machine learning model trained to generate the estimate based on the determined one or more properties of the received signal on the first frequency band. The transceiver is tuned to the second frequency band for subsequent communications with the wireless device based on the estimate of the one or more properties of the signals on the second frequency band.