US 11,996,968 B2
Neural augmentation for device nonlinearity mitigation in x-node machine learning
June Namgoong, San Diego, CA (US); Taesang Yoo, San Diego, CA (US); Naga Bhushan, San Diego, CA (US); Krishna Kiran Mukkavilli, San Diego, CA (US); Tingfang Ji, San Diego, CA (US); Hwan Joon Kwon, San Diego, CA (US); Pavan Kumar Vitthaladevuni, San Diego, CA (US); and Jay Kumar Sundararajan, San Diego, CA (US)
Assigned to QUALCOMM Incorporated, San Diego, CA (US)
Filed by QUALCOMM Incorporated, San Diego, CA (US)
Filed on Aug. 5, 2021, as Appl. No. 17/394,928.
Claims priority of provisional application 63/070,119, filed on Aug. 25, 2020.
Prior Publication US 2022/0070041 A1, Mar. 3, 2022
Int. Cl. H04L 27/26 (2006.01); G06N 3/045 (2023.01)
CPC H04L 27/2614 (2013.01) [G06N 3/045 (2023.01)] 30 Claims
OG exemplary drawing
 
1. A method for reducing peak to average power ratio of wireless transmission waveforms, performed in transmitter circuitry of a wireless communication device, the method comprising:
receiving frequency domain data tones;
transforming the frequency domain data tones to time domain data signals;
generating, using a set of peak reduction tone (PRT) neural networks, time domain PRTs using the time domain data signals, wherein the set of PRT neural networks have been trained in conjunction with an augmentation neural network and a receiver neural network;
generating an output of the augmentation neural network based on an input of final combined time domain signals including the time domain PRTs combined with previous combined time domain signals; and
generating time domain wireless transmission waveforms that include the output of the augmentation neural network combined with the final combined time domain signals.