TY - GEN
T1 - Efficient Beam Alignment in Millimeter Wave Systems Using Contextual Bandits
AU - Hashemi, Morteza
AU - Sabharwal, Ashutosh
AU - Emre Koksal, C.
AU - Shroff, Ness B.
N1 - Funding Information:
This work was supported in part by the following NSF grants: CNS-1518916, CNS-1314822, CNS-1618566, CNS-1514260, CNS-1518829, and ONR grants: N00014-15-1-2166 and N00014-17-1-2417. The authors would like to thank Hongliang Si and Nathan Weirich for performing the experiments.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/8
Y1 - 2018/10/8
N2 - In this paper, we investigate the problem of beam alignment in millimeter wave (mmWave) systems, and design an optimal algorithm to reduce the overhead. Specifically, due to directional communications, the transmitter and receiver beams need to be aligned, which incurs high delay overhead since without a priori knowledge of the transmitter/receiver location, the search space spans the entire angular domain. This is further exacerbated under dynamic conditions (e.g., moving vehicles) where the access to the base station (access point) is highly dynamic with intermittent on-off periods, requiring more frequent beam alignment and signal training. To mitigate this issue, we consider an online stochastic optimization formulation where the goal is to maximize the directivity gain (i.e., received energy) of the beam alignment policy within a time period. We exploit the inherent correlation and unimodality properties of the model, and demonstrate that contextual information improves the performance. To this end, we propose an equivalent structured Multi-Armed Bandit model to optimally exploit the exploration-exploitation tradeoff. In contrast to the classical MAB models, the contextual information makes the lower bound on regret (i.e., performance loss compared with an oracle policy) independent of the number of beams. This is a crucial property since the number of all combinations of beam patterns can be large in transceiver antenna arrays, especially in massive MIMO systems. We further provide an asymptotically optimal beam alignment algorithm, and investigate its performance via simulations.
AB - In this paper, we investigate the problem of beam alignment in millimeter wave (mmWave) systems, and design an optimal algorithm to reduce the overhead. Specifically, due to directional communications, the transmitter and receiver beams need to be aligned, which incurs high delay overhead since without a priori knowledge of the transmitter/receiver location, the search space spans the entire angular domain. This is further exacerbated under dynamic conditions (e.g., moving vehicles) where the access to the base station (access point) is highly dynamic with intermittent on-off periods, requiring more frequent beam alignment and signal training. To mitigate this issue, we consider an online stochastic optimization formulation where the goal is to maximize the directivity gain (i.e., received energy) of the beam alignment policy within a time period. We exploit the inherent correlation and unimodality properties of the model, and demonstrate that contextual information improves the performance. To this end, we propose an equivalent structured Multi-Armed Bandit model to optimally exploit the exploration-exploitation tradeoff. In contrast to the classical MAB models, the contextual information makes the lower bound on regret (i.e., performance loss compared with an oracle policy) independent of the number of beams. This is a crucial property since the number of all combinations of beam patterns can be large in transceiver antenna arrays, especially in massive MIMO systems. We further provide an asymptotically optimal beam alignment algorithm, and investigate its performance via simulations.
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U2 - 10.1109/INFOCOM.2018.8486279
DO - 10.1109/INFOCOM.2018.8486279
M3 - Conference contribution
AN - SCOPUS:85056154471
T3 - Proceedings - IEEE INFOCOM
SP - 2393
EP - 2401
BT - INFOCOM 2018 - IEEE Conference on Computer Communications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Conference on Computer Communications, INFOCOM 2018
Y2 - 15 April 2018 through 19 April 2018
ER -