In this paper, we propose a novel meta-scheduling framework with cooperative learning that
fully exploits a functional split structure of the base station (BS) consisting of a central unit …

The long-term viability of energy resources as a main input is essential to achieve long-term
economic growth of a country and the energy efficiency significantly reduces energy …

In this paper, we study radio and energy resource management in renewable energy-
powered wireless networks, where base stations (BSs) are powered by both on-grid and …

In the conventional approaches using reinforcement learning (RL) for resource allocation in
wireless networks, the structure of the policy depends on network circumstances such as the …

We propose unitary group convolutions (UGConvs), a building block for CNNs which
compose a group convolution with unitary transforms in feature space to learn a richer set of …

Recurrent neural networks can be large and compute-intensive, yet many applications that
benefit from RNNs run on small devices with very limited compute and storage capabilities …

Convolutional neural networks (CNNs) are becoming increasingly deeper, wider, and non-
linear because of the growing demand on prediction accuracy and analysis quality. The …

Bio-inspired neuromorphic hardware is a research direction to approach brain's
computational power and energy efficiency. Spiking neural networks (SNN) encode …

Deep neural networks (DNNs), especially deep convolutional neural networks (CNNs), have
emerged as the powerful technique in various machine learning applications. However, the …

Both industry and academia have extensively investigated hardware accelerations. To
address the demands in increasing computational capability and memory requirement, in …