Telefonaktiebolaget lm ericsson (publ) (20240291508). SYSTEMS AND METHODS FOR MULTIBAND LINEARIZATION ARCHITECTURE USING KERNEL REGRESSION simplified abstract
Contents
SYSTEMS AND METHODS FOR MULTIBAND LINEARIZATION ARCHITECTURE USING KERNEL REGRESSION
Organization Name
telefonaktiebolaget lm ericsson (publ)
Inventor(s)
Ashim Biswas of Sollentuna (SE)
SYSTEMS AND METHODS FOR MULTIBAND LINEARIZATION ARCHITECTURE USING KERNEL REGRESSION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240291508 titled 'SYSTEMS AND METHODS FOR MULTIBAND LINEARIZATION ARCHITECTURE USING KERNEL REGRESSION
- Simplified Explanation:**
This patent application discusses systems and methods for multiband linearization using kernel regression in a transmitter. It involves transforming input signals from different bands, predistorting them using a radial basis function kernel regression, and transmitting the predistorted signals.
- Key Features and Innovation:**
- Transforming input signals from multiple bands into a constructed input vector space
- Predistorting the transformed signals using a radial basis function kernel regression
- Transmitting the predistorted signals
- Reduced computational complexity compared to traditional methods
- Implementation complexity is simplified with a 1D lookup table approach
- Potential Applications:**
This technology can be applied in various multiband transmitters, communication systems, and wireless devices where linearization is required to improve signal quality and efficiency.
- Problems Solved:**
This technology addresses the challenges of nonlinear distortion in multiband transmitters, reducing computational and implementation complexity while improving overall performance.
- Benefits:**
- Improved signal quality
- Reduced computational complexity
- Simplified implementation
- Enhanced efficiency in multiband transmitters
- Commercial Applications:**
The technology can be utilized in the telecommunications industry for base stations, satellite communication systems, and other wireless devices requiring multiband linearization for optimal performance.
- Questions about Multiband Linearization using Kernel Regression:**
1. How does kernel regression help in linearizing signals in multiband transmitters? 2. What are the advantages of using a 1D lookup table implementation in this context?
Original Abstract Submitted
systems and methods for multiband linearization using kernel regression are provided. in some embodiments, a method includes, for each band of the multiband transmitter: transforming a group of input signals from one or more bands into a constructed input vector space to provide transformed input signals; predistorting the transformed input signals to provide a respective group of predistorted input signals in accordance with a radial basis function (rbf) kernel regression; and transmitting the respective group of predistorted input signals. in this way, some advantages include a semi blind approach as one need not to account for the non-linearity order as in volterra-based dpd for example, only the memory depth is needed to be incorporated to the input vector space. the computational complexity of dpd is reduced compared to volterra-based dpd. implementation complexity is relaxed by means of using a 1d lookup table implementation regardless of the number of bands.
