Position Aided Beam Prediction in the Real World: How Useful GPS Locations Actually Are?

Behboodi, Arash; Pezeshki, Hamed; Alkhateeb, Ahmed

doi:10.48550/arxiv.2205.09054

Cited by 1 publication

(6 citation statements)

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“…This motivated researchers in academia and industry to utilize ML tools to induce intelligence into existing processing blocks of wireless communication networks, e.g., at various network layers [31], [32], for channel estimation [33], [34], [35], for network resource optimization [36], and for wireless power transfer [37]. Motivated by some initial promising studies, ML has also been studied to resolve the BM and tracking problem [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], …”

Section: B Machine Learning For Beam Managementmentioning

confidence: 99%

“…To overcome some of the limitations of the current BM framework, studies propose to utilize side information, such as UE location or measurements from additional frequency (sub-7 GHz) communication bands [56], [57]. However, such proposals fully lean on the availability of side information.…”

Section: H Dependability On Side Informationmentioning

confidence: 99%

“…Given the promising results of location-assisted beam tracking approaches, its performance for a real-world scenario was analyzed in [56]. A comparison of three ML algorithms (lookup table, KNN, NN) in combination with known location is provided over the real-world data set DeepSense [116].…”

Section: ) Location Informationmentioning

confidence: 99%

“…However, FIGURE 8. Comparison of beam measurements overhead of the multi-codebook-based [39], [44], [45], [71] and subspace-reduction-based [40], [41], [43], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [68], [69], [73], [74], [75], [76], [77], [79], [80], [81] surveyed works against exhaustive beam sweeping. The number of UE beams is fixed to N = 16.…”

Section: Evaluation Of Machine-learning-based Millimeter Wave and Ter...mentioning

confidence: 99%

“…However, increasing the number of BS beams results in fine narrower beams M W M . Furthermore, the subset of candidate beams for [40], [41], [43], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [68], [69], [73], [74], [75], [76], [77], [79], [80], [81] at the BS is selected as S M = M 8 . Consequently, the subset of candidate beams increases with increased number of beams at the BS which ensures that the beam prediction accuracy stays similar even with increasing the number of BS beams.…”

Section: Evaluation Of Machine-learning-based Millimeter Wave and Ter...mentioning

confidence: 99%

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Machine Learning for Millimeter Wave and Terahertz Beam Management: A Survey and Open Challenges

Khan¹,

Gaber²,

Schulz

et al. 2023

IEEE Access

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Next-generation wireless communication networks will benefit from beamforming gain to utilize higher bandwidths at millimeter wave (mmWave) and terahertz (THz) bands. For high directional gain, a beam management (BM) framework acquires and tracks optimal downlink and uplink beam pairs through exhaustive beam scan. However, for narrower beams at higher carrier frequencies this leads to a huge beam measurement overhead that negatively impacts the beam acquisition and tracking. Moreover, volatility of mmWave and THz channels, user random mobility patterns, and environmental changes further complicate the BM process. Consequently, machine learning (ML) algorithms that can identify and learn complex mobility patterns and track environmental dynamics have been identified as a remedy. In this article, we provide an overview of the existing ML-based mmWave/THz BM and beam tracking techniques. Especially, we highlight key characteristics of an optimal BM and tracking framework. By surveying the recent studies, we identify some open research challenges and provide our recommendations that can serve as a future direction for researchers in this area.INDEX TERMS 6G, beam management (BM), beam tracking, federated learning (FL), machine learning (ML), millimeter wave (mmWave), reinforcement learning (RL), supervised learning (SL), terahertz (THz). I. INTRODUCTION5G New Radio is being deployed across the world, but in order to meet the requirements of extremely demanding applications like holographic video conferencing and extended reality, the industry and academia have already shifted their attention to the sixth generation (6G) of mobile communications systems. In order to support the multiplicity of use cases, 6G will have to provide much higher data rates, e.g., Tbps [1]. To enable high data rate communications, 5G benefits from higher bandwidths in the range of the millimeter wave (mmWave) spectrum, i.e., from 24.25 GHz to 52.6 GHz. It is now anticipated that 6G will stretch theseThe associate editor coordinating the review of this manuscript and approving it for publication was Olutayo O. Oyerinde . limits to terahertz (THz) bands (0.1 THz to 10 THz) [2], [3]. However, communication at higher frequency bands suffers from severe propagation losses. Consequently, directional communication with high beamforming gain is utilized to ensure coverage in mmWave bands. This necessitates the need of a sophisticated beam management (BM) framework at the transmitter (Tx) and the receiver (Rx) to carefully select an ideal beam pair for data transmission. A. PRELIMINARIES OF TRADITIONAL BEAM MANAGEMENTThe mmWave BM framework specified by the 3rd generation partnership project (3GPP) comprises three operations: initial beam establishment including beam sweeping, beam

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Section: B Machine Learning For Beam Managementmentioning

confidence: 99%

Section: H Dependability On Side Informationmentioning

confidence: 99%

Section: ) Location Informationmentioning

confidence: 99%

Section: Evaluation Of Machine-learning-based Millimeter Wave and Ter...mentioning

confidence: 99%

Section: Evaluation Of Machine-learning-based Millimeter Wave and Ter...mentioning

confidence: 99%

See 3 more Smart Citations

Machine Learning for Millimeter Wave and Terahertz Beam Management: A Survey and Open Challenges

Khan¹,

Gaber²,

Schulz

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

Position Aided Beam Prediction in the Real World: How Useful GPS Locations Actually Are?

Cited by 1 publication

References 0 publications

Machine Learning for Millimeter Wave and Terahertz Beam Management: A Survey and Open Challenges

Machine Learning for Millimeter Wave and Terahertz Beam Management: A Survey and Open Challenges

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