New trends in stochastic geometry for wireless networks: A tutorial and survey

Y Hmamouche, M Benjillali, S Saoudi… - Proceedings of the …, 2021 - ieeexplore.ieee.org
Next-generation wireless networks are expected to be highly heterogeneous, multilayered,
with embedded intelligence at both the core and edge of the network. In such a context …

[HTML][HTML] Trends in intelligent communication systems: Review of standards, major research projects, and identification of research gaps

K Koufos, K EI Haloui, M Dianati, M Higgins… - Journal of Sensor and …, 2021 - mdpi.com
The increasing complexity of communication systems, following the advent of
heterogeneous technologies, services and use cases with diverse technical requirements …

A deep-neural-network-based relay selection scheme in wireless-powered cognitive IoT networks

TV Nguyen, TN Tran, K Shim… - IEEE Internet of …, 2020 - ieeexplore.ieee.org
In this article, we propose an efficient deep-neural-network-based relay selection (DNS)
scheme to evaluate and improve the end-to-end throughput in wireless-powered cognitive …

Enhancing PHY-security of FD-enabled NOMA systems using jamming and user selection: Performance analysis and DNN evaluation

K Shim, TN Do, TV Nguyen… - IEEE Internet of Things …, 2021 - ieeexplore.ieee.org
In this article, we study the physical-layer security (PHY-security) improvement method for a
downlink nonorthogonal multiple access (NOMA) system in the presence of an active …

Opportunistic scheduling scheme to improve physical-layer security in cooperative NOMA system: Performance analysis and deep learning design

Y Pramitarini, RHY Perdana, K Shim, B An - IEEE Access, 2024 - ieeexplore.ieee.org
In this paper, we propose a novel opportunistic scheduling-based antenna-user selection
(OBAUS) scheme to improve the secrecy performance of cooperative non-orthogonal …

Secrecy outage performance of ground-to-air communications with multiple aerial eavesdroppers and its deep learning evaluation

T Bao, J Zhu, HC Yang… - IEEE Wireless …, 2020 - ieeexplore.ieee.org
In this letter, we study the secure information transmission from a ground base station (GBS)
to a legitimate unmanned aerial vehicle (UAV) user, in the presence of multiple UAV …

Machine learning classifier approach with gaussian process, ensemble boosted trees, SVM, and linear regression for 5g signal coverage map**

A Gupta, K Ghanshala, RC Joshi - IJIMAI, 2021 - dialnet.unirioja.es
This article offers a thorough analysis of the machine learning classifiers approaches for the
collected Received Signal Strength Indicator (RSSI) samples which can be applied in …

Full-duplex cooperative NOMA network with multiple eavesdroppers and non-ideal system imperfections: Analysis of physical layer security and validation using deep …

T Nimi, AV Babu - IEEE Transactions on Vehicular Technology, 2024 - ieeexplore.ieee.org
In this work, we consider a full-duplex (FD) relay-assisted cooperative non-orthogonal
multiple access (FD-CNOMA) network and examine the physical layer secrecy (PLS) …

Terrain-based coverage manifold estimation: Machine learning, stochastic geometry, or simulation?

R Wang, WU Mondal, MA Kishk… - IEEE Open Journal …, 2023 - ieeexplore.ieee.org
Given the necessity of connecting the unconnected, covering blind spots has emerged as a
critical task in the next-generation wireless communication network. A direct solution …

Comparative analysis of machine learning algorithms for 5G coverage prediction: identification of dominant feature parameters and prediction accuracy

H Yuliana - IEEE Access, 2024 - ieeexplore.ieee.org
5G technology is a key factor in delivering faster and more reliable wireless connectivity.
One crucial aspect in 5G network planning is coverage prediction, which enables network …