Joint power allocation and channel assignment for NOMA with deep reinforcement learning

C He, Y Hu, Y Chen, B Zeng - IEEE Journal on Selected Areas …, 2019 - ieeexplore.ieee.org
Non-orthogonal multiple access (NOMA) has been considered as a significant candidate
technique for the next generation wireless communication to support high throughput and …

Towards 5G: A reinforcement learning-based scheduling solution for data traffic management

IS Comşa, S Zhang, ME Aydin… - … on Network and …, 2018 - ieeexplore.ieee.org
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 …

Deep convolutional compression for massive MIMO CSI feedback

Q Yang, MB Mashhadi… - 2019 IEEE 29th …, 2019 - ieeexplore.ieee.org
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 …

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 …

Efficient resource allocation utilizing Q-learning in multiple UA communications

Y Kawamoto, H Takagi, H Nishiyama… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
In recent years, unmanned aircraft systems (UASs) have garnered significant attention, and
the demand for communication utilizing unmanned aircrafts (UAs) has increased. However …

A reinforcement learning approach to energy efficiency and QoS in 5G wireless networks

Y Wang, X Dai, JM Wang… - IEEE Journal on Selected …, 2019 - ieeexplore.ieee.org
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 …

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 …

Random forests resource allocation for 5G systems: Performance and robustness study

S Imtiaz, H Ghauch, GP Koudouridis… - 2018 IEEE Wireless …, 2018 - ieeexplore.ieee.org
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 …

Random forests for resource allocation in 5G cloud radio access networks based on position information

S Imtiaz, GP Koudouridis, H Ghauch… - EURASIP Journal on …, 2018 - Springer
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 …

Energy efficient WSN: A cross-layer graph signal processing solution to information redundancy

A Chiumento, N Marchetti… - 2019 16th International …, 2019 - ieeexplore.ieee.org
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 …