Search Results - alphan+sahin

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Methods and Procedures for Synchronization and Over-the-Air Computation
Reference #: 01615 The University of South Carolina is offering licensing opportunities for Methods and Procedures for Synchronization and Over-the-Air Computation Background: Over-the-air computation (OAC) reduces the communication latency that linearly increases with the number of devices in a wireless network for machine learning applications....
Published: 9/18/2023   |   Inventor(s): Alphan Sahin
Keywords(s): federated learning, non-coherent computation, over-the-air computation, software-defined radios, sychronization
Category(s): Software and Computing, Engineering and Physical Sciences
Over-the-Air Computation Methods Based on Balanced Number Systems for FEEL
Reference #: 01612 The University of South Carolina is offering licensing opportunities for Over-the-Air Computation Methods Based on Balanced Number Systems for FEEL Background: Federated edge learning (FEEL) is a distributed learning framework that leverages the computational powers of edge devices (EDs) and uses the local data at the EDs without...
Published: 10/24/2022   |   Inventor(s): Alphan Sahin
Keywords(s): federated learning, non-coherent computation, over-the-air computation, quantization, signed-digit number system
Category(s): Software and Computing, Engineering and Physical Sciences
Methods for Long-Range Federated Edge Learning with Chirp-Based Over-the-Air Computation
Reference #: 01574 The University of South Carolina is offering licensing opportunities for Methods for Long-Range Federated Edge Learning with Chirp-Based Over-the-Air Computation Background: Federated edge learning (FEEL) deploys federated learning (FL) over a wireless network, in which many edge devices (EDs) participate in training using locally...
Published: 1/3/2023   |   Inventor(s): Safi Shams Muhtasimul Hoque, Alphan Sahin
Keywords(s): chirp, DFT-s-OFDM, Distributed learning, federated edge learning, LoRa, orthogonal frequency division multiplexing, over-the-air computation, peak-to-mean envelope power ratio, pulse-position modulation
Category(s): Engineering and Physical Sciences, Software and Computing
Methods for Multi-cell Over-the-Air Computation for Distributed Learning
Reference #: 01573 The University of South Carolina is offering licensing opportunities for Methods for Multi-cell Over-the-Air Computation for Distributed Learning Background: Federated edge learning (FEEL) is a distributed learning framework that leverages the computational powers of edge devices (EDs) and uses the local data at the EDs without...
Published: 5/9/2023   |   Inventor(s): Mohammad Hassan Adeli, Alphan Sahin
Keywords(s): Distributed learning, federated edge learning, frequency-shift keying, orthogonal frequency division multiplexing, over-the-air computation, peak-to-mean envelope power ratio
Category(s): Engineering and Physical Sciences, Software and Computing
Methods for Reliable Over-the-Air Computation with Pulses for Distributed Learning
Reference #: 01541 The University of South Carolina is offering licensing opportunities for Methods for Reliable Over-the-Air Computation with Pulses for Distributed Learning Background: Federated edge learning (FEEL) is an implementation of federated learning (FL) over a wireless network to train a model by using the local data at the edge devices...
Published: 9/3/2022   |   Inventor(s): Alphan Sahin, Everette Bryson, Safi Shams Muhtasimul Hoque
Keywords(s): DFT-s-OFDM, Distributed learning, federated edge learning, orthogonal frequency division multiplexing, over-the-air computation, peak-to-mean envelope power ratio, pulse-position modulation, SC-FDE
Category(s): Software and Computing, Engineering and Physical Sciences
Methods for Reliable Over-the-Air Computation and Federated Edge Learning
Reference #: 01538 The University of South Carolina is offering licensing opportunities for Methods for Reliable Over-the-Air Computation and Federated Edge Learning Background: Federated edge learning (FEEL) is a distributed learning framework that leverages the computational powers of edge devices (EDs) and uses the local data at the EDs without...
Published: 9/3/2022   |   Inventor(s): Alphan Sahin, Everette Bryson, Safi Shams Muhtasimul Hoque
Keywords(s): Distributed learning, federated edge learning, frequency-shift keying, orthogonal frequency division multiplexing, over-the-air computation, peak-to-mean envelope power ratio
Category(s): Engineering and Physical Sciences, Software and Computing
Methods for Encoding and Decoding based on Partitioned Complementary Sequences
Reference #: 01512 The University of South Carolina is offering licensing opportunities for Methods for Encoding and Decoding based on Partitioned Complementary Sequences Background: Orthogonal frequency division multiplexing (OFDM) waveform utilized in 4G, 5G, and Wi-Fi has peaky signal characteristics. Hence, the waveform is often distorted under...
Published: 9/3/2022   |   Inventor(s): Alphan Sahin
Keywords(s): low PAPR, Partitioned complementary sequences, Reed-Muller code
Category(s): Engineering and Physical Sciences
Methods for Wideband Index Modulation Based on Chirp Signals
Reference #: 01476 The University of South Carolina is offering licensing opportunities for Methods for Wideband Index Modulation based on Chirp Signals Background: Chirp can provide robustness against distortions due to the non-linear components in a radio-frequency chain, for example a non-linear power amplifier. They also provide good correlation...
Published: 9/3/2022   |   Inventor(s): Alphan Sahin, Safi Shams Muhtasimul Hoque
Keywords(s): chirp signals, Complementary sequences, DFT-spread OFDM, SC-FDMA
Category(s): Engineering and Physical Sciences, Software and Computing
Methods for Non-linear Distortion Immune End-to-End Learning with Autoencoder-OFDM
Reference #: 01441 The University of South Carolina is offering licensing opportunities for Methods for Non-linear Distortion Immune End-to-End Learning with Autoencoder-OFDM1 Background: AI-based communication systems utilize machine learning modules (e.g., deep learning) to replace the functionality of the highly engineered blocks (e.g., coding,...
Published: 9/4/2022   |   Inventor(s): David Matolak, Alphan Sahin
Keywords(s): Autoencoder, Complementary sequences, machine learning, OFDM, PAPR
Category(s): Engineering and Physical Sciences, Software and Computing
Methods for Reliable Chirp Transmissions
Reference #: 01439 The University of South Carolina is offering licensing opportunities for Methods for Reliable Chirp Transmissions and Multiplexing Background: To increase the reliability and security of communication systems the communication link must be established over multiple bands, e.g., L-Band (1-2 GHz) and C-Band (4-8 GHz) for aeronautical...
Published: 9/4/2022   |   Inventor(s): David Matolak, Alphan Sahin, Nozhan Hosseini
Keywords(s): coded orthogonal chirp division multiplexing, Complementary sequence-based chirp spread spectrum, shift encoders, trajectory encoding with multi-DFT clusters
Category(s): Engineering and Physical Sciences
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