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 …

Communication-efficient on-device machine learning: Federated distillation and augmentation under non-iid private data

E Jeong, S Oh, H Kim, J Park, M Bennis… - arXiv preprint arXiv …, 2018 - arxiv.org
On-device machine learning (ML) enables the training process to exploit a massive amount
of user-generated private data samples. To enjoy this benefit, inter-device communication …

Wireless network intelligence at the edge

J Park, S Samarakoon, M Bennis… - Proceedings of the …, 2019 - ieeexplore.ieee.org
Fueled by the availability of more data and computing power, recent breakthroughs in cloud-
based machine learning (ML) have transformed every aspect of our lives from face …

Communication-efficient and distributed learning over wireless networks: Principles and applications

J Park, S Samarakoon, A Elgabli, J Kim… - Proceedings of the …, 2021 - ieeexplore.ieee.org
Machine learning (ML) is a promising enabler for the fifth-generation (5G) communication
systems and beyond. By imbuing intelligence into the network edge, edge nodes can …

A survey of mmWave user association mechanisms and spectrum sharing approaches: An overview, open issues and challenges, future research trends

ML Attiah, AAM Isa, Z Zakaria, MK Abdulhameed… - Wireless …, 2020 - Springer
Fifth generation (5G) cellular networks promise to support multi-radio access technologies
(multi-RATs) with low and high frequencies aiming at delivering good coverage, several …

RIS-assisted coverage enhancement in millimeter-wave cellular networks

M Nemati, J Park, J Choi - IEEE Access, 2020 - ieeexplore.ieee.org
The use of millimeter-wave (mmWave) bandwidth is one key enabler to achieve the high
data rates in the fifth-generation (5G) cellular systems. However, mmWave signals suffer …

Combination of ultra-dense networks and other 5G enabling technologies: A survey

MA Adedoyin, OE Falowo - IEEE Access, 2020 - ieeexplore.ieee.org
Recently, to address the astonishing capacity requirement of 5G, researchers are
investigating the possibility of combining different technologies with ultra-dense networks …

16 federated knowledge distillation

H Seo, J Park, S Oh, M Bennis, SL Kim - Machine Learning and …, 2022 - cambridge.org
Machine learning is one of the key building blocks in 5G and beyond [1–3], spanning a
broad range of applications and use cases. In the context of mission-critical applications [2 …

Wireless edge computing with latency and reliability guarantees

MS Elbamby, C Perfecto, CF Liu, J Park… - Proceedings of the …, 2019 - ieeexplore.ieee.org
Edge computing is an emerging concept based on distributed computing, storage, and
control services closer to end network nodes. Edge computing lies at the heart of the fifth …

Downlink and uplink cell association with traditional macrocells and millimeter wave small cells

H Elshaer, MN Kulkarni, F Boccardi… - IEEE Transactions …, 2016 - ieeexplore.ieee.org
Millimeter wave (mmWave) links will offer high capacity but are poor at penetrating into or
diffracting around solid objects. Thus, we consider a hybrid cellular network with traditional …