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Joint power allocation and channel assignment for NOMA with deep reinforcement learning
Non-orthogonal multiple access (NOMA) has been considered as a significant candidate
technique for the next generation wireless communication to support high throughput and …
technique for the next generation wireless communication to support high throughput and …
Towards 5G: A reinforcement learning-based scheduling solution for data traffic management
Dominated by delay-sensitive and massive data applications, radio resource management
in 5G access networks is expected to satisfy very stringent delay and packet loss …
in 5G access networks is expected to satisfy very stringent delay and packet loss …
Deep convolutional compression for massive MIMO CSI feedback
Massive multiple-input multiple-output (MIMO) systems require downlink channel state
information (CSI) at the base station (BS) to better utilize the available spatial diversity and …
information (CSI) at the base station (BS) to better utilize the available spatial diversity and …
Deep reinforcement learning for dynamic network slicing in IEEE 802.11 networks
S De Bast, R Torrea-Duran… - … -IEEE Conference on …, 2019 - ieeexplore.ieee.org
Network slicing, a key enabler for future wireless networks, divides a physical network into
multiple logical networks that can be dynamically created and configured. In current IEEE …
multiple logical networks that can be dynamically created and configured. In current IEEE …
Efficient resource allocation utilizing Q-learning in multiple UA communications
In recent years, unmanned aircraft systems (UASs) have garnered significant attention, and
the demand for communication utilizing unmanned aircrafts (UAs) has increased. However …
the demand for communication utilizing unmanned aircrafts (UAs) has increased. However …
A reinforcement learning approach to energy efficiency and QoS in 5G wireless networks
Satisfying the huge demand for high bandwidth in 5G networks is in part achieved by vertical
densification of the network infrastructure with so-called small-cell base stations. As a direct …
densification of the network infrastructure with so-called small-cell base stations. As a direct …
eNB selection for machine type communications using reinforcement learning based Markov decision process
YJ Liu, SM Cheng, YL Hsueh - IEEE Transactions on Vehicular …, 2017 - ieeexplore.ieee.org
Machine type communication (MTC), as one of the most promising technologies in the future
wireless communication, has brought mobile communication network into a new level. The …
wireless communication, has brought mobile communication network into a new level. The …
Random forests resource allocation for 5G systems: Performance and robustness study
Next generation cellular networks are expected to improve aggregate multi-user sum rates
by a thousand-fold, implying the deployment of cloud radio access networks (CRANs) that …
by a thousand-fold, implying the deployment of cloud radio access networks (CRANs) that …
Random forests for resource allocation in 5G cloud radio access networks based on position information
Next generation 5G cellular networks are envisioned to accommodate an unprecedented
massive amount of Internet of things (IoT) and user devices while providing high aggregate …
massive amount of Internet of things (IoT) and user devices while providing high aggregate …
Energy efficient WSN: A cross-layer graph signal processing solution to information redundancy
In this work an iterative solution to build a network lifetime-preserving sampling strategy for
WSNs is presented. The paper describes the necessary steps to reconstruct a graph from …
WSNs is presented. The paper describes the necessary steps to reconstruct a graph from …