US 12,224,885 B1
Machine learning based channel precoder selection for downlink
Amit Bar-Or Tillinger, Tel-Aviv (IL); Shay Landis, Hod Hasharon (IL); Idan Michael Horn, Hod Hasharon (IL); Jacob Pick, Mevaseret Zion (IL); and Yehonatan Dallal, Kfar Saba (IL)
Assigned to QUALCOMM Incorporated, San Diego, CA (US)
Filed by QUALCOMM Incorporated, San Diego, CA (US)
Filed on Aug. 10, 2023, as Appl. No. 18/447,942.
Int. Cl. H04L 5/12 (2006.01); H04L 25/02 (2006.01); H04W 28/18 (2009.01); H04W 72/1273 (2023.01)
CPC H04L 25/0254 (2013.01) [H04W 28/18 (2013.01); H04W 72/1273 (2013.01)] 30 Claims
OG exemplary drawing
 
1. A user equipment (UE), comprising:
one or more memories storing processor-executable code; and
one or more processors coupled with the one or more memories and individually or collectively operable to execute the code to cause the UE to:
transmit a capability message indicating one or more neural network characteristics supported by the UE;
receive, based at least in part on transmitting the capability message, a neural network characteristic message indicating one or more neural network coefficients of a neural network model corresponding to precoder calculation based on channel estimation;
receive downlink signaling and one or more demodulation reference signals, wherein a narrowband precoder corresponding to the downlink signaling is different than a precoding status corresponding to the one or more demodulation reference signals; and
decode the downlink signaling according to an output from the neural network model indicating the narrowband precoder of the downlink signaling, wherein the output from the neural network model is based at least in part on the one or more neural network coefficients and an input to the neural network model comprising one or more channel estimation values.