12,775 research outputs found
Robust Location-Aided Beam Alignment in Millimeter Wave Massive MIMO
Location-aided beam alignment has been proposed recently as a potential
approach for fast link establishment in millimeter wave (mmWave) massive MIMO
(mMIMO) communications. However, due to mobility and other imperfections in the
estimation process, the spatial information obtained at the base station (BS)
and the user (UE) is likely to be noisy, degrading beam alignment performance.
In this paper, we introduce a robust beam alignment framework in order to
exhibit resilience with respect to this problem. We first recast beam alignment
as a decentralized coordination problem where BS and UE seek coordination on
the basis of correlated yet individual position information. We formulate the
optimum beam alignment solution as the solution of a Bayesian team decision
problem. We then propose a suite of algorithms to approach optimality with
reduced complexity. The effectiveness of the robust beam alignment procedure,
compared with classical designs, is then verified on simulation settings with
varying location information accuracies.Comment: 24 pages, 7 figures. The short version of this paper has been
accepted to IEEE Globecom 201
Millimeter Wave Beam Alignment: Large Deviations Analysis and Design Insights
In millimeter wave cellular communication, fast and reliable beam alignment
via beam training is crucial to harvest sufficient beamforming gain for the
subsequent data transmission. In this paper, we establish fundamental limits in
beam-alignment performance under both the exhaustive search and the
hierarchical search that adopts multi-resolution beamforming codebooks,
accounting for time-domain training overhead. Specifically, we derive lower and
upper bounds on the probability of misalignment for an arbitrary level in the
hierarchical search, based on a single-path channel model. Using the method of
large deviations, we characterize the decay rate functions of both bounds and
show that the bounds coincide as the training sequence length goes large. We go
on to characterize the asymptotic misalignment probability of both the
hierarchical and exhaustive search, and show that the latter asymptotically
outperforms the former, subject to the same training overhead and codebook
resolution. We show via numerical results that this relative performance
behavior holds in the non-asymptotic regime. Moreover, the exhaustive search is
shown to achieve significantly higher worst-case spectrum efficiency than the
hierarchical search, when the pre-beamforming signal-to-noise ratio (SNR) is
relatively low. This study hence implies that the exhaustive search is more
effective for users situated further from base stations, as they tend to have
low SNR.Comment: Author final manuscript, to appear in IEEE Journal on Selected Areas
in Communications (JSAC), Special Issue on Millimeter Wave Communications for
Future Mobile Networks, 2017 (corresponding author: Min Li
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