UAV selection for high-speed train communication using OTFS modulation.

Providing continuous wireless connectivity for high-speed trains (HSTs) is challenging due to their high speeds, making installing numerous ground base stations (BSs) along the HST route an expensive solution, particularly in rural and wilderness areas. This paper proposes using multiple unmanned aerial vehicles (UAVs) to deliver high data rate wireless connectivity for HSTs, taking advantage of their ability to fly, hover, and maneuver at low altitudes. However, autonomously selecting the optimal UAV by the HST is challenging.

Early Diagnosis of COVID-19 Images Using Optimal CNN Hyperparameters.

Coronavirus disease (COVID-19) is a worldwide epidemic that poses substantial health hazards. However, COVID-19 diagnostic test sensitivity is still restricted due to abnormalities in specimen processing. Meanwhile, optimizing the highly defined number of convolutional neural network (CNN) hyperparameters (hundreds to thousands) is a useful direction to improve its overall performance and overcome its cons.

Two-Stage Multiarmed Bandit for Reconfigurable Intelligent Surface Aided Millimeter Wave Communications.

A reconfigurable intelligent surface (RIS) is a promising technology that can extend short-range millimeter wave (mmWave) communications coverage. However, phase shifts (PSs) of both mmWave transmitter (TX) and RIS antenna elements need to be optimally adjusted to effectively cover a mmWave user. This paper proposes codebook-based phase shifters for mmWave TX and RIS to overcome the difficulty of estimating their mmWave channel state information (CSI).

Wi-Fi Assisted Contextual Multi-Armed Bandit for Neighbor Discovery and Selection in Millimeter Wave Device to Device Communications.

The unique features of millimeter waves (mmWaves) motivate its leveraging to future, beyond-fifth-generation/sixth-generation (B5G/6G)-based device-to-device (D2D) communications. However, the neighborhood discovery and selection (NDS) problem still needs intelligent solutions due to the trade-off of investigating adjacent devices for the optimum device choice against the crucial beamform training (BT) overhead. In this paper, by making use of multiband (μW/mmWave) standard devices, the mmWave NDS problem is addressed using machine-learning-based contextual multi-armed bandit (CMAB) algorithms.

Gateway Selection in Millimeter Wave UAV Wireless Networks Using Multi-Player Multi-Armed Bandit.

Recently, unmanned aerial vehicle (UAV)-based communications gained a lot of attention due to their numerous applications, especially in rescue services in post-disaster areas where the terrestrial network is wholly malfunctioned. Multiple access/gateway UAVs are distributed to fully cover the post-disaster area as flying base stations to provide communication coverage, collect valuable information, disseminate essential instructions, etc. The access UAVs after gathering/broadcasting the necessary information should select and fly towards one of the surrounding gateways for relaying their information.