Survey on machine learning for intelligent end-to-end communication toward 6G: From network access, routing to traffic control and streaming adaption
The end-to-end quality of service (QoS) and quality of experience (QoE) guarantee is quite
important for network optimization. The current 5G and conceived 6G network in the future …
important for network optimization. The current 5G and conceived 6G network in the future …
Survey on joint paradigm of 5G and SDN emerging mobile technologies: Architecture, security, challenges and research directions
Modern communication systems are probable to surface new challenges while introducing
innovative fronts concerning context consciousness in wireless networks. The main outcome …
innovative fronts concerning context consciousness in wireless networks. The main outcome …
Comprehensive review on congestion detection, alleviation, and control for IoT networks
Abstract Context: The Internet of Things (IoT) comprises various computing devices that
operate on a non-standard platform and can connect to wireless networks to transmit data …
operate on a non-standard platform and can connect to wireless networks to transmit data …
[HTML][HTML] End-to-end congestion control approaches for high throughput and low delay in 4G/5G cellular networks
Cellular networks have evolved to support high peak bitrates with low loss rates as observed
by the higher layers. However, applications and services running over cellular networks are …
by the higher layers. However, applications and services running over cellular networks are …
Reinforcement learning-empowered mobile edge computing for 6G edge intelligence
Mobile edge computing (MEC) is considered a novel paradigm for computation-intensive
and delay-sensitive tasks in fifth generation (5G) networks and beyond. However, its …
and delay-sensitive tasks in fifth generation (5G) networks and beyond. However, its …
Edge computing and its role in Industrial Internet: Methodologies, applications, and future directions
Abstract Proliferation of Industrial Internet has dramatically changed the way we live and
work. It brings convenience to our society and sometimes requires real-time processing of …
work. It brings convenience to our society and sometimes requires real-time processing of …
Federated learning for task and resource allocation in wireless high-altitude balloon networks
In this article, the problem of minimizing energy and time consumption for task computation
and transmission in mobile-edge computing-enabled balloon networks is investigated. In the …
and transmission in mobile-edge computing-enabled balloon networks is investigated. In the …
Context-aware taxi dispatching at city-scale using deep reinforcement learning
Proactive taxi dispatching is of great importance to balance taxi demand-supply gaps among
different locations in a city. Recent advances primarily rely on deep reinforcement learning …
different locations in a city. Recent advances primarily rely on deep reinforcement learning …
When machine learning meets network management and orchestration in Edge-based networking paradigms
A Shahraki, T Ohlenforst, F Kreyß - Journal of Network and Computer …, 2023 - Elsevier
Caused by the rising of new network types, eg, Internet of Things (IoT), within the last
decade and related challenges like Big Data and data processing delay, new paradigms …
decade and related challenges like Big Data and data processing delay, new paradigms …
Machine learning for end-to-end congestion control
End-to-end congestion control has been extensively studied for over 30 years as one of the
most important mechanisms to ensure efficient and fair sharing of network resources among …
most important mechanisms to ensure efficient and fair sharing of network resources among …