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 …

In this paper, we develop a knowledge-assisted deep reinforcement learning (DRL)
algorithm to design wireless schedulers in the fifth-generation (5G) cellular networks with …

The massive growth in mobile data traffic and the heterogeneity and stringency of Quality of
Service (QoS) requirements of various applications have put significant pressure on the …

5G wireless networks are expected to satisfy different delay requirements of various traffics
by network resource scheduling. Existing scheduling methods perform poorly in practice due …

Over the years, several research groups have been developing effective and efficient
scheduling algorithms to enhance the quality of service of mobile communication networks …

The fifth generation (5G) wireless system plays a crucial role to realize future network
applications with diverse services requirements. The 3rd Generation Partnership Project …

Scheduling in cellular networks plays a critical role and is a key differentiating factor of
network performance. The design of scheduling algorithms is challenging since it has to be …

The fifth generation (5G) of cellular networks aims at providing very high data rates, ultra-
reliable low latency, and massive connection density. As one of the fundamental design …

In this paper, we study learning-assisted multi-user scheduling for the wireless downlink.
There have been many scheduling algorithms developed that optimize for a plethora of …

Integrating artificial intelligence (AI) into wireless networks has drawn significant interest in
both industry and academia. A common solution is to replace partial or even all modules in …