TY - GEN
T1 - Position-Aided Beam Prediction in the Real World
T2 - 2023 IEEE International Conference on Communications, ICC 2023
AU - Morais, João
AU - Bchboodi, Arash
AU - Pezeshki, Hamed
AU - Alkhateeb, Ahmed
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Millimeter-wave (mmWave) communication systems rely on narrow beams to achieve sufficient receive signal power. Adjusting these beams is typically associated with large training overhead, which becomes particularly critical for highly-mobile applications. Beam selection can benefit from the knowledge of user positions to reduce the overhead in mm Wave beam training. Prior work, however, studied this problem using only synthetic data that does not accurately represent real-world measurements. In this paper, we revisit the position-aided beam prediction problem in light of real-world measurements with commercial-off-the-shelf GPS to derive insights into how much beam training overhead can be saved in practice. We also compare algorithms that perform well in synthetic data but fail to generalize with real data, and attempt to answer what factors cause inference degradation. Further, we propose a machine learning evaluation metric that better captures the end communication system objective. This work aims at closing the gap between reality and simulations in position-aided beam alignment.
AB - Millimeter-wave (mmWave) communication systems rely on narrow beams to achieve sufficient receive signal power. Adjusting these beams is typically associated with large training overhead, which becomes particularly critical for highly-mobile applications. Beam selection can benefit from the knowledge of user positions to reduce the overhead in mm Wave beam training. Prior work, however, studied this problem using only synthetic data that does not accurately represent real-world measurements. In this paper, we revisit the position-aided beam prediction problem in light of real-world measurements with commercial-off-the-shelf GPS to derive insights into how much beam training overhead can be saved in practice. We also compare algorithms that perform well in synthetic data but fail to generalize with real data, and attempt to answer what factors cause inference degradation. Further, we propose a machine learning evaluation metric that better captures the end communication system objective. This work aims at closing the gap between reality and simulations in position-aided beam alignment.
UR - http://www.scopus.com/inward/record.url?scp=85178278975&partnerID=8YFLogxK
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U2 - 10.1109/ICC45041.2023.10278998
DO - 10.1109/ICC45041.2023.10278998
M3 - Conference contribution
AN - SCOPUS:85178278975
T3 - IEEE International Conference on Communications
SP - 1824
EP - 1829
BT - ICC 2023 - IEEE International Conference on Communications
A2 - Zorzi, Michele
A2 - Tao, Meixia
A2 - Saad, Walid
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 28 May 2023 through 1 June 2023
ER -