Adaptive SVM-based Beam Allocation for MmWave Small Cell Networks

Zhu, Jiangpei; Li, Dapeng; Zhao, Haitao; Wang, Xiaoming; Jiang, Rui

doi:10.1109/wcsp49889.2020.9299846

Cited by 2 publications

(1 citation statement)

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“…ML plays an important role in reducing the power and time consumption in millimeter-wave (mm-wave) communications during the beam selection and switching (BSS) process, where an ML algorithm with a single-resolution codebook is selected to obtain an eigen-beam set [33]. Likewise, in [34], the intercell interference of mm-wave signals, when using large antennas, is avoided by a data-driven method based on a fuzzy support vector machine (SVM). Furthermore, AOA estimation with the lowest possible complexity in intricate environments has been achieved in [35], where a data-driven approach employs a MUSIC algorithm in several regression models.…”

Section: B Literature Review Of Machine Learning Based Beamformingmentioning

confidence: 99%

A Review of the State of the Art and Future Challenges of Deep Learning-Based Beamforming

et al. 2022

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The key objective of this paper is to explore the recent state-of-the-art artificial intelligence (AI) applications on the broad field of beamforming. Hence, a multitude of AI-oriented beamforming studies are thoroughly investigated in order to correctly comprehend and profitably interpret the AI contribution in the beamforming performance. Starting from a brief overview of beamforming, including adaptive beamforming algorithms and direction of arrival (DOA) estimation methods, our analysis probes further into the main machine learning (ML) classes, the basic neural network (NN) topologies, and the most efficient deep learning (DL) schemes. Subsequently, and based on the prior aspects, the paper explores several concepts regarding the optimal use of ML and NNs either as standalone beamforming and DOA estimation techniques or in combination with other implementations, such as ultrasound imaging, massive multipleinput multiple-output structures, and intelligent reflecting surfaces. Finally, particular attention is drawn on the realization of beamforming or DOA estimation setups via DL topologies. The survey closes with various important conclusions along with an interesting discussion on potential future aspects and promising research challenges.

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Section: B Literature Review Of Machine Learning Based Beamformingmentioning

confidence: 99%