Research Article
Innovative Platform for Resource Allocation in 5G M2M Systems
@INPROCEEDINGS{10.1007/978-3-319-92213-3_3, author={Alexandru Vulpe and George Suciu and Simona Halunga and Octavian Fratu}, title={Innovative Platform for Resource Allocation in 5G M2M Systems}, proceedings={Future Access Enablers for Ubiquitous and Intelligent Infrastructures. Third International Conference, FABULOUS 2017, Bucharest, Romania, October 12-14, 2017, Proceedings}, proceedings_a={FABULOUS}, year={2018}, month={7}, keywords={Wireless networks Machine-to-Machine 5G Resource allocation}, doi={10.1007/978-3-319-92213-3_3} }- Alexandru Vulpe
George Suciu
Simona Halunga
Octavian Fratu
Year: 2018
Innovative Platform for Resource Allocation in 5G M2M Systems
FABULOUS
Springer
DOI: 10.1007/978-3-319-92213-3_3
Abstract
One of the major drivers of cellular network evolution towards 5G systems is the communication between devices, also known as Machine-to-Machine (M2M) communications. M2M mobile connections will reach an estimated 3.2 billion devices and connections by 2020, which will pose a challenge as the state-of-the-art cellular and wireless networks were designed keeping in mind Human-to-Human (H2H) communication. A massive amount of M2M devices create overload problems with a significant impact on the radio access and core network of the cellular system leading to what are known as the problems of RAN overload and CN overload. The paper presents a proof-of-concept hardware implementation of novel resource allocation algorithms in 4G cellular communication systems. The proof-of-concept thus, will enable lab-scale analytical and experimental studies for validating theoretically developed algorithms with the focus being on validating the scheduling and admission control algorithms for M2M scenarios. The platform will be based on an LTE-A eNodeB implemented using a software defined radio (SDR) platform and a UE simulator that enables simulating a large number of UEs sharing the same spectrum. The platform will be complemented by field-programmable gate array (FPGA) devices that enable the hardware implementation of the analytically developed resource allocation algorithms.