Application of machine learning in wireless networks: Key techniques and open issues
As a key technique for enabling artificial intelligence, machine learning (ML) is capable of
solving complex problems without explicit programming. Motivated by its successful …
solving complex problems without explicit programming. Motivated by its successful …
Orthogonal versus zero-forced beamforming in multibeam antenna systems: Review and challenges for future wireless networks
Y Aslan, A Roederer, NJG Fonseca… - IEEE Journal of …, 2021 - ieeexplore.ieee.org
Orthogonality in multibeam antennas is revisited. The difference between orthogonal
beamforming and zero-forced beamforming is highlighted. The intriguing relation between …
beamforming and zero-forced beamforming is highlighted. The intriguing relation between …
A machine learning framework for resource allocation assisted by cloud computing
Conventionally, resource allocation is formulated as an optimization problem and solved
online with instantaneous scenario information. Since most resource allocation problems …
online with instantaneous scenario information. Since most resource allocation problems …
Clustered cell-free networking: A graph partitioning approach
By moving to millimeter wave (mmWave) frequencies, base stations (BSs) will be densely
deployed to provide seamless coverage in sixth generation (6G) mobile communication …
deployed to provide seamless coverage in sixth generation (6G) mobile communication …
Can dynamic TDD enabled half-duplex cell-free massive MIMO outperform full-duplex cellular massive MIMO?
A Chowdhury, R Chopra… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
We consider a dynamic time division duplex (DTDD) enabled cell-free massive multiple-
input multiple-output (CF-mMIMO) system, where each half-duplex (HD) access point (AP) is …
input multiple-output (CF-mMIMO) system, where each half-duplex (HD) access point (AP) is …
Machine learning based beam selection with low complexity hybrid beamforming design for 5G massive MIMO systems
In this paper, we present an energy-efficient joint machine learning based beam-user
selection and low complexity hybrid beamforming for the multiuser massive multiple-input …
selection and low complexity hybrid beamforming for the multiuser massive multiple-input …
Joint user scheduling and beam selection optimization for beam-based massive MIMO downlinks
In beam-based massive multiple-input multiple-output systems, signals are processed
spatially in the radio-frequency (RF) front end and thereby the number of RF chains can be …
spatially in the radio-frequency (RF) front end and thereby the number of RF chains can be …
The optimal and the greedy: Drone association and positioning schemes for Internet of UAVs
This work considers the deployment of unmanned aerial vehicles (UAVs) over a predefined
area to serve a number of ground users. Due to the heterogeneous nature of the network …
area to serve a number of ground users. Due to the heterogeneous nature of the network …
Meta-Cognitive Radar. Masking Cognition From an Inverse Reinforcement Learner
K Pattanayak, V Krishnamurthy… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
A metacognitive radar switches between two modes of cognition—one mode to achieve a
high-quality estimate of targets, and the other mode to hide its utility function (plan). To …
high-quality estimate of targets, and the other mode to hide its utility function (plan). To …
Massive MIMO for high-accuracy target localization and tracking
High-accuracy target localization and tracking have been widely used in the modern
navigation system. However, most of the methods such as global positioning system (GPS) …
navigation system. However, most of the methods such as global positioning system (GPS) …