Agile Millimeter Wave Networks with Provable Guarantees

Hassanieh, Haitham; Abari, Omid; Rodreguez, Michael; Abdelghany, Mohammed; Katabi, Dina; Indyk, Piotr

doi:10.48550/arxiv.1706.06935

Cited by 1 publication

(1 citation statement)

References 37 publications

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“…The authors in [11] use spatial information extracted at sub-6 GHz to help estimate the best beam pairs at the transmitter and receiver at mmWave frequencies. In [12], a beam alignment scheme based on scanning several directions by one-shot is proposed. In contrast to the previous works, we focus on exploiting contextual information in standalone mmWave systems in order to reduce the search space, and thus the overhead of beam alignment operation.…”

Section: A Beam Alignmentmentioning

confidence: 99%

Efficient Beam Alignment in Millimeter Wave Systems Using Contextual Bandits

Hashemi

Sabharwal

Köksal

et al. 2018

IEEE INFOCOM 2018 - IEEE Conference on Computer Communications

128

111

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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 explorationexploitation 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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Section: A Beam Alignmentmentioning

confidence: 99%