M
Massinissa Lalam
Researcher at École nationale supérieure d'informatique et de mathématiques appliquées de Grenoble
Publications - 11
Citations - 831
Massinissa Lalam is an academic researcher from École nationale supérieure d'informatique et de mathématiques appliquées de Grenoble. The author has contributed to research in topics: Cloud computing & Femtocell. The author has an hindex of 7, co-authored 11 publications receiving 799 citations.
Papers
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Journal ArticleDOI
Cloud technologies for flexible 5G radio access networks
Peter Rost,Carlos J. Bernardos,Antonio De Domenico,Marco Di Girolamo,Massinissa Lalam,Andreas Maeder,Dario Sabella,Dirk Wubben +7 more
TL;DR: How cloud technologies and flexible functionality assignment in radio access networks enable network densification and centralized operation of the radio access network over heterogeneous backhaul networks is discussed.
Journal ArticleDOI
Benefits and Impact of Cloud Computing on 5G Signal Processing: Flexible centralization through cloud-RAN
Dirk Wubben,Peter Rost,Jens Bartelt,Massinissa Lalam,Valentin Savin,Matteo Gorgoglione,Armin Dekorsy,Gerhard Fettweis +7 more
TL;DR: The benefits that cloud computing offers for fifth-generation (5G) mobile networks are explored and the implications on the signal processing algorithms are investigated.
Journal ArticleDOI
Energy Efficiency Benefits of RAN-as-a-Service Concept for a Cloud-Based 5G Mobile Network Infrastructure
Dario Sabella,Antonio De Domenico,Efstathios Katranaras,Muhammad Imran,Marco Di Girolamo,Umer Salim,Massinissa Lalam,Konstantinos Samdanis,Andreas Maeder +8 more
TL;DR: A vision of the advantages of the RANaaS is given, its benefits in terms of energy efficiency are presented, and a consistent system-level power model is proposed as a reference for assessing innovative functionalities toward 5G systems.
Proceedings ArticleDOI
Interference management in self-organized femtocell networks: The BeFEMTO approach
Mehdi Bennis,Lorenza Giupponi,Emilio Mino Diaz,Massinissa Lalam,Masood Maqbool,Emilio Calvanese Strinati,Antonio De Domenico,Matti Latva-aho +7 more
TL;DR: An overview of the BeFEMTO project is given followed by preliminary results based on recent development of distributed algorithms in context aware learning mechanisms, and the performance assessment of self-organizing radio resource management algorithms and interference mitigating techniques for macro-femtocell coexistence is given.
Proceedings ArticleDOI
Kalman filter-based localization for Internet of Things LoRaWAN™ end points
TL;DR: This paper addresses the problem of estimating the location of Internet of Things (IoT) Long Range Wide Area Networks (LoRaWAN) devices from time of arrival differences measured at gateways with particular attention to the processing of outliers.