Abstract The 5th Generation (5G) and beyond networks are expected to offer huge
throughputs, connect large number of devices, support low latency and large numbers of …

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

One of the key foundations of 5th Generation (5G) and beyond 5G (B5G) networks is
network slicing, in which the network is partitioned into several separated logical networks …

Dynamic radio resource allocation to network slices in mobile networks is challenging due to
the diverse requirements of RAN slices and the dynamic environment of wireless networks …

Collaborative deep reinforcement learning (CDRL) algorithms in which multiple agents can
coordinate over a wireless network is a promising approach to enable future intelligent and …

The development of the 5G network and the transition to 6G has given rise to multiple
challenges for ensuring high-quality and reliable network services. One of these main …

Network slicing enables the provision of customized services in next-generation mobile
networks. Accordingly, the network is divided into logically isolated networks that share …

Fifth-generation (5G) wireless technology promises to be the critical enabler of use cases far
beyond smartphones and other connected devices. This next-generation 5G wireless …

Reinforcement Learning (RL) algorithms have recently been proposed to solve dynamic
radio resource management (RRM) problems in beyond 5G networks. However, RL-based …

The open radio access network (O-RAN) architecture supports intelligent network control
algorithms as one of its core capabilities. Data-driven applications incorporate such …