Learning based Beam Tracking in 5G NR

Panda, Ipshita; Ramanath, Sreenath

doi:10.1109/comsnets48256.2020.9027433

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…The authors showed that the proposed algorithm can achieve reliable tracking performance with a much shorter alignment time as compared to traditional schemes. In [28], the authors proposed to combine a deep learning-based tracking algorithm (deep sort) and beamforming in 5G NR to predict and track the UE's location. They showed that the predicted UE location can help in focusing data toward their location without any feedback mechanism.…”

Section: Efficient Beam Tracking In Mmwave/thz Systemsmentioning

confidence: 99%

Spatio-Temporal Coherence of mmWave/THz Channel Characteristics and Their Forecasting Using Video Frame Prediction Techniques

Prosvirov,

Ali,

Khakimov

et al. 2023

Mathematics

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Channel state information in millimeter wave (mmWave) and terahertz (THz) communications systems is vital for various tasks ranging from planning the optimal locations of BSs to efficient beam tracking mechanisms to handover design. Due to the use of large-scale phased antenna arrays and high sensitivity to environmental geometry and materials, precise propagation models for these bands are obtained via ray-tracing modeling. However, the propagation conditions in mmWave/THz systems may theoretically change at very small distances, that is, 1 mm–1 μm, which requires extreme computational effort for modeling. In this paper, we first will assess the effective correlation distances in mmWave/THz systems for different outdoor scenarios, user mobility patterns, and line-of-sight (LoS) and non-LoS (nLoS) conditions. As the metrics of interest, we utilize the angle of arrival/departure (AoA/AoD) and path loss of the first few strongest rays. Then, to reduce the computational efforts required for the ray-tracing procedure, we propose a methodology for the extrapolation and interpolation of these metrics based on the convolutional long short-term memory (ConvLSTM) model. The proposed methodology is based on a special representation of the channel state information in a form suitable for state-of-the-art video enhancement machine learning (ML) techniques, which allows for the use of their powerful prediction capabilities. To assess the prediction performance of the ConvLSTM model, we utilize precision and recall as the main metrics of interest. Our numerical results demonstrate that the channel state correlation in AoA/AoD parameters is preserved up until approximately 0.3–0.6 m, which is 300–600 times larger than the wavelength at 300 GHz. The use of a ConvLSTM model allows us to accurately predict AoA and AoD angles up to the 0.6 m distance with AoA being characterized by a higher mean squared error (MSE). Our results can be utilized to speed up ray-tracing simulations by selecting the grid step size, resulting in the desired trade-off between modeling accuracy and computational time. Additionally, it can also be utilized to improve beam tracking in mmWave/THz systems via a selection of the time step between beam realignment procedures.

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Section: Efficient Beam Tracking In Mmwave/thz Systemsmentioning

confidence: 99%

Spatio-Temporal Coherence of mmWave/THz Channel Characteristics and Their Forecasting Using Video Frame Prediction Techniques

Prosvirov,

Ali,

Khakimov

et al. 2023

Mathematics

View full text Add to dashboard Cite

show abstract

“…The second category of beam trackers is based on machine learning algorithms (i.e. data driven approaches), such as recurrent neural networks (RNN) based on long short term memory (LSTM) architecture [17], [18], Deep SORT algorithm [19], and deep reinforcement learning [20]. These trackers are still being studied and explored in the literature to better understand their performance in different types of scenarios.…”

Section: Introductionmentioning

confidence: 99%

Beam and Channel Tracking for 5G Communication Systems Using Adaptive Filtering Techniques: A Comparison Study

Al-Ibadi

Mahmood

2022

JCOMSS

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In this paper, we study the problem of beam tracking of a multipath channel in millimeter-wave massive MIMO communication system using adaptive filters. We focus on the performance of least-mean-square filter (LMS) and recursive least-squares filter (RLS) algorithms, compared to a reference extended Kalman filter (EKF), in scenarios where the wireless channel is dominated by a single line of sight (LOS) path or a small number of strong paths. The signal direction and channel coefficients are tracked and updated using these filters. Our results recommend that beamforming systems at millimeter-wave bands should consider variable number of paths rather than a single dominant LOS path. Furthermore, we show that the mean squared-error (MSE) of the innovation process gives a better overall view of the tracking performance than the MSE of the state parameters.

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Analysis of Beamforming in Dense Urban Deployments

Panda¹,

Ramanath²

2021

2021 International Conference on COMmunication Systems &Amp; NETworkS (COMSNETS)

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Learning based Beam Tracking in 5G NR

Cited by 3 publications

References 6 publications

Spatio-Temporal Coherence of mmWave/THz Channel Characteristics and Their Forecasting Using Video Frame Prediction Techniques

Spatio-Temporal Coherence of mmWave/THz Channel Characteristics and Their Forecasting Using Video Frame Prediction Techniques

Beam and Channel Tracking for 5G Communication Systems Using Adaptive Filtering Techniques: A Comparison Study

Analysis of Beamforming in Dense Urban Deployments

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