A tutorial on ultrareliable and low-latency communications in 6G: Integrating domain knowledge into deep learning

C She, C Sun, Z Gu, Y Li, C Yang… - Proceedings of the …, 2021 - ieeexplore.ieee.org
As one of the key communication scenarios in the fifth-generation and also the sixth-
generation (6G) mobile communication networks, ultrareliable and low-latency …

Secure transmission rate of short packets with queueing delay requirement

C Li, C She, N Yang, TQS Quek - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Physical layer security (PLS) is promising for secure short-packet transmissions in ultra-
reliable and low-latency communications. The bottlenecks of applying PLS in practice …

Refiner GAN algorithmically enabled deep-RL for guaranteed traffic packets in real-time URLLC B5G communication systems

A Salh, L Audah, KS Kim, SH Alsamhi… - IEEE …, 2022 - ieeexplore.ieee.org
Ultra-reliable and Low-latency Communications (URLLC) is expected to be one of the most
critical characteristics Beyond fifth-Generation (B5G) cellular networks with stringent low …

Power control with QoS guarantees: A differentiable projection-based unsupervised learning framework

M Alizadeh, H Tabassum - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Deep neural networks (DNNs) are emerging as a potential solution to solve NP-hard
wireless resource allocation problems. However, in the presence of intricate constraints, eg …

Learning-based resource allocation in heterogeneous ultradense network

X Zhang, Z Zhang, L Yang - IEEE Internet of Things Journal, 2022 - ieeexplore.ieee.org
Learning-based resource allocation (LRA) is envisioned as an integral element of 6G. This
article proposes a novel learning-based paradigm to address resource allocation problems …

Adaptive wireless power allocation with graph neural networks

N NaderiAlizadeh, M Eisen… - ICASSP 2022-2022 IEEE …, 2022 - ieeexplore.ieee.org
We consider the problem of power control in wireless networks, consisting of multiple
transmitter-receiver pairs communicating with each other over a single shared wireless …

An unsupervised deep unrolling framework for constrained optimization problems in wireless networks

S He, S Xiong, Z An, W Zhang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In wireless networks, the optimization problems generally have complex constraints and are
usually solved via utilizing the traditional optimization methods that have high computational …

New reward-clipping mechanism in deep-learning enabled internet of things in 6G to improve intelligent transmission scheduling

M Alhartomi - 2023 IEEE 13th annual computing and …, 2023 - ieeexplore.ieee.org
Sixth-generation (6G) networks and apps have lately benefited from the use of artificial
intelligence (AI) to improve a significant amount of data. The integration of AI with 6G can …

Online deep learning based energy efficient optimization for IRS-assisted eMBB and URLLC services

W Liu, X He, H Xu, Z Wang, A Zhou - Physical Communication, 2023 - Elsevier
In this paper, we consider a downlink multiple-input single-output (MISO) system for
enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communication …

Machine Learning with Computer Networks: Techniques, Datasets and Models

H Afifi, S Pochaba, A Boltres, D Laniewski… - IEEE …, 2024 - ieeexplore.ieee.org
Machine learning has found many applications in network contexts. These include solving
optimisation problems and managing network operations. Conversely, networks are …