Multi-Agent Reinforcement Learning for Power Control in Wireless Networks via Adaptive Graphs

The ever-increasing demand for high-quality and heterogeneous wireless communication services has driven extensive research on dynamic optimization strategies in wireless networks. Among several possible approaches, multi-agent deep reinforcement learning (MADRL) has emerged as a promising method to address a wide range of complex optimization problems like power control. However, the seamless application of MADRL to a variety of network optimization problems faces several challenges related to convergence. In this paper, we present the use of graphs as communication-inducing structures among distributed agents as an effective means to mitigate these challenges. Specifically, we harness graph neural networks (GNNs) as neural architectures for policy parameterization to introduce a relational inductive bias in the collective decision-making process. Most importantly, we focus on modeling the dynamic interactions among sets of neighboring agents through the introduction of innovative methods for defining a graph-induced framework for integrated communication and learning. Finally, the superior generalization capabilities of the proposed methodology to larger networks and to networks with different user categories is verified through simulations.Comment: 6 pages, 4 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

GUMBLE: Uncertainty-Aware Conditional Mobile Data Generation using Bayesian Learning

In the context of mobile and Internet of Things (IoT) networks, data naturally originates at the edge, making crowdsourcing a convenient and inherent approach to data collection. However, crowdsourcing presents challenges related to privacy, sampling bias, statistical sufficiency, and the need for time-consuming post-processing. To this end, generating synthetic data using Deep Learning techniques emerges as a promising solution to overcome such limitations. In this study, we propose an innovative framework that transcends applications and data types, enabling the conditional generation of crowdsourced datasets with location information in mobile and IoT networks. A crucial aspect of our methodology is its ability to assess uncertainty in newly generated samples and produce calibrated predictions through approximate Bayesian methods. Without loss of generality, we ascertain the validity of our method on the task of Minimization of Drive Test (MDT) data generation, presenting for the first time a comparison of synthetically generated data with an original large-scale MDT set collected from a Mobile Network Operator's network infrastructure. By offering a versatile solution to data generation, our framework contributes to overcoming challenges associated with crowdsourced data, opening up possibilities for advanced analytics and experimentation in mobile and IoT networks

Multi-Agent Reinforcement Learning for Power Control in Wireless Networks via Adaptive Graphs

The ever-increasing demand for high-quality and heterogeneous wireless communication services has driven extensive research on dynamic optimization strategies in wireless networks. Among several possible approaches, multi-agent deep reinforcement learning (MADRL) has emerged as a promising method to address a wide range of complex optimization problems like power control. However, the seamless application of MADRL to a variety of network optimization problems faces several challenges related to convergence. In this paper, we present the use of graphs as communication-inducing structures among distributed agents as an effective means to mitigate these challenges. Specifically, we harness graph neural networks (GNNs) as neural architectures for policy parameterization to introduce a relational inductive bias in the collective decision-making process. Most importantly, we focus on modeling the dynamic interactions among sets of neighboring agents through the introduction of innovative methods for defining a graph-induced framework for integrated communication and learning. Finally, the superior generalization capabilities of the proposed methodology to larger networks and to networks with different user categories is verified through simulations