@article{Lagkas2021,

title = {Optimized Joint Allocation of Radio, Optical, and MEC Resources for the 5G and Beyond Fronthaul},

author = {T. Lagkas and D. Klonidis and P. Sarigiannidis and I. Tomkos},

url = {https://www.researchgate.net/publication/353062079_Optimized_Joint_Allocation_of_Radio_Optical_and_MEC_Resources_for_the_5G_and_Beyond_Fronthaul},

doi = {10.1109/TNSM.2021.3094789},

year  = {2021},

date = {2021-07-06},

journal = {IEEE Transactions on Network and Service Management},

abstract = {In 5G and beyond telecommunication infrastructures a crucial challenge in achieving the strict Key Performance Indicators (KPIs) regarding capacity, latency, and guaranteed quality of service, is the efficient handling of the fronthaul bottleneck. This part of the next generation networks is expected to comprise the New Radio (NR) access and the Next Generation Passive Optical Network (NGPON) domains. Latest developments load the fronthaul with computing tasks as well (e.g., for AI-based processes) in the context of Mobile Edge Computing (MEC). Towards efficient management of all resource types, this paper proposes a joint allocation scheme with three optimization phases for radio, optical, and MEC resources. This scheme, which has been developed in the context of the blueSPACE 5G Infrastructure Public Private Partnership (5G PPP) project, exploits cutting-edge technologies, such as radio beamforming, spatial-spectral granularity in optical networks, and Network Function Virtualization (NFV), to provide dynamic, adaptive, and energy efficient allocation of resources. The devised model is mathematically described and the overall solution is evaluated in a realistic simulation scenario, demonstrating its effectiveness.},

keywords = {5G and beyond, energy efficiency, joint resource allocation, Learning automata, MEC, New Radio, NFV},

pubstate = {published},

tppubtype = {article}

}