Dedicated Beam-based Channel Training Technique for Millimeter Wave Communications with high Mobility

Bae, Jisu; Lim, Sun Hong; Yoo, Jin Hyeok; Choi, Jun Won; Shim, Byonghyo

doi:10.23919/eusipco.2018.8553578

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Both AoD and AoA can be estimated using beam training procedure, and if one identifies the dominant angular bins, one can make channel estimation without incurring in excessive computational complexity. In the work of Bae et al, a beam tracking method based on the AoD was proposed. This beam tracking technique exploits the continuous nature of time‐varying AoD for beam selection.…”

Section: Introductionmentioning

confidence: 99%

“…This beam tracking technique exploits the continuous nature of time‐varying AoD for beam selection. Bae et al showed that the transmission of only two training beams is enough to track the time‐varying AoD with good accuracy. In the work of Zhang et al, a full scan of all possible beam combinations is used to create a measurement matrix to which the extended Kalman filter (EKF) is applied.…”

Section: Introductionmentioning

confidence: 99%

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Supervised learning and graph signal processing strategies for beam tracking in highly directional mobile communications

Ortega

Guerreiro

Hui

et al. 2019

Trans Emerging Tel Tech

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The use of efficient beam tracking mechanisms becomes necessary in highly directional communication of the fifth‐generation systems. In this context, this paper analyzes the tracking problem and proposes a framework that exploits the samples in the user equipment (UE) dataset (historical UE dataset) to efficiently estimate and predict the channel state at the base station. The framework is composed of two steps. First, a supervised learning algorithm, namely, K‐nearest neighbors (K‐NN), is evaluated as a mean of (i) finding the most similar historical samples to some beam measurements reported by the UE and (ii) predicting the corresponding channel state. As a second step, a sampling and reconstruction strategy based on graph signal processing (GSP) called K‐nearest neighbors with reconstruction (K‐NN‐R) is introduced in order to reduce the beam search space during the beam measurement stage, which allows a more efficient usage of the feedback channel. Simulation results illustrate the performance of the proposal in terms of normalized mean square error in comparison with three traditional/baseline prediction techniques. The K‐NN technique provides a better performance than the baseline approaches for any length of the observed temporal window and with full beam sweep. Meanwhile, the K‐NN‐R framework outperforms the baseline approaches with only half of the beam pairs and throughout a significantly low length of observed temporal window.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Supervised learning and graph signal processing strategies for beam tracking in highly directional mobile communications

Ortega

Guerreiro

Hui

et al. 2019

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

Efficient Beam Training and Sparse Channel Estimation for Millimeter Wave Communications Under Mobility

Lim

Kim

Shim

et al. 2020

IEEE Trans. Commun.

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Beam Allocation for Millimeter-Wave MIMO Tracking Systems

Zhang

Chen

et al. 2020

IEEE Trans. Veh. Technol.

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In this paper, we propose a new beam allocation strategy aiming to maximize the average successful tracking probability (ASTP) of time-varying millimeter-wave MIMO systems. In contrast to most existing works that employ one transmitting-receiving (Tx-Rx) beam pair once only in each training period, we investigate a more general framework, where the Tx-Rx beam pairs are allowed to be used repeatedly to improve the received signal powers in specific directions. In the case of orthogonal Tx-Rx beam pairs, a power-based estimator is employed to track the time-varying AoA and AoD of the channel, and the resulting training beam pair sequence design problem is formulated as an integer nonlinear programming (I-NLP) problem. By dividing the feasible region into a set of subregions, the formulated I-NLP is decomposed into a series of concave sub I-NLPs, which can be solved by recursively invoking a nonlinear branch-and-bound algorithm. To reduce the computational cost, we relax the integer constraints of each sub I-NLP and obtain a low-complexity solution via solving the Karush-Kuhn-Tucker conditions of their relaxed problems. For the case when the Tx-Rx beam pairs are overlapped in the angular space, we estimate the updated AoA and AoD via an orthogonal matching pursuit (OMP) algorithm. Moreover, since no explicit expression for the ASTP exists for the OMP-based estimator, we derive a closed-form lower bound of the ASTP, based on which a favorable beam pair allocation strategy can be obtained. Numerical results demonstrate the superiority of the proposed beam allocation strategy over existing benchmarks. Index TermsMillimeter wave, time-varying channel, beam training, beam tracking, training beam sequence design.

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Dedicated Beam-based Channel Training Technique for Millimeter Wave Communications with high Mobility

Cited by 3 publications

References 14 publications

Supervised learning and graph signal processing strategies for beam tracking in highly directional mobile communications

Supervised learning and graph signal processing strategies for beam tracking in highly directional mobile communications

Efficient Beam Training and Sparse Channel Estimation for Millimeter Wave Communications Under Mobility

Beam Allocation for Millimeter-Wave MIMO Tracking Systems

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