A review of advanced localization techniques for crowdsensing wireless sensor networks
The wide availability of sensing modules and computing capabilities in modern mobile
devices (smartphones, smart watches, in-vehicle sensors, etc.) is driving the shift from mote …
devices (smartphones, smart watches, in-vehicle sensors, etc.) is driving the shift from mote …
DeepCog: Cognitive network management in sliced 5G networks with deep learning
Network slicing is a new paradigm for future 5G networks where the network infrastructure is
divided into slices devoted to different services and customized to their needs. With this …
divided into slices devoted to different services and customized to their needs. With this …
On the specialization of fdrl agents for scalable and distributed 6g ran slicing orchestration
Network slicing enables multiple virtual networks to be instantiated and customized to meet
heterogeneous use case requirements over 5G and beyond network deployments …
heterogeneous use case requirements over 5G and beyond network deployments …
DeepCog: Optimizing resource provisioning in network slicing with AI-based capacity forecasting
The dynamic management of network resources is both a critical and challenging task in
upcoming multi-tenant mobile networks, which requires allocating capacity to individual …
upcoming multi-tenant mobile networks, which requires allocating capacity to individual …
Health risks associated with 5G exposure: A view from the communications engineering perspective
The deployment of the fifth-generation (5G) wireless communication services requires the
installation of 5G next-generation Node-B Base Stations (gNBs) over the territory and the …
installation of 5G next-generation Node-B Base Stations (gNBs) over the territory and the …
TRANSIT: Fine-grained human mobility trajectory inference at scale with mobile network signaling data
Call detail records (CDR) collected by mobile phone network providers have been largely
used to model and analyze human-centric mobility. Despite their potential, they are limited in …
used to model and analyze human-centric mobility. Despite their potential, they are limited in …
Traffic prediction-assisted federated deep reinforcement learning for service migration in digital twins-enabled MEC networks
In Mobile Edge Computing (MEC) networks, dynamic service migration can support service
continuity and reduce user-perceived delay. However, service migration in MEC networks …
continuity and reduce user-perceived delay. However, service migration in MEC networks …
Applying big data, machine learning, and SDN/NFV to 5G traffic clustering, forecasting, and management
Traffic clustering, forecasting, and management play a crucial role in improving network
efficiency, network quality, load balancing (LB), and energy saving of mobile networks …
efficiency, network quality, load balancing (LB), and energy saving of mobile networks …
Cache optimization models and algorithms
Caching refers to the act of replicating information at a faster (or closer) medium with the
purpose of improving performance. This deceptively simple idea has given rise to some of …
purpose of improving performance. This deceptively simple idea has given rise to some of …
Data-driven evaluation of anticipatory networking in LTE networks
Anticipatory networking is a recent branch of network optimization based on prediction of the
system state. Our work specifically tackles prediction-driven resource allocation for mobile …
system state. Our work specifically tackles prediction-driven resource allocation for mobile …