Machine Learning Based Network Analysis Using Millimeter-Wave Narrow-Band Energy Traces

Scalabrin, Maria; Bielsa, Guillermo; Loch, Adrian; Rossi, Michele; Widmer, Joerg

doi:10.1109/tmc.2019.2907585

Cited by 5 publications

(6 citation statements)

References 88 publications

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“… [ 20 ] M antenna LOS Developed a machine learning-based framework for learning the surroundings and beamforming codebooks that are hardware responsive. [ 25 ] Large array antenna beam selection Map- based millimeter Wave channel model [ 29 ] Misaligned antennas EM Developing channel traces [ 102 ] Antenna (3TX,4RX) R-D,FFT mmWave sensing is used to create a long-range gesture recognition model. [ 103 ] MIMO antenna Deep Learning state-of-the-art DL based techniques The existing DL based techniques [ 104 , 105 ] are outperformed by the proposed deep learning framework and achieve reasonable channel evaluation accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…In [ 29 ], a model for evaluating a machine learning framework for performing protocol layer analysis and diagnosing physical layer faults in 60 GHz networks was provided. The major goal is to provide a machine learning framework that can appropriately classify transmitted networks and aid in the detection of network faults.…”

Section: Machine Learning and Deep Learning For Various Antenna Desig...mentioning

confidence: 99%

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A review on machine learning and deep learning for various antenna design applications

Khan

Hossain

Majumder

et al. 2022

Heliyon

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Section: Discussionmentioning

confidence: 99%

Section: Machine Learning and Deep Learning For Various Antenna Desig...mentioning

confidence: 99%

A review on machine learning and deep learning for various antenna design applications

Khan

Hossain

Majumder

et al. 2022

Heliyon

View full text Add to dashboard Cite

“…Another research direction that has recently received significant interest from academia and industry is applying machine learning-based techniques for channel estimation, beam alignment, and calculating the hybrid beamforming weights for mmWave systems [38], [39]. At least, in theory, such approaches seem very appealing as they can enable the transceivers to learn about the environment and adapt to new conditions.…”

Section: Discussionmentioning

confidence: 99%

Developing the First mmWave Fully-Connected Hybrid Beamformer With a Large Antenna Array

et al. 2020

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Millimeter wave (mmWave) systems with effective beamforming capability play a key role in fulfilling the high data-rate demands of current and future wireless technologies. Hybrid analog-todigital beamformers have been identified as a cost-effective and energy-efficient solution towards deploying such systems. Most of the existing hybrid beamforming architectures rely on a subconnected phase shifter network with a large number of antennas. Such approaches, however, cannot fully exploit the advantages of large arrays. On the other hand, the current fully-connected beamformers accommodate only a small number of antennas, which substantially limits their beamforming capabilities. In this paper, we present a mmWave hybrid beamformer testbed with a fully-connected network of phase shifters and adjustable attenuators and a large number of antenna elements. To our knowledge, this is the first platform that connects two RF inputs from the baseband to a 16× 8 antenna array, and it operates at 26 GHz with a 2 GHz bandwidth. It provides a wide scanning range of 60 • , and the flexibility to control both the phase and the amplitude of the signals between each of the RF chains and antennas. This beamforming platform can be used in both short and long-range communications with linear equivalent isotropically radiated power (EIRP) variation between 10 dBm and 60 dBm. In this paper, we present the design, calibration procedures and evaluations of such a complex system as well as discussions on the critical factors to consider for their practical implementation. INDEX TERMS Millimeter wave, Multiple-input multiple-output, fully-connected hybrid beamforming, phased array antenna, testbed.

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“…By continuing to research and develop new MIMO shape antennas, it is possible to further improve the performance of wireless communication systems. MIMO (Multiple Input Multiple Output) technology can improve the performance of wireless communication systems by exploiting rich multipath environments to improve channel capacity and reliability [41][42][43].…”

Section: Related Workmentioning

confidence: 99%

“…It has an isolation of 20 dB and operates at a frequency of 5.2 GHz. In addition, work [42] proposes a dual-band MIMO antenna geometry that incorporates a novel B-type printed monopole antenna. The impedance bandwidth of the proposed antenna is 29.9% at 2.45 GHz and 33.8% at 5.8 GHz.…”

Section: Related Workmentioning

confidence: 99%

Machine learning with A MIMO Antenna for Wireless Communication Systems

Shalal,

Shareef,

Taher

et al. 2023

AJEST

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Multiple-input multiple-output (MIMO) antenna is a technology that uses multiple antennas at both the transmitter and receiver to improve the performance of wireless communication systems. One of the challenges in designing MIMO antennas is to reduce the complexity of the antenna array, while still maintaining good performance. One way to reduce complexity is to use shared antennas. Shared antennas are antennas that are used by multiple elements of the MIMO array. This can reduce the number of required antennas and simplify the design of the antenna array. The B-shape of the MIMO antenna by using CST program can also be used to reduce complexity. For example, hexagonal MIMO antennas have been shown to have good performance and can be implemented with a simple design. Additionally, the use of defected ground planes (DGPs) can be used to improve the isolation between the elements of a MIMO antenna array, which can also reduce complexity. The design of MIMO antennas is a complex and challenging task. However, by using shared antennas and other design techniques and AI, it is possible to reduce the complexity of MIMO antennas while still maintaining good performance.

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Machine Learning Based Network Analysis Using Millimeter-Wave Narrow-Band Energy Traces

Cited by 5 publications

References 88 publications

A review on machine learning and deep learning for various antenna design applications

A review on machine learning and deep learning for various antenna design applications

Developing the First mmWave Fully-Connected Hybrid Beamformer With a Large Antenna Array

Machine learning with A MIMO Antenna for Wireless Communication Systems

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