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Online Channel Estimation for Hybrid Beamforming Architectures

: Fink, J.; Cavalcante, R.L.G.; Stanczak, S.


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society; IEEE Signal Processing Society:
IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2020. Proceedings : May 4-8, 2020, Barcelona, Spain
Piscataway, NJ: IEEE, 2020
ISBN: 978-1-5090-6631-5
ISBN: 978-1-5090-6632-2
International Conference on Acoustics, Speech and Signal Processing (ICASSP) <45, 2020, Barcelona>
Fraunhofer HHI ()

Hybrid analog-/digital beamforming architectures are a promising means of reducing power consumption and hardware costs in large multi-antenna transceivers. However, channel estimation becomes more complicated compared with conventional (fully-digital) architectures because multiple measurements (pilots in subsequent time slots) are required to reconstruct the channel matrix. In this paper, we use variants of the adaptive projected subgradient method to devise online estimation algorithms that exploit temporal correlations of channel samples. Simulations show that these methods are competitive with conventional batch methods in terms of estimation error at a significantly reduced computational cost. We further improve the performance of the proposed methods by exploiting their potential to adapt the analog combiner at runtime.