Network Slicing and Deep Reinforcement Learning (DRL) are vital enablers for achieving
5G and 6G networks. A 5G/6G network can comprise various network slices from unique or …

Dominated by delay-sensitive and massive data applications, radio resource management
in 5G access networks is expected to satisfy very stringent delay and packet loss …

Fairness-aware learning aims at satisfying various fairness constraints in addition to the
usual performance criteria via data-driven machine learning techniques. Most of the …

Cyber security has grown up to be a hot issue in recent years. How to identify potential
malware becomes a challenging task. To tackle this challenge, we adopt deep learning …

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 …

The expected diversity of services and the variety of use cases in 5G networks will require a
flexible Radio Resource Management able to satisfy the heterogeneous Quality of Service …

Fifth-generation (5G) cellular communication systems have embraced massive multiple-
input-multiple-output (MIMO) in the low-and mid-band frequencies. In a multiband system …

The next generation of wireless networks targets aspiring key performance indicators, like
very low latency, higher data rates and more capacity, paving the way for new generations of …

Due to large-scale control problems in 5G access networks, the complexity of radio resource
management is expected to increase significantly. Reinforcement learning is seen as a …

The problem of radio resource scheduling subject to fairness satisfaction is very challenging
even in future radio access networks. Standard fairness criteria aim to find the best trade-off …