Nvidia corporation (20240097750). FREQUENCY DIVISION MULTIPLEXING WITH NEURAL NETWORKS IN RADIO COMMUNICATION SYSTEMS simplified abstract
Contents
- 1 FREQUENCY DIVISION MULTIPLEXING WITH NEURAL NETWORKS IN RADIO COMMUNICATION SYSTEMS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 FREQUENCY DIVISION MULTIPLEXING WITH NEURAL NETWORKS IN RADIO COMMUNICATION SYSTEMS - 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 Original Abstract Submitted
FREQUENCY DIVISION MULTIPLEXING WITH NEURAL NETWORKS IN RADIO COMMUNICATION SYSTEMS
Organization Name
Inventor(s)
Jakob Richard Hoydis of Paris (DE)
Sebastain Cammerer of Tuebingen (DE)
Alexander Keller of Berlin (DE)
Fayçal Aït Aoudia of Saint-Cloud (FR)
FREQUENCY DIVISION MULTIPLEXING WITH NEURAL NETWORKS IN RADIO COMMUNICATION SYSTEMS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240097750 titled 'FREQUENCY DIVISION MULTIPLEXING WITH NEURAL NETWORKS IN RADIO COMMUNICATION SYSTEMS
Simplified Explanation
The patent application describes a system that uses machine learning to determine transmitted signals in communication systems utilizing orthogonal frequency division multiplexing.
- The system includes receiving antennas to capture signals over specific resource elements of a resource grid.
- Each resource element is associated with different radio subcarriers and/or data symbols.
- The received signals consist of multiple transmitted streams.
- A processing device utilizes neural network models to analyze the received signals and identify the transmitted data symbols.
Potential Applications
This technology can be applied in wireless communication systems, such as 5G networks, to improve signal detection and decoding processes.
Problems Solved
This technology helps in accurately determining transmitted signals in complex communication systems, leading to enhanced data transmission efficiency and reliability.
Benefits
The use of machine learning and neural networks improves the accuracy and speed of signal detection and decoding, resulting in better overall performance of communication systems.
Potential Commercial Applications
This technology can be utilized in telecommunications companies, network equipment manufacturers, and other industries involved in wireless communication technology.
Possible Prior Art
One possible prior art could be the use of traditional signal processing techniques in communication systems to determine transmitted signals.
Unanswered Questions
How does this technology compare to traditional signal processing methods in terms of accuracy and efficiency?
This article does not provide a direct comparison between this technology and traditional signal processing methods.
What are the potential limitations or challenges in implementing machine learning for signal detection in communication systems?
The article does not address any potential limitations or challenges that may arise in implementing machine learning for signal detection in communication systems.
Original Abstract Submitted
disclosed are apparatuses, systems, and techniques that may use machine learning for determining transmitted signals in communication systems that deploy orthogonal frequency division multiplexing. a system for performing the disclosed techniques includes receiving (rx) antennas to receive rx signals, each rx signal received over a respective resource element of a resource grid. individual resource elements of the resource grid are associated with different radio subcarriers and/or data symbols. the rx signals include a combination of a plurality of transmitted (tx) streams. the system further includes a processing device to process the rx signals using one or more neural network models to determine tx data symbols transmitted via the plurality of tx streams.
