Nvidia Corporation

Sebastian Cammerer

Alexander Georg Keller

Faycal Ait Aoudia

Jakob Hoydis

Matthijs Jules Van Keirsbilck

Jakob Richard Hoydis

Fayçal Aït Aoudia

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.

Frequency division multiplexing with neural networks in radio communication systems

Neural network-based structures for action user equipment device detection, estimation of time-of-arrival, and estimation of carrier frequency offset utilized with the narrowband physical random-access channel of wireless communication systems. The structure includes a neural network to generate predictions of active user equipment devices, and a twin neural network to generate time-of-arrival predictions for signals from the user equipment devices and carrier frequency offset predictions for signals from the user equipment devices.

Deep learning-based wireless communication synchronization structures

Various embodiments and implementations of graph-neural-network (GNN)-based decoding applications are disclosed. The GNN-based decoding schemes are broadly applicable to different coding schemes, and capable of operating on both binary and non-binary codewords, in different implementations. Advantageously, the inventive GNN-based decoding is scalable, even with arbitrary block lengths, and not subject to typical limits with respect to dimensionality. Decoding performance of the inventive GNN-based techniques demonstrably matches or outpaces BCH and LDPC (both regular and 5G NR) decoding algorithms, while exhibiting improvements with respect to number of iterations required and scalability of the GNN-based approach. These inventive concepts are implemented, according to various embodiments, as methods, systems, and computer program products.

Growing community of inventors

Tuebingen, Germany

Sebastian Cammerer