Multiple access integrated adaptive finite blocklength for ultra-low delay in 6G wireless networks

Y Zhang, W Cheng, W Zhang - IEEE Transactions on Wireless …, 2023 - ieeexplore.ieee.org
Facing the dramatic increase of real-time applications and time-sensitive services, large-
scale ultra-low delay requirements are put forward for the sixth generation (6G) wireless …

Deep learning for SWIPT: Optimization of transmit-harvest-respond in wireless-powered interference channel

W Lee, K Lee, HH Choi… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
In this paper, we consider a wireless-powered two-way communication, called transmit-
harvest-respond, with co-channel interference. The two-way communication considered …

AI-assisted framework for green-routing and load balancing in hybrid software-defined networking: Proposal, challenges and future perspective

R Etengu, SC Tan, LC Kwang, FM Abbou… - IEEE …, 2020 - ieeexplore.ieee.org
The explosive growth of IP networks, the advent of cloud computing, and the rapid progress
in wireless communications witnessed today reflect significant progress towards meeting the …

A machine-learning-based action recommender for network operation centers

SA Mohammed, AR Mohammed, D Côté… - … on Network and …, 2021 - ieeexplore.ieee.org
Failure management and cost-aware traffic engineering are two important tasks done in
Network Operation Centers (NOC). These are performed by expert technicians who must …

Big data‐driven machine learning‐enabled traffic flow prediction

F Kong, J Li, B Jiang, T Zhang… - Transactions on …, 2019 - Wiley Online Library
Real‐time effective traffic flow big data prediction network has important application
significance. Over the past few years, traffic flow data have been exploding and we have …

Deep reinforcement learning optimal transmission policy for communication systems with energy harvesting and adaptive MQAM

M Li, X Zhao, H Liang, F Hu - IEEE Transactions on Vehicular …, 2019 - ieeexplore.ieee.org
In this paper, we study an optimal transmission problem in a point-to-point wireless
communication system with energy harvesting and limited battery at its transmitter …

Routing optimization meets Machine Intelligence: A perspective for the future network

B Dai, Y Cao, Z Wu, Z Dai, R Yao, Y Xu - Neurocomputing, 2021 - Elsevier
The future network is expected to support extremely large bandwidth, ultra-low latency or
deterministic delay, extremely high reliability, and massive connectivity for novel forward …

EARS: Intelligence-driven experiential network architecture for automatic routing in software-defined networking

Y Hu, Z Li, J Lan, J Wu, L Yao - China Communications, 2020 - ieeexplore.ieee.org
Software-Defined Networking (SDN) adapts logically-centralized control by decoupling
control plane from data plane and provides the efficient use of network resources. However …

A tensor based deep learning technique for intelligent packet routing

B Mao, ZM Fadlullah, F Tang, N Kato… - … 2017-2017 IEEE …, 2017 - ieeexplore.ieee.org
Recently, network operators are confronting the challenge of exploding traffic and more
complex network environments due to the increasing number of access terminals having …

Traffic modeling and optimization in datacenters with graph neural network

J Li, P Sun, Y Hu - Computer Networks, 2020 - Elsevier
Traffic Optimization (TO) is a well-known and established topic in datacenters with the
fundamental goal of operating networks efficiently. Traditional TO heuristics may suffer from …