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

Brilhante, Davi da Silva; Manjarres, Joanna Carolina; Moreira, Rodrigo; Veiga, Lucas de Oliveira; Rezende, José Ferreira de; Müller, Francisco; Klautau, Aldebaro; Mendes, Luciano Leonel; Figueiredo, Felipe Augusto Pereira de

doi:10.3390/s23094359

Cited by 13 publications

(9 citation statements)

References 223 publications

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“…This improves coverage and reduces interference. Beamforming in 6G is expected to play a key role in achieving high data rates and low latency [97]. 6G networks are exploiting advanced beamforming techniques such as hybrid beamforming, which combines digital and analog beamforming, and intelligent beamforming, which uses machine learning techniques to optimize the beamforming process based on real-time network conditions.…”

Section: Beamformingmentioning

confidence: 99%

AI-RAN in 6G Networks: State-of-the-Art and Challenges

Khan,

Schmid

2024

IEEE Open J. Commun. Soc.

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6G is a next-generation cellular communication technology that builds up on existing 5G networks which are currently rolled out worldwide. Through incorporation of artificial intelligence (AI) and machine learning (ML), the core 5G network is advanced into an intelligent 6G network. The 6G Artificial Intelligence Radio Access Network (AI-RAN) is anticipated to offer advanced features like reduced latency, improved bandwidth, data rates and coverage. Furthermore, AI-RAN is expected to support complex use cases such as extreme connectivity, multi-user communications and dynamic spectrum access. This paper provides a detailed survey and thorough assessment of AI-RAN's vision and state-of-the-art challenges. We first present a concise introduction to 6G AI-RAN followed by background information on the current 5G RAN and its challenges that must be overcome to implement 6G AI-RAN. The paper then examines trending research issues in AI-RAN, i.e., challenges related to spectrum allocation, network architecture, and resource management. We discuss the methods to overcome these challenges which include the adoption of advanced machine learning and edge computing technologies to boost the performance of 6G AI-RAN. We conclude by stating open research directions.

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Section: Beamformingmentioning

confidence: 99%

AI-RAN in 6G Networks: State-of-the-Art and Challenges

Khan,

Schmid

2024

IEEE Open J. Commun. Soc.

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“…In the field of AI-assisted beamforming and beam management for 5G and future systems, substantial attention has been given to this topic with numerous recent literature reviews reporting in. Davi da et al [15] carried out a detailed survey, emphasizing the increasing role of artificial intelligence in improving beamforming performance. The work of these authors gives a contextual background for the current study in which main challenges and fields where beamforming can be applied to modern AWCS are highlighted.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning-Based Beamforming Algorithm for Massive MIMO Systems in 5G Networks

Shrikant Upadhyay

2024

jes

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The following research focuses on the use of machine learning-based beamforming algorithms to improve Massive Multiple Input Multiple Output (MIMO) systems in 5G networks. Four unique algorithms namely, the Deep Learning Beamforming Algorithm (DLBA), Reinforcement Learning-Based Doa Estimation Algorithm (RLBEA), Clustering based beam forming algorithm(CBA) and GeneticAlgorithm Based Beam Forming Algoeithm were developed after which each of them was undertook evaluation. Widespread trials, in a simulated 5G environment, have revealed that the DLBA and RLBA considerably outperform other technologies by means of system throughput SINR as well Both the DLBA and RLBA achieved high system throughput, increased SINR levels and low BER. CBA and GABA, using clustering and genetic algorithms as their approaches, displayed moderate values on all assessed composite measures. This research offers important insights on the adaptability and learning potential of machine-learning based beamforming algorithms highlighting their ability to improve efficiency in wireless communication networks during the 5G revolution

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“…Interference mitigation strategies include avoidance, cancellation, coordination, alignment, spatial signal processing, and exploitation, [14]. In general, space signal processing techniques aim to reduce wireless interference from unwanted directions by focusing on the transmission or reception of signals using techniques such as analog, digital, or hybrid beamforming, a key technology used in 5G and proposed for 6G networks to improve wireless communication performance, [15].…”

Section: Vanet and Digital Beamforming Fundamentalsmentioning

confidence: 99%

Vehicular Ad Hoc Network Interference Mitigation Using Digital Beamforming Approach in Roundabout Scenarios

Ornelas-Gutierrez,

Vargas-Rosales,

Villalpando-Hernandez

2023

IEEE Access

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The Vehicular Ad Hoc Networks (VANETs) paradigm is a promising technology to enable the Internet of Vehicles (IoV) for future integration with 5G and 6G infrastructures in smart cities. However, VANET can be susceptible to wireless interference, like co-channel interference, which can cause communication quality and performance to deteriorate, threatening vehicular safety applications, including traffic management and accident prevention. Hence, VANET requires practical interference mitigation strategies, such as digital beamforming, that mitigate the signals from unwanted directions while improving the reception of signals from desired directions with adequate precision, which is helpful for VANET's highly mobile environments. In this paper, a review of the literature on digital beamforming techniques is carried out that examines their advantages and limitations. Furthermore, an important contribution of this paper is the development of an experimental platform for a roundabout VANET based on simulations, where different digital beamforming algorithms, such as phase-shift and Minimum-Variance Distortionless-Response (MVDR), are tested. The results show that the MVDR algorithm leads to improvement due to its adaptability to ad hoc environments. Also, they show that MVDR adaptive beamforming improves Signal-to-Interference-plus-Noise Ratio (SINR) and reduces outage event proportion compared to non-beamforming and conventional beamforming scenarios, highlighting their potential to mitigate co-channel interference in highly changing VANET environments. Therefore, adaptive digital beamforming can improve the performance and reliability of VANETs, offering the potential for advanced wireless network implementations in smart cities with 5G and 6G infrastructures.INDEX TERMS Co-channel interference, digital beamforming, interference mitigation, internet of vehicles, vehicular ad hoc network. This paper aims to assess and contrast the performance of digital beamforming algorithms such as phase-shift and

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

Cited by 13 publications

References 223 publications

AI-RAN in 6G Networks: State-of-the-Art and Challenges

AI-RAN in 6G Networks: State-of-the-Art and Challenges

Machine Learning-Based Beamforming Algorithm for Massive MIMO Systems in 5G Networks

Vehicular Ad Hoc Network Interference Mitigation Using Digital Beamforming Approach in Roundabout Scenarios

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