@conference {1075, title = {Radio Maps for Beam Alignment in mmWave Communications with Location Uncertainty}, booktitle = {IEEE 99th Vehicular Technology Conference: VTC2024-Spring. Workshop "Radio Map for 6G Communications"}, year = {2024}, month = {accepted}, address = {Singapore}, abstract = {
Abstract{\textemdash}Next generation communication systems require
accurate beam alignment to counteract the impairments that
characterize propagation in high-frequency bands. The overhead
of the pilot sequences required to select the best beam pair is
prohibitive when codebooks contain a large number of beams, as
is the case in practice. To remedy this issue, some schemes exploit
information about the user location to predict the best beam pair.
However, these schemes (i) involve no measurements whatsoever,
which generally results in a highly suboptimal predicted beam,
and (ii) are not robust to localization errors. To address these
limitations, this paper builds upon the notion of radio map to
develop two algorithms that attain a balance between the quality
of the obtained beam pair and measurement overhead. The
proposed algorithms predict the received power corresponding to
each pair and measure just the Q pairs with highest prediction.
While the first algorithm targets simplicity, the second one
relies on a Bayesian approach to endow the prediction process
with robustness to localization error. The performance of both
algorithms is shown to widely outperform existing methods using
ray-tracing data.
}, keywords = {mayte, mmWave, radio maps}, author = {Tien, Ngoc-Ha and Daniel Romero and R. L{\'o}pez-Valcarce} }