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

Due to the scarcity in the wireless spectrum and limited energy resources especially in
mobile applications, efficient resource allocation strategies are critical in wireless networks …

In a cell-free network, a large number of mobile devices are served simultaneously by
several base stations (BSs)/access points (APs) using the same time/frequency resources …

Next generation wireless networks are expected to be extremely complex due to their
massive heterogeneity in terms of the types of network architectures they incorporate, the …

Resource allocation in wireless networks, such as device-to-device (D2D) communications,
is usually formulated as mixed integer nonlinear programming (MINLP) problems, which are …

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 …

It has been a long-held belief that judicious resource allocation is critical to mitigating
interference, improving network efficiency, and ultimately optimizing wireless communication …

This work demonstrates the potential of deep reinforcement learning techniques for transmit
power control in wireless networks. Existing techniques typically find near-optimal power …

Heterogeneous cellular networks can offload the mobile traffic and reduce the deployment
costs, which have been considered to be a promising technique in the next-generation …

Many real-world reinforcement learning tasks require multiple agents to make sequential
decisions under the agents' interaction, where well-coordinated actions among the agents …