2026-04-17T01:02:02Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/1843722026-02-02T09:49:45Zcom_2072_1033col_2072_452950

Dynamic resource allocation in C-RAN with real-time traffic and realistic scenarios Guerra Gómez, Rolando Ruiz Boqué, Sílvia García Lozano, Mario Olmos Bonafé, Juan José Universitat Politècnica de Catalunya. Doctorat en Teoria del Senyal i Comunicacions Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions Universitat Politècnica de Catalunya. WiComTec - Grup de recerca en Tecnologies i Comunicacions Sense Fils Àrees temàtiques de la UPC::Enginyeria de la telecomunicació Machine learning Integrated services digital networks C-RAN Optimization Resource allocation Realistic scenarios Real-time traffic Dades massives Aprenentatge automàtic Ordinadors, Xarxes d' Xarxes Digitals de Serveis Integrats This work proposes the use of a modified and improved version of the realistic Vienna Scenario that was defined in COST action IC1004, to test two different scale C-RAN deployments. First, a large-scale analysis with 628 Macro-cells (Mcells) and 221 Small-cells (Scells) is used to test different algorithms oriented to optimize the network deployment by minimizing delays, balancing the load among the Base Band Unit (BBU) pools, or clustering the Remote Radio Heads (RRH) efficiently to maximize the multiplexing gain. After planning, real-time resource allocation strategies with QoS constraints should be optimized as well. To do so, a realistic small-scale scenario for the metropolitan area is defined by modelling the individual time-variant traffic patterns of 7000 users (UEs) connected to different services. The distribution of resources among UEs and BBUs is optimized by algorithms, based on a realistic calculation of the UEs Signal to Interference and Noise Ratios (SINRs), that account for the required computational capacity per cell, the QoS constraints and the service priorities. Results show that even after the optimization, there are some time intervals where the allocated resources are underutilized, which opens the door to the definition of new Machine Learning algorithms able to predict the required capacity. This work has been funded by the Spanish ministry of science through the project RTI2018-099880-B-C32 and with ERFD funds. This work has been done under COST CA15104 IRACON EU project. Peer Reviewed Postprint (published version) 2019 Conference report Guerra, R. [et al.]. Dynamic resource allocation in C-RAN with real-time traffic and realistic scenarios. A: IEEE International Conference on Wireless and Mobile Computing, Networking and Communications. "15th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2019)". Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 1-6. 978-1-7281-3316-4 https://hdl.handle.net/2117/184372 10.1109/WiMOB.2019.8923310 eng https://ieeexplore.ieee.org/document/8923310 info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-099880-B-C32/ES/ROBOTICA EN LA NUBE Y EL IMPACTO DE LAS REDES 5G EN LA FABRICA DEL FUTURO. SUBPROYECTO UPC./ info:eu-repo/grantAgreement/EC/IRACON/COSTCA15104 Restricted access - publisher's policy 6 p. application/pdf Institute of Electrical and Electronics Engineers (IEEE)