US 12,438,675 B2
Synchronization for OFDM-based over-the-air aggregation
Huayan Guo, New Territories (HK); Yifan Zhu, New Territories (HK); Haoyu Ma, New Territories (HK); Vincent Kin Nang Lau, Tseung Kwan O (HK); and Kaibin Huang, New Territories (HK)
Assigned to THE HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY, Kowloon (HK); and THE UNIVERSITY OF HONG KONG, Hong Kong (HK)
Filed by The Hong Kong University of Science and Technology, Kowloon (HK); and The University of Hong Kong, Hong Kong (HK)
Filed on Apr. 19, 2023, as Appl. No. 18/303,104.
Claims priority of provisional application 63/333,113, filed on Apr. 20, 2022.
Prior Publication US 2023/0344602 A1, Oct. 26, 2023
Int. Cl. H04L 5/00 (2006.01); H04L 27/26 (2006.01)
CPC H04L 5/0053 (2013.01) [H04L 5/0078 (2013.01); H04L 27/2605 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system, comprising:
a processor; and
a memory coupled to the processor, comprising instructions that, in response to execution by the processor, cause the system to perform operations, comprising:
receiving respective data from respective sensors, wherein the respective data represents respective gradient values for a neural network produced by the respective first sensors according to a federated learning process;
transforming the respective data into respective analog waveforms;
applying orthogonal frequency-division multiplexing to the respective analog waveforms to produce respective aligned analog waveforms;
creating a superposition analog waveform that comprises a superposition of the respective aligned analog waveforms; and
transmitting the superposition analog waveform to an access point, wherein the access point is configured to update the neural network with the superposition analog waveform according to the federated learning process.