Publica
Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. Memoryassisted Statisticallyranked RF Beam Training Algorithms for Sparse MIMO
 Institute of Electrical and Electronics Engineers IEEE: IEEE 91st Vehicular Technology Conference, VTC2020Spring. Proceedings : 2528 May 2020, Antwerp, Belgium, virtual event Piscataway, NJ: IEEE, 2020 ISBN: 9781728152073 ISBN: 9781728140537 ISBN: 9781728152066 7 S. 
 Vehicular Technology Conference (VTC Spring) <91, 2020, Online> 

 Englisch 
 Konferenzbeitrag 
 Fraunhofer IZM () 
Abstract
This paper presents novel radio frequency (RF) beam training algorithms for sparse multipleinput multipleoutput (MIMO) channels using unitary RF beamforming codebooks at transmitter (Tx) and receiver (Rx). The algorithms leverage the statistical knowledge from memorybased past beam training data for expedited massive MIMO channellearning with statisticallyminimal training overheads. Beams are tested in the order of their ranks based on their probabilities for providing a communication link. For low beam entropy scenarios, statisticallyranked beam search performs excellent in reducing the average number of beam tests per TxRx beam pair identification for a communication link. For high beam entropy cases, a hybrid RF beam training algorithm involving both memoryassisted statisticallyranked (MarS) beam search and multilevel (ML) beam search is also proposed. Savings in training overheads increase with decrease in relative beam entropy and increase in MIMO channel dimensions. A novel mathematical operation of multiplying the elements of a Kronecker product with their respective position numbers in the Kronecker product vector is also brought out which may find further applications in the future.