251 citations · 833 across the 65 of their papers we have counts for
22 papers · 1 filter
Deep Deterministic Policy Gradient to Minimize the Age of Information in Cellular V2X Communications
Zoubeir Mlika, Soumaya Cherkaoui
This paper studies the problem of minimizing the age of information (AoI) in cellular vehicle-to-everything communications. To provide minimal AoI and high reliability for vehicles…
Federated Deep Reinforcement Learning for Open RAN Slicing in 6G Networks
Amine Abouaomar, Afaf Taik, Abderrahime Filali +1
Radio access network (RAN) slicing is a key element in enabling current 5G networks and next-generation networks to meet the requirements of different services in various verticals…
Unsupervised Data Splitting Scheme for Federated Edge Learning in IoT Networks
Boubakr Nour, Soumaya Cherkaoui
Federated Edge Learning (FEEL) is a promising distributed learning technique that aims to train a shared global model while reducing communication costs and promoting users' privac…
Communication and Computation O-RAN Resource Slicing for URLLC Services Using Deep Reinforcement Learning
Abderrahime Filali, Boubakr Nour, Soumaya Cherkaoui +1
The evolution of the future beyond-5G/6G networks towards a service-aware network is based on network slicing technology. With network slicing, communication service providers seek…
Dynamic SDN-based Radio Access Network Slicing with Deep Reinforcement Learning for URLLC and eMBB Services
Abderrahime Filali, Zoubeir Mlika, Soumaya Cherkaoui +1
Radio access network (RAN) slicing is a key technology that enables 5G network to support heterogeneous requirements of generic services, namely ultra-reliable low-latency communic…
Competitive Algorithms and Reinforcement Learning for NOMA in IoT Networks
Zoubeir Mlika, Soumaya Cherkaoui
This paper studies the problem of massive Internet of things (IoT) access in beyond fifth generation (B5G) networks using non-orthogonal multiple access (NOMA) technique. The probl…
Resource Provisioning in Edge Computing for Latency Sensitive Applications
Amine Abouaomar, Soumaya Cherkaoui, Zoubeir Mlika +1
Low-Latency IoT applications such as autonomous vehicles, augmented/virtual reality devices and security applications require high computation resources to make decisions on the fl…
A Deep Reinforcement Learning Approach for Service Migration in MEC-enabled Vehicular Networks
Amine Abouaomar, Zoubeir Mlika, Abderrahime Filali +2
Multi-access edge computing (MEC) is a key enabler to reduce the latency of vehicular network. Due to the vehicles mobility, their requested services (e.g., infotainment services)…
Mean-Field Game and Reinforcement Learning MEC Resource Provisioning for SFC
Amine Abouaomar, Soumaya Cherkaoui, Zoubeir Mlika +1
In this paper, we address the resource provisioning problem for service function chaining (SFC) in terms of the placement and chaining of virtual network functions (VNFs) within a…
A Resources Representation For Resource Allocation In Fog Computing Networks
Amine Abouaomar, Soumaya Cherkaoui, Abdellatif Kobbane +1
Fog computing is emerging as a new paradigm to deal with latency-sensitive applications, by making data processing and analysis close to their source. Due to the heterogeneity of d…
Matching-Game for User-Fog Assignment
Amine Abouaomar, Abdellatif Kobbane, Soumaya Cherkaoui
Fog computing has emerged as a new paradigm in mobile network communications, aiming to equip the edge of the network with the computing and storing capabilities to deal with the h…
An Overview of Blockchain and 5G Networks
Hajar Moudoud, Soumaya Cherkaoui, Lyes Khoukhi
The 5G wireless networks are potentially revolutionizing future technologies. The 5G technologies are expected to foresee demands of diverse vertical applications with diverse requ…
An IoT Blockchain Architecture Using Oracles and Smart Contracts: the Use-Case of a Food Supply Chain
Hajar Moudoud, Soumaya Cherkaoui, Lyes Khoukhi
The blockchain is a distributed technology which allows establishing trust among unreliable users who interact and perform transactions with each other. While blockchain technology…
Prediction and Detection of FDIA and DDoS Attacks in 5G Enabled IoT
Hajar Moudoud, Lyes Khoukhi, Soumaya Cherkaoui
Security in the fifth generation (5G) networks has become one of the prime concerns in the telecommunication industry. 5G security challenges come from the fact that 5G networks in…
Towards a Scalable and Trustworthy Blockchain: IoT Use Case
Hajar Moudoud, Soumaya Cherkaoui, Lyes Khoukhi
Recently, blockchain has gained momentum as a novel technology that gives rise to a plethora of new decentralized applications (e.g., Internet of Things (IoT)). However, its integr…
Towards a Secure and Reliable Federated Learning using Blockchain
Hajar Moudoud, Soumaya Cherkaoui, Lyes Khoukhi
Federated learning (FL) is a distributed machine learning (ML) technique that enables collaborative training in which devices perform learning using a local dataset while preservin…
Network Slicing with MEC and Deep Reinforcement Learning for the Internet of Vehicles
Zoubeir Mlika, Soumaya Cherkaoui
The interconnection of vehicles in the future fifth generation (5G) wireless ecosystem forms the so-called Internet of vehicles (IoV). IoV offers new kinds of applications requirin…
Network slicing for vehicular communications: a multi-agent deep reinforcement learning approach
Zoubeir Mlika, Soumaya Cherkaoui
This paper studies the multi-agent resource allocation problem in vehicular networks using non-orthogonal multiple access (NOMA) and network slicing. To ensure heterogeneous servic…
Clustered Vehicular Federated Learning: Process and Optimization
Afaf Taik, Zoubeir Mlika, Soumaya Cherkaoui
Federated Learning (FL) is expected to play a prominent role for privacy-preserving machine learning (ML) in autonomous vehicles. FL involves the collaborative training of a single…
Electrical Load Forecasting Using Edge Computing and Federated Learning
Afaf Taik, Soumaya Cherkaoui
In the smart grid, huge amounts of consumption data are used to train deep learning models for applications such as load monitoring and demand response. However, these applications…
Data-Quality Based Scheduling for Federated Edge Learning
Afaf Taik, Hajar Moudoud, Soumaya Cherkaoui
FEderated Edge Learning (FEEL) has emerged as a leading technique for privacy-preserving distributed training in wireless edge networks, where edge devices collaboratively train ma…
Matching-based Service Offloading for Compute-less Driven IoT Networks
Boubakr Nour, Soumaya Cherkaoui
With the advent of the Internet of Things (IoT) and 5G networks, edge computing is offering new opportunities for business model and use cases innovations. Service providers can no…