Efficient resource allocation strategies are pivotal in vehicular communications as connected
devices steeply increase in scenarios with much more stringent requirements. In this work …

The quantization of neural networks for the mitigation of the nonlinear and components'
distortions in dual-polarization optical fiber transmission is studied. Two low-complexity …

Wireless communication technologies have profoundly impacted the interconnectivity of
mobile users and terminals. Nevertheless, the exponential increase in the number of users …

The use of directly modulated lasers (DMLs) is attractive in low-power, cost-constrained
short-reach optical links. However, their limited modulation bandwidth can induce waveform …

Deep neural networks (DNNs) have been widely used for learning precoding policy to
achieve good system performance with low computational complexity. However, existing …

Neuromorphic computing based on spiking neural networks (SNN) realized in CMOS mixed-
signal circuits promises lower power consumption than conventional digital computing. This …

Convolutional Neural Networks (CNNs) have demonstrated remarkable performance in
medical image segmentation tasks, with the U-Net architecture being a prominent example …

Cyber-attacks are becoming increasingly sophisticated, posing more significant challenges
to traditional intrusion detection methods. The inability to prevent intrusions could …

Graph neural networks (GNNs) have been used for optimizing resource allocation due to
their potential in scalability and size generalizability. To facilitate their application to large …

Conventional Time Series Classification (TSC) methods are often black boxes that obscure
inherent interpretation of their decision-making processes. In this work, we leverage Multiple …