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Machine learning for large-scale optimization in 6g wireless networks
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 …
“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
As one of the key communication scenarios in the fifth-generation and also the sixth-
generation (6G) mobile communication networks, ultrareliable and low-latency …
generation (6G) mobile communication networks, ultrareliable and low-latency …
White paper on broadband connectivity in 6G
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 …
wireless systems. Different categories of use cases are considered, from extreme capacity …
Thirty years of machine learning: The road to Pareto-optimal wireless networks
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 …
complex compelling applications both in military and civilian fields, where the users are able …
Optimal wireless resource allocation with random edge graph neural networks
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 …
receivers in a wireless network. The resulting optimization problem takes the form of …
Deep-learning-based wireless resource allocation with application to vehicular networks
It has been a long-held belief that judicious resource allocation is critical to mitigating
interference, improving network efficiency, and ultimately optimizing wireless communication …
interference, improving network efficiency, and ultimately optimizing wireless communication …
A deep learning framework for optimization of MISO downlink beamforming
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 …
multiple-input-single-output (MISO) systems. Traditionally, finding the optimal beamforming …
Iterative algorithm induced deep-unfolding neural networks: Precoding design for multiuser MIMO systems
Optimization theory assisted algorithms have received great attention for precoding design
in multiuser multiple-input multiple-output (MU-MIMO) systems. Although the resultant …
in multiuser multiple-input multiple-output (MU-MIMO) systems. Although the resultant …
Spatial deep learning for wireless scheduling
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 …
reuse is a challenging task. The traditional method involves first estimating all the interfering …
Unfolding WMMSE using graph neural networks for efficient power allocation
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 …
In solving this problem, we depart from classical purely model-based approaches and …