AoD and AoA tracking with directional sounding beam design for millimeter wave MIMO systems

Duan, Qiyou; Kim, Taejoon; Huang, Huang; Liu, Kunpeng; Wang, Guangjian

doi:10.1109/pimrc.2015.7343676

Cited by 43 publications

(26 citation statements)

References 19 publications

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“…AoA/AoD estimation leverages the sparsity of mmWave channels to reduce the number of measurements required compared to beam sweeping. For example, compressive sensing is used in [16], [17] while an approximate maximum likelihood estimator is derived using the channel structure directly in [18]. Compressive measurements have to overcome the lack of antenna gain during the measurement phase, unlike our approach that uses narrow beams for the beam training.…”

mentioning

confidence: 99%

Inverse Multipath Fingerprinting for Millimeter Wave V2I Beam Alignment

Choi

Shimizu

et al. 2018

IEEE Trans. Veh. Technol.

198

190

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Efficient beam alignment is a crucial component in millimeter wave systems with analog beamforming, especially in fast-changing vehicular settings. This paper proposes a positionaided approach where the vehicle's position (e.g., available via GPS) is used to query the multipath fingerprint database, which provides prior knowledge of potential pointing directions for reliable beam alignment. The approach is the inverse of fingerprinting localization, where the measured multipath signature is compared to the fingerprint database to retrieve the most likely position. The power loss probability is introduced as a metric to quantify misalignment accuracy and is used for optimizing candidate beam selection. Two candidate beam selection methods are developed, where one is a heuristic while the other minimizes the misalignment probability. The proposed beam alignment is evaluated using realistic channels generated from a commercial ray-tracing simulator. Using the generated channels, an extensive investigation is provided, which includes the required measurement sample size to build an effective fingerprint, the impact of measurement noise, the sensitivity to changes in traffic density, and beam alignment overhead comparison with IEEE 802.11ad as the baseline. Using the concept of beam coherence time, which is the duration between two consecutive beam alignments, and parameters of IEEE 802.11ad, the overhead is compared in the mobility context. The results show that while the proposed approach provides increasing rates with larger antenna arrays, IEEE 802.11ad has decreasing rates due to the larger beam training overhead that eats up a large portion of the beam coherence time, which becomes shorter with increasing mobility.

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mentioning

confidence: 99%

Inverse Multipath Fingerprinting for Millimeter Wave V2I Beam Alignment

Choi

Shimizu

et al. 2018

IEEE Trans. Veh. Technol.

198

190

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“…In the work of Bae et al, 2 a beam tracking method based on the AoD was proposed. Through adoption of the sparse beam space multiple-input-multiple-output (MIMO) channel representation, Duan et al 6 formulated the AoD and AoA tracking problem as a support recovery problem. Bae et al 2 showed that the transmission of only two training beams is enough to track the time-varying AoD with good accuracy.…”

Section: Related Work On Channel Estimation and Beam Trackingmentioning

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

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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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“…However, a base station requires collecting exact data of beamspace channel to select a ray which is not easy in real system, specifically at the time of limited amount of RF chains. Nowadays for solving this barrier, some techniques have been proposed on the basis of compressive sensing (CS) [3,4,5]. The main observation from these mechanisms is to take advantage of the scattering of mmWave channels in the directed area to accurately calculate the mmWave mMIMO channels of broad dimension.…”

Section: Introductionmentioning

confidence: 99%

Untitled

2021

IJWMN

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Beamspace channel estimation mechanism for massive MIMO (multiple input multiple output) antenna system presents a major process to compensate the 5G spectrum challenges caused by the proliferation of information from mobile devices. However, this estimation is required to ensure the perfect channel state information (CSI) for lower amount of Radio Frequency (RF) chains for each beam. In addition, phase shifter (PS) components used in this estimation need high power to select the beam in the desired direction. To overcome these limitations, in this work, we propose Regular Scanning Support Detection (RSSD) based channel estimation mechanism. Moreover, we utilise a 3D lens antenna array having metallic plate and a switch in our model which compensates the limitation of phase shifters. Simulation results show that the proposed RSSD based channel estimation surpasses traditional technique and SD based channel estimation even in lower SNR area which is highly desirable in the millimeter wave (mmWave) massive MIMO systems.

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AoD and AoA tracking with directional sounding beam design for millimeter wave MIMO systems

Cited by 43 publications

References 19 publications

Inverse Multipath Fingerprinting for Millimeter Wave V2I Beam Alignment

Inverse Multipath Fingerprinting for Millimeter Wave V2I Beam Alignment

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

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