View source for 18267138. Channel Estimation Using Machine Learning simplified abstract (Nokia Technologies Oy)

=Channel Estimation Using Machine Learning=


==Organization Name==

[[:Category:Nokia Technologies Oy|Nokia Technologies Oy]]


[[Category:Nokia Technologies Oy]]

==Inventor(s)==

[[:Category:Wolfgang Zirwas of Munich (DE)|Wolfgang Zirwas of Munich (DE)]][[Category:Wolfgang Zirwas of Munich (DE)]]

[[:Category:Brenda Vilas Boas of Neubiberg (DE)|Brenda Vilas Boas of Neubiberg (DE)]][[Category:Brenda Vilas Boas of Neubiberg (DE)]]

[[:Category:Zexian Li of Espoo (FI)|Zexian Li of Espoo (FI)]][[Category:Zexian Li of Espoo (FI)]]

[[:Category:Amir Mehdi Ahmadian Tehrani of Munich (DE)|Amir Mehdi Ahmadian Tehrani of Munich (DE)]][[Category:Amir Mehdi Ahmadian Tehrani of Munich (DE)]]

[[:Category:Oana-Elena Barbu of Aalborg (DK)|Oana-Elena Barbu of Aalborg (DK)]][[Category: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.

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### 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.



[[Category:H04L25/02]]