US 12,245,251 B2
Distortion probing reference signals
June Namgoong, San Diego, CA (US); Taesang Yoo, San Diego, CA (US); Naga Bhushan, San Diego, CA (US); Tingfang Ji, San Diego, CA (US); Krishna Kiran Mukkavili, San Diego, CA (US); Jay Kumar Sundararajan, San Diego, CA (US); and Pavan Kumar Vitthaladevuni, San Diego, CA (US)
Assigned to QUALCOMM Incorporated, San Diego, CA (US)
Filed by QUALCOMM Incorporated, San Diego, CA (US)
Filed on May 25, 2023, as Appl. No. 18/324,005.
Application 18/324,005 is a continuation of application No. 17/136,840, filed on Dec. 29, 2020, granted, now 11,737,106.
Claims priority of provisional application 62/980,869, filed on Feb. 24, 2020.
Prior Publication US 2023/0300850 A1, Sep. 21, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. H04W 72/23 (2023.01); H03F 1/32 (2006.01); H04L 5/00 (2006.01); H04W 72/044 (2023.01)
CPC H04W 72/23 (2023.01) [H03F 1/3247 (2013.01); H04L 5/0048 (2013.01); H04W 72/0473 (2013.01)] 16 Claims
OG exemplary drawing
 
1. A method for wireless communications at a first device, comprising:
transmitting an indication configuring a second device for transmission of a first reference signal with a first peak to average power ratio and for transmission of a second reference signal with a second peak to average power ratio that is greater than the first peak to average power ratio;
receiving, from the second device, the first reference signal and the second reference signal based at least in part on transmitting the indication;
estimating, using a neural network model and one or more neural network weights corresponding to one or more transmission parameters associated with the second device and based at least in part on a channel estimate from the first reference signal and the second reference signal, a nonlinear response associated with the second device;
estimating a transmission encoding metric and a reception decoding metric based at least in part on the nonlinear response associated with the second device;
transmitting signaling associated with the neural network model and the one or more neural network weights based at least in part on estimating the transmission encoding metric and the reception decoding metric; and
communicating with the second device based at least in part on the reception decoding metric.