Deep reinforcement learning can effectively address resource allocation in wireless
networks. However, its learning speed may be slower in more complex networks and a new …

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 …

In this paper, the convergence time of federated reinforcement learning (FRL) that is
deployed over a realistic wireless network is studied. In the considered model, several …

Advances in Artificial Intelligence (AI) provide new capabilities to handle network routing
problems. However, the lack of up-to-date training data, slow convergence, and low …

In this paper we introduce federated learning (FL) based resource allocation (RA) for
wireless communication networks, where users cooperatively train a RA policy in a …

Driven by the soaring traffic demand and the growing diversity of mobile services, wireless
networks are evolving to be increasingly dense and heterogeneous. Accordingly, in such …

Scalability is the key roadstone towards the application of cooperative intelligent algorithms
in large-scale networks. Reinforcement learning (RL) is known as model-free and high …

We propose a mechanism for distributed resource management and interference mitigation
in wireless networks using multi-agent deep reinforcement learning (RL). We equip each …

Future-generation wireless networks (5G and beyond) must accommodate surging growth in
mobile data traffic and support an increasingly high density of mobile users involving a …

Federated Learning (FL) enables large-scale machine learning without uploading the
private data of wireless devices. Due to the heterogeneity and limitation of the devices' …