Online Sparse Beamforming in C-RAN: A Deep Reinforcement Learning Approach

Zhong, Chonghao; Guo, Kun; Zhao, Mingxiong

doi:10.1109/wcnc49053.2021.9417394

Cited by 5 publications

(2 citation statements)

References 19 publications

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“…Furthermore, in the context of AI-aided beamforming management, C-RANs offers the possibility of enhancing the solutions to related problems by training AI algorithms with data coming from several different and localized radios, which can hugely improve the latency, QoS, and spectral and energy efficiency [193,194].…”

Section: Centralized and Decentralized Learningmentioning

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: Centralized and Decentralized Learningmentioning

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

“…Furthermore, in the context of AI-aided beamforming management, C-RANs offers the possibility of enhancing the solutions to related problems by training AI algorithms with data coming from several different and localized radios, which can hugely improve latency, QoS, and spectral and energy efficiency [190,191].…”

Section: Centralized and Decentralized Learningmentioning

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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Optimization of BBU-RRH Mapping for Load Balancing in 5G C-RAN Using Swarm Intelligence (SI) Algorithms

Rao

Srinivas

2022

Lecture Notes in Networks and Systems

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Online Sparse Beamforming in C-RAN: A Deep Reinforcement Learning Approach

Cited by 5 publications

References 19 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

Optimization of BBU-RRH Mapping for Load Balancing in 5G C-RAN Using Swarm Intelligence (SI) Algorithms

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