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 …
Leveraging deep reinforcement learning for traffic engineering: A survey
After decades of unprecedented development, modern networks have evolved far beyond
expectations in terms of scale and complexity. In many cases, traditional traffic engineering …
expectations in terms of scale and complexity. In many cases, traditional traffic engineering …
A deep reinforcement learning-based dynamic traffic offloading in space-air-ground integrated networks (SAGIN)
Space-Air-Ground Integrated Networks (SAGIN) is considered as the key structure of the
next generation network. The space satellites and air nodes are the potential candidates to …
next generation network. The space satellites and air nodes are the potential candidates to …
Hierarchical deep reinforcement learning for backscattering data collection with multiple UAVs
The emerging backscatter communication technology is recognized as a promising solution
to the battery problem of Internet of Things (IoT) devices. For example, the wireless sensor …
to the battery problem of Internet of Things (IoT) devices. For example, the wireless sensor …
QR-SDN: Towards reinforcement learning states, actions, and rewards for direct flow routing in software-defined networks
Flow routing can achieve fine-grained network performance optimizations by routing distinct
packet traffic flows over different network paths. While the centralized control of Software …
packet traffic flows over different network paths. While the centralized control of Software …
DRSIR: A deep reinforcement learning approach for routing in software-defined networking
DM Casas-Velasco, OMC Rendon… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Traditional routing protocols employ limited information to make routing decisions, which
leads to slow adaptation to traffic variability and restricted support to the quality of service …
leads to slow adaptation to traffic variability and restricted support to the quality of service …
Multiagent meta-reinforcement learning for adaptive multipath routing optimization
In this article, we investigate the routing problem of packet networks through multiagent
reinforcement learning (RL), which is a very challenging topic in distributed and autonomous …
reinforcement learning (RL), which is a very challenging topic in distributed and autonomous …
A trusted distributed routing scheme for wireless sensor networks using blockchain and meta‐heuristics‐based deep learning technique
M Revanesh, V Sridhar - Transactions on Emerging …, 2021 - Wiley Online Library
The wireless sensor network (WSN) with fluctuating environs might be susceptible to diverse
types of malicious cyber‐attacks, and they are mostly dependent on the authentication and …
types of malicious cyber‐attacks, and they are mostly dependent on the authentication and …
Adaptive routing in wireless mesh networks using hybrid reinforcement learning algorithm
Wireless mesh networks are popular due to their adaptability, easy-setup, flexibility, cost,
and transmission time-reductions. The routing algorithm plays a vital role in transferring the …
and transmission time-reductions. The routing algorithm plays a vital role in transferring the …
Intelligent routing algorithm for wireless sensor networks dynamically guided by distributed neural networks
Z Liu, Y Liu, X Wang - Computer Communications, 2023 - Elsevier
Using reinforcement learning to adjust the power balance of sensor nodes dynamically is an
essential approach for extending the lifetime of wireless sensor networks (WSNs), which …
essential approach for extending the lifetime of wireless sensor networks (WSNs), which …