Adaptive Millimeter-Wave Communications Exploiting Mobility and Blockage Dynamics

Hussain, Muddassar; Scalabrin, Maria; Rossi, Michele; Michelusi, Nicolò

doi:10.1109/icc40277.2020.9148959

Cited by 6 publications

(5 citation statements)

References 15 publications

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“…However, the extensive body of literature investigated for this survey lacks detailed studies tackling them. For example, in [211], the authors propose an online learning approach to optimize beam training, selection, and handover procedures. However, they do not study the effects high mobility has on the system's performance.…”

Section: Beamforming At Low-snr Regimes and Joint Optimizationmentioning

confidence: 99%

A Literature Survey on AI-Aided Beamforming and Beam Management for 5G and 6G Systems

Brilhante

Manjarres

Moreira

et al. 2023

Sensors

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Modern wireless communication systems rely heavily on multiple antennas and their corresponding signal processing to achieve optimal performance. As 5G and 6G networks emerge, beamforming and beam management become increasingly complex due to factors such as user mobility, a higher number of antennas, and the adoption of elevated frequencies. Artificial intelligence, specifically machine learning, offers a valuable solution to mitigate this complexity and minimize the overhead associated with beam management and selection, all while maintaining system performance. Despite growing interest in AI-assisted beamforming, beam management, and selection, a comprehensive collection of datasets and benchmarks remains scarce. Furthermore, identifying the most-suitable algorithm for a given scenario remains an open question. This article aimed to provide an exhaustive survey of the subject, highlighting unresolved issues and potential directions for future developments. The discussion encompasses the architectural and signal processing aspects of contemporary beamforming, beam management, and selection. In addition, the article examines various communication challenges and their respective solutions, considering approaches such as centralized/decentralized, supervised/unsupervised, semi-supervised, active, federated, and reinforcement learning.

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Section: Beamforming At Low-snr Regimes and Joint Optimizationmentioning

confidence: 99%

A Literature Survey on AI-Aided Beamforming and Beam Management for 5G and 6G Systems

Brilhante

Manjarres

Moreira

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…In practice where multiple base stations are often in place, they can cooperate in the beam association and handoff process to achieve a globally optimal beam selection solution. An online learning approach is proposed in [17] to jointly optimize beam training, data transmission, and handover processes. Nevertheless, the effect of high mobility on the system performance has not been studied.…”

Section: A Related Work and Motivationmentioning

confidence: 99%

Optimal Beam Association for High Mobility mmWave Vehicular Networks: Lightweight Parallel Reinforcement Learning Approach

Huynh

Nguyen

Hoang

et al. 2021

IEEE Trans. Commun.

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“…In this work, we focus on a specific segment of the road link covered by a pair of BSs and a single UE, 1 as depicted in the framed area of Fig. 1.…”

Section: System Modelmentioning

confidence: 99%

“…Let I ∈ {0, 1} I denote the index of the serving BS at time k. Let x k ∈ C L be the transmitted signal with E[ x k 2 2 ] = L, where L denotes the number of symbols transmitted. The 1 The proposed system model and techniques can be applied to a multi-user scenario by partitioning the BS resources using orthogonal frequency division multiple access (OFDMA) and multiple RF chains or time division duplexing (TDD) [20]. received signal at the UE is expressed as…”

Section: A Signal and Channel Modelsmentioning

confidence: 99%

Mobility and Blockage-Aware Communications in Millimeter-Wave Vehicular Networks

Hussain

Scalabrin

Rossi

et al. 2020

IEEE Trans. Veh. Technol.

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Mobility may degrade the performance of nextgeneration vehicular networks operating at the millimeter-wave spectrum: frequent mis-alignment and blockages require repeated beam-training and handover, with enormous overhead. Nevertheless, mobility induces temporal correlations in the communication beams and in blockage events. In this paper, an adaptive design is proposed, that learns and exploits these temporal correlations to reduce the beam-training overhead and make handover decisions. At each time-slot, the serving base station (BS) decides to perform either beam-training, data communication, or handover, under uncertainty in the system state. The decision problem is cast as a partially observable Markov decision process, with the goal to maximize the throughput delivered to the user, under an average power constraint. To address the high-dimensional optimization, an approximate constrained point-based value iteration (C-PBVI) method is developed, which simultaneously optimizes the primal and dual functions to meet the power constraint. Numerical results demonstrate a good match between the analysis and a simulation based on 2D mobility and 3D analog beamforming via uniform planar arrays at both BSs and UE, and reveal that C-PBVI performs near-optimally, and outperforms a baseline scheme with periodic beam-training by 38% in spectral efficiency. Motivated by the structure of C-PBVI, two heuristics are proposed, that trade complexity with sub-optimality, and achieve only 4% and 15% loss in spectral efficiency. Finally, the effect of mobility and multiple users on blockage dynamics is evaluated numerically, demonstrating superior performance over the baseline scheme. Index Terms-Blockage, beam training, handover, millimeter wave, Markov decision process, point based value iteration. I. INTRODUCTION C URRENT sub-6 GHz vehicular communication systems cannot support the demand of future applications such as autonomous driving, augmented reality and infotainment, due to limited bandwidth availability [2]. To this end, new Manuscript

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Adaptive Millimeter-Wave Communications Exploiting Mobility and Blockage Dynamics

Cited by 6 publications

References 15 publications

A Literature Survey on AI-Aided Beamforming and Beam Management for 5G and 6G Systems

A Literature Survey on AI-Aided Beamforming and Beam Management for 5G and 6G Systems

Optimal Beam Association for High Mobility mmWave Vehicular Networks: Lightweight Parallel Reinforcement Learning Approach

Mobility and Blockage-Aware Communications in Millimeter-Wave Vehicular Networks

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