Reinforcement Learning Based Power-Optimal Usage of Beamforming Antenna Array for Multi-Way Wireless Communication in Vehicular Traffic Environments

Komarraju, Suhasini; Chatterjee, Abhijit

doi:10.1109/mwscas54063.2022.9859297

Cited by 1 publication

(2 citation statements)

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“…In the case of an array and MIMO antenna, RL is a useful tool for beamforming. RL-based intelligent beamforming capability for array antenna and mmWave massive MIMO is presented in [45]- [47]. The efficient beamforming and optimal power usage techniques can reduce co-channel interference, and improve the sum rate capacity for mmWave mobile communications [45]- [47].…”

Section: Ml/dl-based Antenna Designmentioning

confidence: 99%

“…RL-based intelligent beamforming capability for array antenna and mmWave massive MIMO is presented in [45]- [47]. The efficient beamforming and optimal power usage techniques can reduce co-channel interference, and improve the sum rate capacity for mmWave mobile communications [45]- [47]. However, the optimal beamforming for large antenna arrays is a big challenge at the mmWave band.…”

Section: Ml/dl-based Antenna Designmentioning

confidence: 99%

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Applications of Machine Learning and Deep Learning in Antenna Design, Optimization, and Selection: A Review

Sarker,

Podder,

Mondal

et al. 2023

IEEE Access

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This review paper provides an overview of the latest developments in artificial intelligence (AI)-based antenna design and optimization for wireless communications. Machine learning (ML) and deep learning (DL) algorithms are applied to antenna engineering to improve the efficiency of the design and optimization processes. The review discusses the use of electromagnetic (EM) simulators such as computer simulation technology (CST) and high-frequency structure simulator (HFSS) for ML and DL-based antenna design, which also covers reinforcement learning (RL)-bases approaches. Various antenna optimization methods including parallel optimization, single and multi-objective optimization, variable fidelity optimization, multilayer ML-assisted optimization, and surrogate-based optimization are discussed. The review also covers the AI-based antenna selection approaches for wireless applications. To support the automation of antenna engineering, the data generation technique with computational electromagnetics software is described and some useful datasets are reported. The review concludes that ML/DL can enhance antenna behavior prediction, reduce the number of simulations, improve computer efficiency, and speed up the antenna design process.

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