Beam Allocation for Millimeter-Wave MIMO Tracking Systems

Zhang, Deyou; Li, Ang; Chen, He; Wei, Ning; Ding, Ming; Li, Yonghui; Vucetic, Branka

doi:10.1109/tvt.2019.2957039

Cited by 12 publications

(3 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The estimation of AoD is formulated as a weighted sum of previously observed AoD values and can be near to the real AoD with high probability. In contrast to the above work, Zhang et al provided a broad framework for improving the received signal strength in certain directions by using several TX-RX beam pairs [148]. Fig.…”

Section: Machine Learning Based Beam Trackingmentioning

confidence: 99%

Beam Training and Tracking in MmWave Communication: A Survey

Wang¹,

Wei²,

Zhang³

2022

Preprint

View full text Add to dashboard Cite

Communicating on millimeter wave (mmWave) bands is ushering in a new epoch of mobile communication which provides the availability of 10 Gbps high data rate transmission. However, mmWave links are easily prone to short transmission range communication because of the serious free space path loss and the blockage by obstacles. To overcome these challenges, highly directional beams are exploited to achieve robust links by hybrid beamforming. Accurately aligning the transmitter and receiver beams, i.e. beam training, is vitally important to high data rate transmission. However, it may cause huge overhead which has negative effects on initial access, handover, and tracking. Besides, the mobility patterns of users are complicated and dynamic, which may cause tracking error and large tracking latency. An efficient beam tracking method has a positive effect on sustaining robust links. This article provides an overview of the beam training and tracking technologies on mmWave bands and reveals the insights for future research in the 6th Generation (6G) mobile network. Especially, some open research problems are proposed to realize fast, accurate, and robust beam training and tracking. We hope that this survey provides guidelines for the researchers in the area of mmWave communications.

show abstract

Section: Machine Learning Based Beam Trackingmentioning

confidence: 99%

Beam Training and Tracking in MmWave Communication: A Survey

Wang¹,

Wei²,

Zhang³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…However, the method cannot be applied to the codebook based scenario, since the directions of auxiliary beams may not be perfectly matched. Differently, the works [31], [32] formulated the time-varying AoA and AoD as discrete Markov processes with known transition probabilities, and proposed beam tracking strategies to maximize the successful tracking probability. The BS handover was further taken into consideration to fight against beam misalignment and blockage in mobile scenarios [33].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Assisted Calibrated Beam Training for Millimeter-Wave Communication Systems

Sun

et al. 2021

IEEE Trans. Commun.

View full text Add to dashboard Cite

Huge overhead of beam training imposes a significant challenge in millimeter-wave (mmWave) wireless communications. To address this issue, in this paper, we propose a wide beam based training approach to calibrate the narrow beam direction according to the channel power leakage. To handle the complex nonlinear properties of the channel power leakage, deep learning is utilized to predict the optimal narrow beam directly. Specifically, three deep learning assisted calibrated beam training schemes are proposed. The first scheme adopts convolution neural network to implement the prediction based on the instantaneous received signals of wide beam training. We also perform the additional narrow beam training based on the predicted probabilities for further beam direction calibrations. However, the first scheme only depends on one wide beam training, which lacks the robustness to noise. To tackle this problem, the second scheme adopts long-short term memory (LSTM) network for tracking the movement of users and calibrating the beam direction according to the received signals of prior beam training, in order to enhance the robustness to noise. To further reduce the overhead of wide beam training, our third scheme, an adaptive beam training strategy, selects partial wide beams to be trained based on the prior received signals. Two criteria, namely, optimal neighboring criterion and maximum probability criterion, are designed for the selection. Furthermore, to handle mobile scenarios, auxiliary LSTM is introduced to calibrate the directions of the selected wide beams more precisely. Simulation results demonstrate that our proposed schemes achieve significantly higher beamforming gain with smaller beam training overhead compared with the conventional and existing deep-learning based counterparts.

show abstract

“…The first category intends to reduce the required number of probing beams. Specifically, the number scales logarithmically with the array size when advanced signal processing techniques that exploit the sparsity of mmW channel are used [8]- [10]. Further, various side-information, e.g., location information and out-of-band measurements [11], can also be used to reduce the required number of probing beams.…”

mentioning

confidence: 99%

True-Time-Delay Arrays for Fast Beam Training in Wideband Millimeter-Wave Systems

Boljanovic¹,

Han²,

Lin³

et al. 2020

Preprint

View full text Add to dashboard Cite

The best beam steering directions are estimated through beam training, which is one of the most important and challenging tasks in millimeter-wave and sub-terahertz communications. Novel array architectures and signal processing techniques are required to avoid prohibitive beam training overhead associated with large antenna arrays and narrow beams. In this work, we leverage recent developments in true-time-delay (TTD) arrays with large delay-bandwidth products to accelerate beam training using frequency-dependent probing beams. We propose and study two TTD architecture candidates, including analog and hybrid analog-digital arrays, that can facilitate beam training with only one wideband pilot. We also propose a suitable algorithm that requires a single pilot to achieve high-accuracy estimation of angle of arrival. The proposed array architectures are compared in terms of beam training requirements and performance, robustness to practical hardware impairments, and power consumption. The findings suggest that the analog and hybrid TTD arrays achieve a sub-degree beam alignment precision with 66% and 25% lower power consumption than a fully digital array, respectively. Our results yield important design trade-offs among the basic system parameters, power consumption, and accuracy of angle of arrival estimation in fast TTD beam training. Index Terms-True-time-delay array, array architecture, beam training, millimeter-wave communication, wideband systems I. INTRODUCTIONA BUNDANT spectrum at millimeter-wave (mmW) frequencies is seen as the key resource for providing high data rates in the fifth generation of cellular systems [1]. However, the use of mmW communication bands comes at the cost of less favorable propagation conditions [2]. Both the base station (BS) and user equipment (UE) are required to use large antenna arrays to achieve high beamforming (BF) gain and compensate for severe propagation loss. Beam pointing directions are estimated through beam training, a procedure that identifies the angle of arrival (AoA) and angle

show abstract

Beam Allocation for Millimeter-Wave MIMO Tracking Systems

Cited by 12 publications

References 45 publications

Beam Training and Tracking in MmWave Communication: A Survey

Beam Training and Tracking in MmWave Communication: A Survey

Deep Learning Assisted Calibrated Beam Training for Millimeter-Wave Communication Systems

True-Time-Delay Arrays for Fast Beam Training in Wideband Millimeter-Wave Systems

Contact Info

Product

Resources

About