Machine learning for large-scale optimization in 6g wireless networks

Y Shi, L Lian, Y Shi, Z Wang, Y Zhou… - … Surveys & Tutorials, 2023 - ieeexplore.ieee.org
The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from
“connected things” to “connected intelligence”, featured by ultra high density, large-scale …

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 …

White paper on broadband connectivity in 6G

N Rajatheva, I Atzeni, E Bjornson, A Bourdoux… - arxiv preprint arxiv …, 2020 - arxiv.org
This white paper explores the road to implementing broadband connectivity in future 6G
wireless systems. Different categories of use cases are considered, from extreme capacity …

Thirty years of machine learning: The road to Pareto-optimal wireless networks

J Wang, C Jiang, H Zhang, Y Ren… - … Surveys & Tutorials, 2020 - ieeexplore.ieee.org
Future wireless networks have a substantial potential in terms of supporting a broad range of
complex compelling applications both in military and civilian fields, where the users are able …

Optimal wireless resource allocation with random edge graph neural networks

M Eisen, A Ribeiro - ieee transactions on signal processing, 2020 - ieeexplore.ieee.org
We consider the problem of optimally allocating resources across a set of transmitters and
receivers in a wireless network. The resulting optimization problem takes the form of …

Deep-learning-based wireless resource allocation with application to vehicular networks

L Liang, H Ye, G Yu, GY Li - Proceedings of the IEEE, 2019 - ieeexplore.ieee.org
It has been a long-held belief that judicious resource allocation is critical to mitigating
interference, improving network efficiency, and ultimately optimizing wireless communication …

A deep learning framework for optimization of MISO downlink beamforming

W **a, G Zheng, Y Zhu, J Zhang, J Wang… - IEEE Transactions …, 2019 - ieeexplore.ieee.org
Beamforming is an effective means to improve the quality of the received signals in multiuser
multiple-input-single-output (MISO) systems. Traditionally, finding the optimal beamforming …

Iterative algorithm induced deep-unfolding neural networks: Precoding design for multiuser MIMO systems

Q Hu, Y Cai, Q Shi, K Xu, G Yu… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Optimization theory assisted algorithms have received great attention for precoding design
in multiuser multiple-input multiple-output (MU-MIMO) systems. Although the resultant …

Spatial deep learning for wireless scheduling

W Cui, K Shen, W Yu - ieee journal on selected areas in …, 2019 - ieeexplore.ieee.org
The optimal scheduling of interfering links in a dense wireless network with full frequency
reuse is a challenging task. The traditional method involves first estimating all the interfering …

Unfolding WMMSE using graph neural networks for efficient power allocation

A Chowdhury, G Verma, C Rao… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
We study the problem of optimal power allocation in a single-hop ad hoc wireless network.
In solving this problem, we depart from classical purely model-based approaches and …