US 11,894,880 B2
Automatic fine-grained radio map construction and adaption
Han Zou, Berkeley, CA (US); Costas J. Spanos, Lafayette, CA (US); and Yuxun Zhou, Chicago, IL (US)
Assigned to THE REGENTS OF THE UNIVERSITY OF CALIFORNIA, Oakland, CA (US)
Appl. No. 17/312,803
Filed by The Regents of the University of California, Oakland, CA (US)
PCT Filed Dec. 16, 2019, PCT No. PCT/US2019/066627
§ 371(c)(1), (2) Date Jun. 10, 2021,
PCT Pub. No. WO2020/124091, PCT Pub. Date Jun. 18, 2020.
Claims priority of provisional application 62/779,956, filed on Dec. 14, 2018.
Prior Publication US 2022/0077944 A1, Mar. 10, 2022
Int. Cl. H04B 17/318 (2015.01); H04B 17/21 (2015.01); G06N 3/08 (2023.01); H04W 16/22 (2009.01)
CPC H04B 17/318 (2015.01) [G06N 3/08 (2013.01); H04B 17/21 (2015.01); H04W 16/225 (2013.01)] 20 Claims
OG exemplary drawing
 
13. A method, comprising:
a sensor of an automatic wireless fine-grained ratio map construction and adaptation system collecting real wireless received signal strength (RSS) measurements in a free space;
a processor of the automatic wireless fine-grained ratio map construction and adaptation system constructing a Gaussian process regression (GPR) model based at least in part on the real wireless RSS measurements collected by the sensor in the free space to provide coarse RSS estimation in a constrained space; and
a generator of a generative adversarial network (GAN) of the automatic wireless fine-grained ratio map construction and adaptation system providing fine-grained RSS estimation in the constrained space by using an output of the GPR as an input for the generator of the GAN.