Best Beam Prediction in Non-Standalone mm Wave Systems

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

4 Sitaatiot (Scopus)
148 Lataukset (Pure)


We consider a machine learning approach to perform best beam prediction in Non-Standalone Millimeter Wave (mmWave) Systems utilizing Channel Charting (CC). The approach reduces communication overheads and delays associated
with initial access and beam tracking in 5G New Radio (NR) systems. The network has a mmWave and a sub-6 GHz component. We devise a Base Station (BS) centric approach for best mmWave beam prediction, based on Channel State Information (CSI) measured at the sub-6 GHz BS, with no need to exchange information with UEs. In a training phase, we collect CSI at the sub-6 GHz BS from sample UEs, and construct a dimensional reduction of the sample CSI, called a CC. We annotate the CC with best beam information measured at a mmWave BS for the sample UEs, assuming autonomous beamformer at the UE side. A beam predictor is trained based on this information, connecting any sub-6 GHz CSI with a predicted best mmWave beam. To evaluate the efficiency of the proposed framework, we perform simulations for a street segment with synthetic spatially consistent CSI. With a neural network predictor, we obtain 91% accuracy for predicting best beam and 99% accuracy for predicting one of two best beams. The accuracy of CC based beam prediction is indistinguishable from true location based beam prediction.
Otsikko2021 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)
ISBN (elektroninen)978-1-6654-1526-2
DOI - pysyväislinkit
TilaJulkaistu - 28 heinäk. 2021
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaEuropean Conference on Networks and Communications - Porto, Portugali
Kesto: 8 kesäk. 202111 kesäk. 2021


NimiEuropean conference on networks and communications
ISSN (painettu)2475-6490
ISSN (elektroninen)2575-4912


ConferenceEuropean Conference on Networks and Communications


Sukella tutkimusaiheisiin 'Best Beam Prediction in Non-Standalone mm Wave Systems'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.

Siteeraa tätä