Beyond throughput, the next generation: A 5G dataset with channel and context metrics
In this paper, we present a 5G trace dataset collected from a major Irish mobile operator. The
dataset is generated from two mobility patterns (static and car), and across two application …
dataset is generated from two mobility patterns (static and car), and across two application …
Throughput prediction using machine learning in LTE and 5G networks
D Minovski, N Ögren, K Mitra… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
The emergence of novel cellular network technologies, within 5G, are envisioned as key
enablers of a new set of use-cases, including industrial automation, intelligent …
enablers of a new set of use-cases, including industrial automation, intelligent …
Beyond throughput: A 4G LTE dataset with channel and context metrics
In this paper, we present a 4G trace dataset composed of client-side cellular key
performance indicators (KPIs) collected from two major Irish mobile operators, across …
performance indicators (KPIs) collected from two major Irish mobile operators, across …
A survey on client throughput prediction algorithms in wired and wireless networks
J Schmid, A Höss, BW Schuller - ACM Computing Surveys (CSUR), 2021 - dl.acm.org
Network communication has become a part of everyday life, and the interconnection among
devices and people will increase even more in the future. Nevertheless, prediction of Quality …
devices and people will increase even more in the future. Nevertheless, prediction of Quality …
Accelerating federated learning with cluster construction and hierarchical aggregation
Federated learning (FL) has emerged in edge computing to address the limited bandwidth
and privacy concerns of traditional cloud-based training. However, the existing FL …
and privacy concerns of traditional cloud-based training. However, the existing FL …
Accelerating decentralized federated learning in heterogeneous edge computing
In edge computing (EC), federated learning (FL) enables massive devices to collaboratively
train AI models without exposing local data. In order to avoid the possible bottleneck of the …
train AI models without exposing local data. In order to avoid the possible bottleneck of the …
Adaptive batch size for federated learning in resource-constrained edge computing
The emerging Federated Learning (FL) enables IoT devices to collaboratively learn a
shared model based on their local datasets. However, due to end devices' heterogeneity, it …
shared model based on their local datasets. However, due to end devices' heterogeneity, it …
HiveMind: Towards cellular native machine learning model splitting
The increasing processing load of today's mobile machine learning (ML) application
challenges the stringent computation budget of mobile user equipment (UE). With the wide …
challenges the stringent computation budget of mobile user equipment (UE). With the wide …
PERCEIVE: Deep learning-based cellular uplink prediction using real-time scheduling patterns
J Lee, S Lee, J Lee, SD Sathyanarayana… - Proceedings of the 18th …, 2020 - dl.acm.org
As video calls and personal broadcasting become popular, the demand for mobile live
streaming over cellular uplink channels is growing fast. However, current live streaming …
streaming over cellular uplink channels is growing fast. However, current live streaming …
Mergesfl: Split federated learning with feature merging and batch size regulation
Recently, federated learning (FL) has emerged as a popular technique for edge AI to mine
valuable knowledge in edge computing (EC) systems. To boost the performance of AI …
valuable knowledge in edge computing (EC) systems. To boost the performance of AI …