Beam Searching for mmWave Networks with sub-6 GHz WiFi and Inertial Sensors Inputs: an experimental study

Abstract

Beam training in dynamic millimeter-wave (mm-wave) networks with mobile devices is highly challenging as devices must scan a large angular domain to maintain alignment of their directional beams under mobility. In this work, we exploit the trend of multiple chipsets integrated in the same mobile device to study a set of non-mmwave input data that can be leveraged jointly to provide faster beam search and better data rate. We leverage these findings to introduce SLASH, an algorithm that adaptively narrows the sector search space and accelerates link establishment, link maintenance and handover between mm-wave devices. We experimentally evaluate SLASH with commodity hardware, including a 60 GHz testbed, commercial sub-6 GHz WiFi APs and smartphones. SLASH can increase the median data rate by more than 22% for link establishment and 25% for link maintenance with respect to prior work.

Yago Lizarribar Carrillo

PhD Student

Anything related to SDR, Machine-Learning, Wireless Communications or Robotics and I’m all ears.