Adaptive Beam Tracking With Supervised Learning

•  Utilizing millimeter-wave (mmWave) frequencies forwireless communication in mobile systems is challenging sincecontinuous tracking of the beam direction is needed. For the purpose, beam sweeping is performed periodically. Such approachcan be sufficient in the initial deployment of the network whenthe number of users is small. However, a more efficient solutionis needed when lots of users are connected to the network due tohigher overhead consumption. We explore a supervised learningapproach to adaptively perform beam sweeping, which has lowimplementation complexity and can improve cell capacity byreducing beam sweeping overhead. By formulating the beamtracking problem as a binary classification problem, we appliedsupervised learning methods to solve the formulated problem.The methods were tested on two scenarios: ray-tracing outdoorscenario and over-the-air (OTA) testing dataset from Ericsson.Both experimental results show that the proposed methodssignificantly increase cell throughput comparing with existingexhaustive sweeping and periodical sweeping strategies. 

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Telekommunikation (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Telecommunications (hsv//eng)

Nyckelord

millimeter-wave; beam tracking; supervised learning

Publikations- och innehållstyp

vet (ämneskategori)

ovr (ämneskategori)