Deep learning for B5G open radio access network: Evolution, survey, case studies, and challenges
… DEEP LEARNING BASED WORKS FOR RAN In this section, we review existing Deep
Learning-based works addressing the 4G/5G RAN. Then, we show how these works can be …
Learning-based works addressing the 4G/5G RAN. Then, we show how these works can be …
[HTML][HTML] On the application of machine learning to the design of UAV-based 5G radio access networks
… should be capable of adaptively changing their radio access functions in response to dynamic
changes in the environment [1,2]. In particular, radio access networks (RANs) have to face …
changes in the environment [1,2]. In particular, radio access networks (RANs) have to face …
Multi-stage jamming attacks detection using deep learning combined with kernelized support vector machine in 5g cloud radio access networks
… networks has attracted significant attention. Among various anomaly-based intrusion detection
techniques, the most promising one is the machine learning… multi-stage machine learning-…
techniques, the most promising one is the machine learning… multi-stage machine learning-…
[HTML][HTML] Deep learning-driven wireless communication for edge-cloud computing: opportunities and challenges
… of things (IoT) applications, show significant promise for … convergence of radio access
networks and deep learning is … in future wireless networking applications and architectures, this …
networks and deep learning is … in future wireless networking applications and architectures, this …
[HTML][HTML] Resource management in cloud radio access network: Conventional and new approaches
… Traditional radio access networks (RANs) would become exceptionally expensive if they are
to … A survey of machine learning applications for energy-efficient resource management in …
to … A survey of machine learning applications for energy-efficient resource management in …
5G vehicular network resource management for improving radio access through machine learning
… behind this study is to implement a machine learning-enabled … networks has made it somewhat
tricky to manage network … of concept for leveraging machine learning-enabled resource …
tricky to manage network … of concept for leveraging machine learning-enabled resource …
The evolution of radio access network towards open-RAN: Challenges and opportunities
… 8 shows various ML enabled applications driven by modern learning methods [38]. As
per a survey, some machine learning algorithms are already introduced for future wireless …
per a survey, some machine learning algorithms are already introduced for future wireless …
Double deep Q-network-based energy-efficient resource allocation in cloud radio access network
… Qlearning is the Q-value approximation via deep learning. In [24], a DQN based dynamic RA
for self-powered ultra-dense networks is … Another DQN work in [26] is presented to study the …
for self-powered ultra-dense networks is … Another DQN work in [26] is presented to study the …
Deep reinforcement learning based computation offloading and resource allocation for low-latency fog radio access networks
GMS Rahman, T Dang, M Ahmed - … and Converged Networks, 2020 - ieeexplore.ieee.org
… [22] YH Sun, MG Peng, YC Zhou, YZ Huang, and SW Mao, Application of machine learning
in wireless networks: Key techniques and open issues, IEEE Commun. Surv. Tutor., vol. …
in wireless networks: Key techniques and open issues, IEEE Commun. Surv. Tutor., vol. …
Deep learning architectures in emerging cloud computing architectures: Recent development, challenges and next research trend
… This paper presents an extensive systematic literature survey on the applications of deep
learning in emerging cloud … Access Networks within the vicinity of the users [41], [42]. …
learning in emerging cloud … Access Networks within the vicinity of the users [41], [42]. …