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Beam training in dynamic millimeter-wave (mm-wave) networks with mobiledevices is highly challenging as devices must scan a large angular domain tomaintain alignment of their directional antennas under mobility. Devicerotation is particularly challenging, as a handheld device may rotatesignificantly over a very short period of time, causing it to lose theconnection to the Access Point (AP) unless the rotation is accompanied byimmediate beam realignment. We study how to maintain the link to a mm-wave APunder rotation and without any input from inertial sensors, exploiting the factthat mm-wave devices will typically be multi-band. We present a model that mapsTime-of-Flight measurements to rotation and propose a method to infer therotation speed of the mobile terminal using only measurements from sub-6 GHzWiFi. We also use the same sub-6 GHz WiFi system to reduce the angle errorestimate for link establishment, exploiting the spatial geometry of thedeployed APs and a statistical model that maps the user position's spatialdistribution to an angle error distribution. We leverage these findings tointroduce SLASH, a Statistical Location and rotation-Aware beam SearcHalgorithm that adaptively narrows the sector search space and accelerates bothlink establishment and maintenance between mm-wave devices. We evaluate SLASHwith experiments conducted indoors with a sub-6 GHz WiFi Time-of-Flightpositioning system and a 60-GHz testbed. SLASH can increase the data rate bymore than 41% for link establishment and 67% for link maintenance with respectto prior work.

Statistical Location and Rotation-Aware Beam Search for Millimeter-Wave Networks