Online Reinforcement Learning for Beam Tracking and Rate Adaptation in Millimeter-wave Systems

Krunz, Marwan; Aykin, Irmak; Sarkar, Sopan; Akgun, Berk

doi:10.1109/tmc.2023.3243910

Cited by 7 publications

(2 citation statements)

References 40 publications

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“…In [107], a limited feedback channel is assumed in order to reproduce real-world scenarios, so a limited CSI is used by a DNN regression for beam allocation, resulting in near-optimal performance in the −10 up to 20 dB SNR regimen. The authors in [108] propose the use of standard ACK/NACK messages transmitted by the UE to the BS during the Hybrid Automatic Repeat Request (HARQ) procedure as the input to an online RL scheme to lower the signaling overhead required for beam tracking and rate adaptation. In [109], the RSRP reported by the UE is used to feed an ML-assisted beam change prediction scheme based on Long Short-Term Memory (LSTM) and helps saving more than half the power used by the UE for Beam Management (BM) compared to other methods.…”

Section: Beam Selection In Mimo Systemsmentioning

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: Beam Selection In Mimo Systemsmentioning

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 [104], a limited feedback channel is assumed in order to reproduce real-world scenarios, so a limited CSI is used by a DNN regression for beam allocation, resulting in near-optimal performance in the −10 up to 20 dB SNR regimen. The authors in [105] propose the use of standard ACK/NACK messages transmitted by the UE to the BS during the Hybrid Automatic Repeat Request (HARQ) procedure as input to an online RL scheme to lower the signaling overhead required for beam tracking and rate adaptation. In [106], the RSRP reported by the UE is used to feed a ML assisted beam change prediction scheme based on Long Short-Term Memory (LSTM), and helps saving more than half the power used by the UE for Beam Management (BM) compared to other methods.…”

Section: Beam Selection In Mimo Systemsmentioning

confidence: 99%

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

Brilhante¹,

Manjares²,

Moreira³

et al. 2023

Preprint

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The performance of modern wireless communication systems is highly dependent on the adoption of multiple antennas and the associated signal processing. In 5G and 6G networks, beamforming and beam management become challenging tasks due to aspects such as user mobility, increased number of antennas, and the adoption of higher frequencies. Artificial intelligence, and more specifically, machine learning, are efficient tools to reduce the complexity involved in generating beams and the overhead associated with beam management without sacrificing system performance. Therefore, AI-aided beamforming and beam management have received a lot of attention recently. This article presents a complete survey on this topic, emphasizing open problems and promising directions. The discussion includes architectural and signal processing aspects of modern beamforming and beam management. The article presents communication problems and respective solutions using centralized/decentralized, supervised/unsupervised, semi-supervised, active, federated, and reinforcement learning.

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Comparison of Centralized and Federated Machine Learning Techniques for Beamtracking in 5G/6G Systems

Ali,

Prosvirov

2024

Lecture Notes in Computer Science

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Online Reinforcement Learning for Beam Tracking and Rate Adaptation in Millimeter-wave Systems

Cited by 7 publications

References 40 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

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

Comparison of Centralized and Federated Machine Learning Techniques for Beamtracking in 5G/6G Systems

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