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Found 11 papers, 1 papers with code
Multi-agent Reinforcement Learning for Energy Saving in Multi-Cell Massive MIMO Systems
We develop a multi-agent reinforcement learning (MARL) algorithm to minimize the total energy consumption of multiple massive MIMO (multiple-input multiple-output) base stations (BSs) in a multi-cell network while preserving the overall quality-of-service (QoS) by making decisions on the multi-level advanced sleep modes (ASMs) and antenna switching of these BSs.
Nash Soft Actor-Critic LEO Satellite Handover Management Algorithm for Flying Vehicles
Compared with the terrestrial networks (TN), which can only support limited coverage areas, low-earth orbit (LEO) satellites can provide seamless global coverage and high survivability in case of emergencies.
Reliability and Delay Analysis of 3-Dimensional Networks with Multi-Connectivity: Satellite, HAPs, and Cellular Communications
Based on the simulation results, we find out that even with very efficient interference mitigation, MC is the key enabler for safe remote piloting operations.
Cell-Free Massive MIMO in O-RAN: Energy-Aware Joint Orchestration of Cloud, Fronthaul, and Radio Resources
In this paper, we explore the performance and power consumption of cell-free massive MIMO technology in comparison with traditional small-cell systems, in the virtualized O-RAN architecture.
Digital Twin Assisted Risk-Aware Sleep Mode Management Using Deep Q-Networks
In this study, we model the problem of BS energy saving utilizing multiple sleep modes as a sequential MDP and propose an online traffic-aware deep reinforcement learning approach to maximize the long-term energy saving.
Ultra-Reliable Low-Latency Communication for Aerial Vehicles via Multi-Connectivity
In our numerical study, we find that providing requirements by single connectivity to AVs is very challenging due to the line-of-sight (LoS) interference and reduced gains of downtilt ground base station (BS) antenna.
Cell-Free Massive MIMO in Virtualized CRAN: How to Minimize the Total Network Power?
This paper proposes a new cell-free architecture that can be implemented on top of a virtualized cloud radio access network (V-CRAN).
Energy and Resource Efficiency by User Traffic Prediction and Classification in Cellular Networks
There is a lack of research on the analysis of per-user traffic in cellular networks, for deriving and following traffic-aware network management.
Towards a Rigorous Evaluation of Explainability for Multivariate Time Series
Machine learning-based systems are rapidly gaining popularity and in-line with that there has been a huge research surge in the field of explainability to ensure that machine learning models are reliable, fair, and can be held liable for their decision-making process.
Machine Learning assisted Handover and Resource Management for Cellular Connected Drones
Here, we first present the major challenges in co-existence of terrestrial and drone communications by considering real geographical network data for Stockholm.
Risk-Aware Resource Allocation for URLLC: Challenges and Strategies with Machine Learning
The results further provide insights on the benefits of leveraging intelligent RRM, e. g. a 75% increase in data rate with respect to the conservative design approach for the scheduled traffic is achieved, while the 99. 99% reliability of both scheduled and nonscheduled traffic types is satisfied.
