Online Learning for Position-Aided Millimeter Wave Beam Training

For unmanned aerial vehicle (UAV) aided millimeter wave (mmWave) networks, we propose a unified threedimensional (3D) spatial framework in this paper to model a general case that uncovered users send messages to base stations via UAVs. More specifically, the locations of transceivers in downlink and uplink are modeled through the Poisson point processes and Poisson cluster processes (PCPs), respectively. For PCPs, Matern cluster and Thomas cluster processes, are analyzed. Furthermore, both 3D blockage processes and 3D antenna patterns are introduced for appraising the effect of altitudes. Based on this unified framework, several closed-form expressions for the coverage probability in the uplink and downlink, are derived. By investigating the entire communication process, which includes the two aforementioned phases and the cooperative transmission between them, tractable expressions of system coverage probabilities are derived. Next, three practical applications in UAV networks are provided as case studies of the proposed framework. The results reveal that the impact of thermal noise and non-line-of-sight mmWave transmissions is negligible. In the considered networks, mmWave outperforms sub-6 GHz in terms of the data rate, due to the sharp direction beamforming and large transmit bandwidth. Additionally, there exists an optimal altitude of UAVs, which maximizes the system coverage probability.

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“…Remark 4. When the Nakagami parameter m = 1, namely Rayleigh fading channel, then the equality holds in (39).…”

Section: ) Coverage Probability For the Uplink Phasementioning

confidence: 99%

A Unified Spatial Framework for UAV-Aided MmWave Networks

Liu

Bodanese

et al. 2019

IEEE Trans. Commun.

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“…15, it is confirmed that our proposed solution tracks near the best beam pair quite well even in a practical fast time-varying channel with a far-less sounding overhead. As a demonstration, rather than relying on simulations under a geometric statistical channel model, we adopt a raytracing tool to show that our proposed solution performs well under more realistic time-varying channel parameters (angles, powers, and relative delays) for a moving MS scenario in a specific site, such as trends in [11], [12], [72], [73]. In Fig.…”

Section: B Lower Sounding Overhead Beam Trackingmentioning

confidence: 99%

Hybrid Precoding Based on Monopulse Ratio for Millimeter Wave Systems With Limited Feedback

2020

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Multiuser hybrid precoding in 5G NR mmWave communication system faces significant challenges such as establishing accurate directional radio links and maintaining links for mobile stations (MSs) moving in outdoor environments. A conventional solution relies on finite codebook-based beam sweeping for initial first-stage beam acquisition and subsequent second-stage beam tracking by sweeping adjacent beam pairs. However, such a conventional solution has inevitable residual AoA/AoD errors even after the best beam pair is established in the first stage and incurs nonnegligible overheads to sound adjacent beam pairs for maintaining the best beam pair in the second stage. To overcome these problems, a novel codebook-based two-stage solution that combines a novel beam tracking protocol with a low sounding overhead, a unique receiver structure employing a beam scheduler and a beam tester, and a fine accuracy residual AoA/AoD error estimation algorithm based on the monopulse ratio concept is proposed. The solution is unique because of the receiver structure for the residual AoA/AoD error estimation that exploits the cyclic prefix in OFDM systems, inspired by the monopulse ratio in radar systems. Moreover, it can be applied to MSs with a single RF chain. This solution, using the proposed receiver structure and algorithm, can establish a more accurate directional beam pair right after the initial beam sweeping in the first stage. For beam tracking in the second stage, it estimates the residual AoA/AoD errors of the current best beam pair rather than sweeping adjacent beam pairs, thereby reducing beam tracking overheads. Numerical evaluation and computer simulations show that the proposed solution offers more accurate beam acquisition (i.e., average array gain improvement of several dB) and costs considerably reduced beam sounding overheads compared to the conventional solution. Lastly, a ray-tracing tool is used to demonstrate that our solution is effective in practical channel parameters for outdoor environments. INDEX TERMS Monopulse ratio, mmWave, multiuser hybrid precoding, limited feedback, AoA/AoD, beam acquisition, beam tracking, cyclic prefix, OFDM, and single RF chain.

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“…The authors of [8] use a UCB-based policy for beam-alignment in high speed train applications. The work in [9] introduces a position-aided mmWave beam selection technique. The more recent work [10] also uses the contextual stochastic bandits with a prior knowledge on the channel fluctuations to reduce the beam-alignment delay.…”

Section: Introductionmentioning

confidence: 99%

Exploiting Channel Sparsity for Beam Alignment in mmWave Systems via Exponential Learning

Chafaa

Belmega

Debbah

2020

2020 IEEE International Conference on Communications Workshops (ICC Workshops)

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The large available spectrum in the millimeter wave (mmWave) band represents an attractive alternative for the congested sub-6 GHz spectrum. To overcome the difficult propagation conditions at high frequencies, directional communications via multiple antenna arrays and high-gain beams can be employed. Nevertheless, these beams need to be well aligned to reliably transmit data, which is a challenging task given the user mobility and the unpredictable changes of the wireless environment. In this paper, we propose a new distributed beam-alignment strategy relying on a single bit of feedback, which equals one if the signal-to-interference-plus-noise (SINR) reaches a predefined threshold. The novelty consists in a modified reward function, inspired from the sparse nature of the mmWave channel, coupled with the well-known exponential weights algorithm (EXP3). First, we show that our resulting adaptive policy comes with optimal theoretical guarantees in terms of sub-linear regret. Second, our numerical results demonstrate significant performance gains of our beam-alignment policy compared with the original EXP3 algorithm and other existing policies in a mmWave setting with user mobility.

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Online Learning for Position-Aided Millimeter Wave Beam Training

Cited by 118 publications

References 37 publications

A Unified Spatial Framework for UAV-Aided MmWave Networks

A Unified Spatial Framework for UAV-Aided MmWave Networks

Hybrid Precoding Based on Monopulse Ratio for Millimeter Wave Systems With Limited Feedback

Exploiting Channel Sparsity for Beam Alignment in mmWave Systems via Exponential Learning

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