Jump to content

18617740. Nonlinear Neural Network with Phase Normalization for Base-Band Modelling of Radio-Frequency Non-Linearities simplified abstract (Nokia Solutions and Networks Oy)

From WikiPatents
Revision as of 05:40, 18 October 2024 by Unknown user (talk) (Creating a new page)
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)

Nonlinear Neural Network with Phase Normalization for Base-Band Modelling of Radio-Frequency Non-Linearities

Organization Name

Nokia Solutions and Networks Oy

Inventor(s)

Arne Fischer-buhner of Leuven (BE)

Nonlinear Neural Network with Phase Normalization for Base-Band Modelling of Radio-Frequency Non-Linearities - A simplified explanation of the abstract

This abstract first appeared for US patent application 18617740 titled 'Nonlinear Neural Network with Phase Normalization for Base-Band Modelling of Radio-Frequency Non-Linearities

Simplified Explanation: The patent application discusses a method to mitigate non-linearity in a data communication chain by using a neural network.

  • **Key Features and Innovation:**
   - Obtaining a data communication signal with complex-valued input samples.
   - Capturing a current sample and a set of delayed samples from the input.
   - Generating a phase-normalized input signal by normalizing the phase of the samples.
   - Providing the phase-normalized signal to a neural network to mitigate non-linearity.
   - Denormalizing the output sample to restore the phase of the data communication signal.
  • **Potential Applications:**
   - Improving the performance and efficiency of data communication systems.
   - Enhancing signal processing in telecommunications and networking technologies.
  • **Problems Solved:**
   - Addressing non-linearity issues in data communication chains.
   - Improving the accuracy and reliability of data transmission.
  • **Benefits:**
   - Enhanced signal quality and reduced distortion.
   - Increased data transmission speeds and efficiency.
  • **Commercial Applications:**
   - Telecom companies can use this technology to optimize their data communication networks.
   - Manufacturers of networking equipment can integrate this innovation to enhance their products.
  • **Prior Art:**
   - Researchers and engineers in the field of signal processing and telecommunications may have explored similar methods to mitigate non-linearity in data communication chains.
  • **Frequently Updated Research:**
   - Stay updated on advancements in neural network applications in signal processing and data communication technologies.

Questions about Mitigation of Non-Linearity in Data Communication Chain: 1. How does the normalization of phase help in mitigating non-linearity in data communication chains? 2. What are the potential challenges in implementing neural networks for non-linearity mitigation in data communication systems?


Original Abstract Submitted

Various example embodiments relate to mitigation of a non-linearity in a data communication chain. A method may include: obtaining a data communication signal including a plurality of complex-valued input samples; capturing, from the plurality of complex-valued input samples, a current sample and a set of delayed samples; generating a phase-normalized input signal based on normalizing phase of the current sample and the set of delayed samples by a normalization term, wherein the normalization term is common for the current sample and the set of delayed samples; providing the phase-normalized input signal to a neural network configured to mitigate non-linearity of a data communication chain and to output a complex-valued output sample for each of the plurality of complex-valued input samples; and denormalizing phase of the complex-valued output sample by a denormalization term configured to restore phase of the data communication signal.

(Ad) Transform your business with AI in minutes, not months

Custom AI strategy tailored to your specific industry needs
Step-by-step implementation with measurable ROI
5-minute setup that requires zero technical skills
Get your AI playbook

Trusted by 1,000+ companies worldwide

Cookies help us deliver our services. By using our services, you agree to our use of cookies.