The incumbent Internet of Things suffers from poor scalability and elasticity exhibiting in
communication, computing, caching and control (4Cs) problems. The recent advances in …

The dynamicity of distributed wireless networks caused by node mobility, dynamic network
topology, and others has been a major challenge to routing in such networks. In the …

Deep reinforcement learning (DRL) is the combination of reinforcement learning (RL) and
deep learning. It has been able to solve a wide range of complex decision-making tasks that …

As deep reinforcement learning (RL) showcases its strengths in networking and systems, its
pitfalls also come to the public's attention--when trained to handle a wide range of network …

Interference among concurrent transmissions in a wireless network is a key factor limiting
the system performance. One way to alleviate this problem is to manage the radio resources …

Unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS) technologies
have recently been identified as enablers for future wireless networks. Deep reinforcement …

With the widespread adoption of deep learning, reinforcement learning (RL) has
experienced a dramatic increase in popularity, scaling to previously intractable problems …

One of the fundamental issues for Federated Learning (FL) is data heterogeneity, which
causes accuracy degradation, slow convergence, and the communication bottleneck issue …

Reinforcement learning (RL) is capable of managing wireless, energy-harvesting IoT nodes
by solving the problem of autonomous management in non-stationary, resource-constrained …

Reinforcement Learning (RL) is crucial for data-driven decisionmaking but suffers from
sample inefficiency. This poses a risk to system safety and can be costly in real-world …