91 citations · 250 across the 16 of their papers we have counts for
17 papers
Learning Energy-Efficient Hardware Configurations for Massive MIMO Beamforming
Hamed Hojatian, Zoubeir Mlika, Jérémy Nadal +2
Hybrid beamforming (HBF) and antenna selection are promising techniques for improving the energy efficiency~(EE) of massive multiple-input multiple-output~(mMIMO) systems. However,…
User Trajectory Prediction in Mobile Wireless Networks Using Quantum Reservoir Computing
Zoubeir Mlika, Soumaya Cherkaoui, Jean Frédéric Laprade +1
This paper applies a quantum machine learning technique to predict mobile users' trajectories in mobile wireless networks using an approach called quantum reservoir computing (QRC)…
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…
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…
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…
Service Function Chaining in MEC: A Mean-Field Game and Reinforcement Learning Approach
Amine Abouaomar, Soumaya Cherkaoui, Zoubeir Mlika +1
Multi-access edge computing (MEC) and network virtualization technologies are important enablers for fifth-generation (5G) networks to deliver diverse applications and services. Se…
Data-Aware Device Scheduling for Federated Edge Learning
Afaf Taik, Zoubeir Mlika, Soumaya Cherkaoui
Federated Edge Learning (FEEL) involves the collaborative training of machine learning models among edge devices, with the orchestration of a server in a wireless edge network. Due…
Massive IoT Access With NOMA in 5G Networks and Beyond Using Online Competitiveness and Learning
Zoubeir Mlika, Soumaya Cherkaoui
This paper studies the problem of online user grouping, scheduling and power allocation in beyond 5G cellular-based Internet of things networks. Due to the massive number of device…
Resource Allocation in Green Dense Cellular Networks: Complexity and Algorithms
Zoubeir Mlika, Elmahdi Driouch, Wessam Ajib
This paper studies the problem of user association, scheduling and channel allocation in dense cellular networks with energy harvesting base stations (EBSs). In this problem, the E…
On the Base Station Association Problem in HetSNets
Zoubeir Mlika, Elmahdi Driouch, Wessam Ajib +1
The dense deployment of small-cell base stations in HetSNets requires efficient resource allocation techniques. More precisely, the problem of associating users to SBSs must be rev…
User-Base Station Association in HetSNets: Complexity and Efficient Algorithms
Zoubeir Mlika, Mathew Goonewardena, Wessam Ajib +1
This work considers the problem of user association to small-cell base stations (SBSs) in a heterogeneous and small-cell network (HetSNet). Two optimization problems are investigat…