Telefonaktiebolaget lm ericsson (publ) (20240137258). METHOD AND DEVICE(S) FOR SUPPORTING MACHINE LEARNING BASED CREST FACTOR REDUCTION AND DIGITAL PREDISTORTION simplified abstract
Contents
- 1 METHOD AND DEVICE(S) FOR SUPPORTING MACHINE LEARNING BASED CREST FACTOR REDUCTION AND DIGITAL PREDISTORTION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD AND DEVICE(S) FOR SUPPORTING MACHINE LEARNING BASED CREST FACTOR REDUCTION AND DIGITAL PREDISTORTION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
METHOD AND DEVICE(S) FOR SUPPORTING MACHINE LEARNING BASED CREST FACTOR REDUCTION AND DIGITAL PREDISTORTION
Organization Name
telefonaktiebolaget lm ericsson (publ)
Inventor(s)
S M Shahrear Tanzil of Ottawa (CA)
Ashim Biswas of Sollentuna (SE)
METHOD AND DEVICE(S) FOR SUPPORTING MACHINE LEARNING BASED CREST FACTOR REDUCTION AND DIGITAL PREDISTORTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240137258 titled 'METHOD AND DEVICE(S) FOR SUPPORTING MACHINE LEARNING BASED CREST FACTOR REDUCTION AND DIGITAL PREDISTORTION
Simplified Explanation
The patent application describes a method and device for supporting the performance of machine learning based CFR (Crest Factor Reduction) and DPD (Digital Pre-Distortion) on multiple digital input signals related to different frequency bands in a wireless communication network.
- The device obtains multiple digital input signals as complex valued signals.
- The device performs feature construction on the input signals to provide constructed feature signals based on predefined feature types such as real part, imaginary part, absolute value, and phase of the sample.
- The constructed feature signals help in signal conditioning before power amplification and transmission in the frequency bands.
Potential Applications
This technology can be applied in:
- Wireless communication networks
- Signal processing systems
- Telecommunication infrastructure
Problems Solved
This technology helps in:
- Improving signal quality
- Reducing interference
- Enhancing transmission efficiency
Benefits
The benefits of this technology include:
- Enhanced performance of CFR and DPD
- Improved signal accuracy
- Optimal power amplification
Potential Commercial Applications
Potential commercial applications of this technology include:
- Telecommunication equipment manufacturing
- Wireless network infrastructure development
- Signal processing software development
Possible Prior Art
One possible prior art for this technology could be:
- Existing CFR and DPD techniques in wireless communication systems
Unanswered Questions
How does this technology compare to traditional signal processing methods?
This article does not provide a direct comparison to traditional signal processing methods in terms of performance and efficiency.
What are the specific machine learning algorithms used in this technology?
The article does not mention the specific machine learning algorithms employed in the CFR and DPD processes.
Original Abstract Submitted
method and device(s) for supporting performance of machine learning based cfr and dpd on multiple digital input signals relating to different frequency bands, respectively, in order to signal condition said signals before power amplification and subsequent transmission in said frequency bands by a wireless communication network. the device(s) obtain said multiple digital input signals as complex valued signals. the device(s) perform feature construction that takes said multiple digital input signals as input and provides constructed feature signals according to predefined constructed feature types as output. said predefined constructed feature types relate to at least the following per complex valued sample of the obtained complex valued multiple digital input signals the real part of the sample, the imaginary part of the sample and at least one of the absolute value of the sample and the phase of the sample.
