18267138. Channel Estimation Using Machine Learning simplified abstract (Nokia Technologies Oy)
Contents
Channel Estimation Using Machine Learning
Organization Name
Inventor(s)
Wolfgang Zirwas of Munich (DE)
Brenda Vilas Boas of Neubiberg (DE)
Amir Mehdi Ahmadian Tehrani of Munich (DE)
Oana-Elena Barbu of Aalborg (DK)
Channel Estimation Using Machine Learning - A simplified explanation of the abstract
This abstract first appeared for US patent application 18267138 titled 'Channel Estimation Using Machine Learning
Simplified Explanation
The abstract describes a method for performing multiple-input and multiple-output channel estimation using machine learning models.
- Initial set of estimated channel information is generated from a first input set of channel information corresponding to a first plurality of radio-frequency chains.
- Second machine-learning model is used to generate a set of estimated channel phases from the initial set of estimated channel information and a second input set of channel information corresponding to a second plurality of radio-frequency chains.
- The initial set of estimated channel information and the set of estimated channel phases are combined to generate an enhanced set of estimated channel information.
---
- Potential Applications
- Wireless communication systems
- 5G and beyond networks
- Internet of Things (IoT) devices
- Problems Solved
- Efficient channel estimation in complex communication systems
- Improved performance in MIMO systems
- Enhanced data transmission reliability
- Benefits
- Increased data throughput
- Enhanced signal quality
- Reduced interference and latency
Original Abstract Submitted
A method for performing multiple-input and multiple-output channel estimation includes: generating, using a first machine-learning model, an initial set of estimated channel information from a first input set of channel information, wherein the first input set of channel information corresponds to a first plurality of radio-frequency chains, and wherein the estimated set of channel information corresponds to the first plurality of radio-frequency chains and a second plurality of radio-frequency chains; generating, using a second machine-learning model, a set of estimated channel phases from the initial set of estimated channel information and a second input set of channel information, wherein the second set of input channel information corresponds to the second plurality of radio-frequency chains; and combining the initial set of estimated channel information and the set of estimated channel phases to generate an enhanced set of estimated channel information.
