18233203. FREQUENCY DIVISION MULTIPLEXING WITH NEURAL NETWORKS IN RADIO COMMUNICATION SYSTEMS simplified abstract (NVIDIA Corporation)
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 18233203 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 that utilize 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 various communication systems, such as wireless networks, satellite communications, and radar systems.
Problems Solved
This innovation helps in accurately determining transmitted signals in complex communication systems, leading to improved signal processing and data transmission efficiency.
Benefits
The use of machine learning and neural networks enhances the accuracy and speed of signal analysis, resulting in better overall system performance and reliability.
Potential Commercial Applications
This technology can be utilized in 5G networks, IoT devices, autonomous vehicles, and other advanced communication systems for enhanced signal processing capabilities.
Possible Prior Art
One possible prior art could be the use of traditional signal processing techniques in communication systems to analyze transmitted signals. However, the integration of machine learning and neural networks for this purpose represents a novel approach in this field.
=== What are the specific neural network models used in the processing device for signal analysis? The patent application does not specify the exact neural network models employed in the system for determining transmitted signals.
=== How does the system handle interference and noise in the received signals during the signal analysis process? The patent application does not provide detailed information on how the system addresses interference and noise in the received signals, which could be crucial for real-world applications where signal quality may vary.
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.
