@article {Filali20222174,
	title = {Dynamic SDN-Based Radio Access Network Slicing With Deep Reinforcement Learning for URLLC and eMBB Services},
	journal = {IEEE Transactions on Network Science and Engineering},
	volume = {9},
	number = {4},
	year = {2022},
	note = {cited By 3},
	pages = {2174-2187},
	abstract = {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 communication (URLLC) and enhanced mobile broadband (eMBB). In this paper, we propose a two time-scales RAN slicing mechanism to optimize the performance of URLLC and eMBB services. In a large time-scale, an SDN controller allocates radio resources to gNodeBs according to the requirements of the eMBB and URLLC services. In a short time-scale, each gNodeB allocates its available resources to its end-users and requests, if needed, additional resources from adjacent gNodeBs. We formulate this problem as a non-linear binary program and prove its NP-hardness. Next, for each time-scale, we model the problem as a Markov decision process (MDP), where the large-time scale is modeled as a single agent MDP whereas the shorter time-scale is modeled as a multi-agent MDP. We leverage the exponential-weight algorithm for exploration and exploitation (EXP3) to solve the single-agent MDP of the large time-scale MDP and the multi-agent deep Q-learning (DQL) algorithm to solve the multi-agent MDP of the short time-scale resource allocation. Extensive simulations show that our approach is efficient under different network parameters configuration and it outperforms recent benchmark solutions. {\textcopyright} 2013 IEEE.},
	keywords = {5G mobile communication systems, Deep learning, EMBB, Heuristic algorithms, Heuristics algorithm, Learning algorithms, Low-latency communication, Markov processes, Multi agent systems, Network slicing, Optimisations, Optimization, Quality of service, Quality-of-service, Radio access networks, Reinforcement learning, Resource allocation, Resource Management, Software agents, Software defined networking, Software-defined networkings, Time measurement, Ultra reliable low latency communication, URLLC},
	doi = {10.1109/TNSE.2022.3157274},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126273319\&doi=10.1109\%2fTNSE.2022.3157274\&partnerID=40\&md5=44d6a41c3d7b8b6d8c4d6918856f36c5},
	author = {Filali, A. and Mlika, Z. and Cherkaoui, S. and Kobbane, A.}
}
@conference {Abouaomar2021273,
	title = {A deep reinforcement learning approach for service migration in MEC-enabled vehicular networks},
	booktitle = {Proceedings - Conference on Local Computer Networks, LCN},
	volume = {2021-October},
	year = {2021},
	note = {cited By 4},
	pages = {273-280},
	abstract = {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) should frequently be migrated across different MEC servers to guarantee their stringent quality of service requirements. In this paper, we study the problem of service migration in a MEC-enabled vehicular network in order to minimize the total service latency and migration cost. This problem is formulated as a nonlinear integer program and is linearized to help obtaining the optimal solution using off-the-shelf solvers. Then, to obtain an efficient solution, it is modeled as a multi-agent Markov decision process and solved by leveraging deep Q learning (DQL) algorithm. The proposed DQL scheme performs a proactive services migration while ensuring their continuity under high mobility constraints. Finally, simulations results show that the proposed DQL scheme achieves close-to-optimal performance. {\textcopyright} 2021 IEEE.},
	keywords = {Deep learning, Edge computing, Infotainment, Integer programming, Learning schemes, Markov processes, Multi agent systems, Multi-access edge computing, Multiaccess, Q-learning, Quality of service, Reinforcement learning, Reinforcement learning approach, Service migration, Vehicle mobility, Vehicular networks},
	doi = {10.1109/LCN52139.2021.9524882},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118466383\&doi=10.1109\%2fLCN52139.2021.9524882\&partnerID=40\&md5=8e35dfee04f347da3f757f57f5d575c8},
	author = {Abouaomar, A. and Mlika, Z. and Filali, A. and Cherkaoui, S. and Kobbane, A.}
}
@conference {Abouaomar2021,
	title = {Mean-Field Game and Reinforcement Learning MEC Resource Provisioning for SFC},
	booktitle = {2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings},
	year = {2021},
	note = {cited By 0},
	abstract = {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 multi-access edge computing (MEC) infrastructure to reduce service delay. We consider the VNFs as the main entities of the system and propose a mean-field game (MFG) framework to model their behavior for their placement and chaining. Then, to achieve the optimal resource provisioning policy without considering the system control parameters, we reduce the proposed MFG to a Markov decision process (MDP). In this way, we leverage reinforcement learning with an actor-critic approach for MEC nodes to learn complex placement and chaining policies. Simulation results show that our proposed approach outperforms benchmark state-of-the-art approaches. {\textcopyright} 2021 IEEE.},
	keywords = {Computer games, Computing infrastructures, Computing resource, e-learning, Edge computing, Markov processes, Mean-field games, Multi-access edge computing, Multiaccess, Network function virtualization, Reinforcement learning, Reinforcement learnings, Resource provisioning, Service function chaining, Service functions, Transfer functions, Virtual addresses, Virtual reality},
	doi = {10.1109/GLOBECOM46510.2021.9685236},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124062107\&doi=10.1109\%2fGLOBECOM46510.2021.9685236\&partnerID=40\&md5=8319d7756843c2ccadb8f9de4bec7eff},
	author = {Abouaomar, A. and Cherkaoui, S. and Mlika, Z. and Kobbane, A.}
}
@conference {Naja2020,
	title = {Alert Message Dissemination using Graph-based Markov Decision Process Model in VANETs},
	booktitle = {IEEE International Conference on Communications},
	volume = {2020-June},
	year = {2020},
	note = {cited By 0},
	abstract = {Vehicular Ad-hoc Networks (VANETs) have many promising applications such as improving vehicle driver{\textquoteright}s safety, and so, decreasing car deaths. In VANETs, the main communication way is broadcast. Such Broadcast needs to be done efficiently especially in congested areas to avoid the broadcast storm problem. A crashed vehicle disseminates a message about its incident to all nodes in the network. The vehicle nodes of the network have to disseminate in their turn the alert message carefully to maximize both reachability and saved rebroadcast (by reducing congestion) while minimizing the delay. This concern is demonstrated and modeled by decision hypothesis. In this paper, at first, we are keen on the Markov decision process (MDP), which is utilized to model and tackle such successive decision issues. We aim to optimize a utility for each vehicle relying upon an irregular domain and choices made by it. As the MDP formalism achieves its cutoff points when it is important to consider the cooperation between the several vehicles. We will begin utilizing the Graph-based MDP where the state and activity spaces are factorizable by factors. We then demonstrate that the transition functions and rewards are deteriorated into nearby functions, and the reliance relations between the vehicles are spoken to by a graph. To Figure the optimal strategy, we use Mean Field Approximation (MFA) for tackling GMDP issue. {\textcopyright} 2020 IEEE.},
	keywords = {Accidents, Broadcast storm problem, Graphic methods, Markov decision process models, Markov Decision Processes, Markov processes, Mean field approximation, Message dissemination, Mobile telecommunication systems, Optimal strategies, Traffic congestion, Transition functions, Vehicles, Vehicular ad hoc networks, Vehicular Adhoc Networks (VANETs)},
	doi = {10.1109/ICC40277.2020.9148997},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089436123\&doi=10.1109\%2fICC40277.2020.9148997\&partnerID=40\&md5=12d16adc7ab7a4b60d7ff52c15709c2c},
	author = {Naja, A. and Oualhaj, O.A. and Boulmalf, M. and Essaaidi, M. and Kobbane, A.}
}
@article {Lhazmir2020,
	title = {A decision-making analysis in UAV-enabled wireless power transfer for IoT networks},
	journal = {Simulation Modelling Practice and Theory},
	volume = {103},
	year = {2020},
	note = {cited By 8},
	abstract = {We consider an IoT network with energy-harvesting capabilities. To extend the network lifetime, we propose a novel unmanned aerial vehicle (UAV)- enabled wireless power transfer (WPT) system, where UAVs move among IoT devices and act as data aggregators and wireless power providers. This paper addresses the decision-making problem since the limited buffer and energy resources constrain all nodes. Each IoT node must decide on whether to request a data transmission, to ask for a wireless energy transfer or to abstain and not take any action. When a UAV receives a request from an IoT device, either for data reception or wireless energy transmission, it has to accept or decline. In this paper, we aim to find a proper packet delivery and energy transfer policy according to the system state that maximizes the data transmission efficiency of the system. We first formulate the problem as a Markov Decision Process (MDP) to tackle the successive decision issues, to optimize a utility for each node upon a casual environment. As the MDP formalism achieves its limits when the interactions between different nodes are considered, we formulate the problem as a Graph-based MDP (GMDP). The transition functions and rewards are then decomposed into local functions, and a graph illustrates the dependency{\textquoteright} relations among the nodes. To obtain the optimal policy despite the system{\textquoteright}s variations, Mean-Field Approximation (MFA) and Approximate linear-programming (ALP) algorithms were proposed to solve the GMDP problem. {\textcopyright} 2020 Elsevier B.V.},
	keywords = {Antennas, Approximate linear programming, Approximation algorithms, Behavioral research, Data communication systems, Data transfer, Data transmission efficiency, Decision making, Decision making analysis, Decision-making problem, Energy harvesting, Energy resources, Energy transfer, Graph theory, Graphic methods, Inductive power transmission, Internet of things, Linear programming, Markov Decision Processes, Markov processes, Mean field approximation, Unmanned aerial vehicles (UAV), Wireless energy transfers, Wireless power transfer (WPT)},
	doi = {10.1016/j.simpat.2020.102102},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084066274\&doi=10.1016\%2fj.simpat.2020.102102\&partnerID=40\&md5=6a1c9e107244a170a782e61d0f9755cc},
	author = {Lhazmir, S. and Oualhaj, O.A. and Kobbane, A. and Mokdad, L.}
}
@conference {Lhazmir2020,
	title = {UAV for Wireless Power Transfer in IoT Networks: A GMDP approach},
	booktitle = {IEEE International Conference on Communications},
	volume = {2020-June},
	year = {2020},
	note = {cited By 3},
	abstract = {Unmanned aerial vehicles (UAVs) are a promising technology employed as moving aggregators and wireless power transmitters for IoT networks. In this paper, we consider an UAV-IoT wireless energy and data transmission system and the decision-making problem is investigated. We aim at optimizing the nodes{\textquoteright} utilities by defining a good packet delivery and energy transfer policy according to the system state. We formulate the problem as a Markov Decision Process (MDP) to tackle the successive decision issues. As the MDP formalism achieves its limits when the neighbors{\textquoteright} interactions are considered, we formulate the problem as a Graph-based MDP (GMDP). We then propose a Mean-Field Approximation (MFA) algorithm to find a solution. The simulation results show that our framework achieves a good analysis of the system behavior. {\textcopyright} 2020 IEEE.},
	keywords = {Antennas, Approximation algorithms, Behavioral research, Decision making, Decision-making problem, Energy transfer, Graphic methods, Inductive power transmission, Internet of things, Markov Decision Processes, Markov processes, Mean field approximation, Packet Delivery, System behaviors, System state, Unmanned aerial vehicles (UAV), Wireless energy, Wireless power},
	doi = {10.1109/ICC40277.2020.9148956},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089418365\&doi=10.1109\%2fICC40277.2020.9148956\&partnerID=40\&md5=a3d1ed58b9a7436f2191be8372102f51},
	author = {Lhazmir, S. and Oualhaj, O.A. and Kobbane, A. and Amlioud, E.M. and Ben-Othman, J.}
}
@article {5099547320100301,
	title = {The Uplink Capacity Evaluation of Wireless Networks: Spectral Analysis Approach.},
	journal = {Journal of Computing \& Information Technology},
	volume = {18},
	number = {1},
	year = {2010},
	pages = {1 - 17},
	abstract = {In this paper we study the capacity of wireless cellular network, in particular the uplink of WCDMA system by using the two dimensional continuous-time Markov chain (CTMC) technique. Considering two types of calls: real-time (RT) calls characterized by a quasi fixed transmission rate, and best-effort (BE) calls which do not require strict demand but need some reliability conditions, we develop an approach based on the spectral analysis for evaluating the cell capacity. We explicitly obtain the simultaneous distribution of the number of RT connections and the number of BE connections in the steady-state. This analysis allows us to simplify the computation of the performance measures including expected delay and throughput of BE traffic. These performances are obtained explicitly in both cases (finite and infinite) of BE calls as function of system parameters like arrival rate of BE and RT calls, service rate of BE and RT calls. These results allow the operator to evaluate the cell capa},
	keywords = {call admission control, Data transmission systems, Markov processes, Matrix-Geometric method, Mobile communication systems, spectral analysis approach, Spectrum analysis, Telecommunication systems, Wireless communication systems, wireless networks},
	issn = {13301136},
	url = {http://search.ebscohost.com/login.aspx?direct=true\&db=iih\&AN=50995473\&site=ehost-live},
	author = {Kobbane, Abdellatif and El-Azouzi, Rachid and Ibrahimi, Khalil and Samanta, Sujit Kumar and Bouyakhf, El-Houssine}
}
