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Memory-assisted Statistically-ranked RF Beam Training Algorithms for Sparse MIMO

 
: Tiwari, K.K.; Grass, E.; Thompson, J.S.

:

Institute of Electrical and Electronics Engineers -IEEE-:
IEEE 91st Vehicular Technology Conference, VTC2020-Spring. Proceedings : 25-28 May 2020, Antwerp, Belgium, virtual event
Piscataway, NJ: IEEE, 2020
ISBN: 978-1-7281-5207-3
ISBN: 978-1-7281-4053-7
ISBN: 978-1-7281-5206-6
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 multiple-input multiple-output (MIMO) channels using unitary RF beamforming codebooks at transmitter (Tx) and receiver (Rx). The algorithms leverage the statistical knowledge from memory-based past beam training data for expedited massive MIMO channel-learning with statistically-minimal 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, statistically-ranked beam search performs excellent in reducing the average number of beam tests per Tx-Rx beam pair identification for a communication link. For high beam entropy cases, a hybrid RF beam training algorithm involving both memory-assisted statistically-ranked (MarS) beam search and multi-level (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.

: http://publica.fraunhofer.de/dokumente/N-602772.html