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Luis Tello-Oquendo

Researcher at Polytechnic University of Valencia

Publications -  43
Citations -  488

Luis Tello-Oquendo is an academic researcher from Polytechnic University of Valencia. The author has contributed to research in topics: Cellular network & Network congestion. The author has an hindex of 9, co-authored 38 publications receiving 299 citations. Previous affiliations of Luis Tello-Oquendo include Georgia Institute of Technology.

Papers
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Journal ArticleDOI

Performance Analysis and Optimal Access Class Barring Parameter Configuration in LTE-A Networks With Massive M2M Traffic

Luis Tello-Oquendo, +6 more
- 01 Apr 2018 - 
TL;DR: This paper seeks to enhance the performance of LTE-A in massive M2M scenarios by modifying certain configuration parameters and by the implementation of ACB, and observes that ACB is appropriate for handling sporadic periods of congestion.
Proceedings ArticleDOI

Reinforcement Learning-Based ACB in LTE-A Networks for Handling Massive M2M and H2H Communications

Luis Tello-Oquendo, +3 more
TL;DR: A dynamic algorithm based on reinforcement learning to adapt the barring rate parameter of Access Class Barring is proposed, which can adapt it to different traffic conditions, reducing congestion and hence the number of collisions in the RACH.
Proceedings ArticleDOI

Performance analysis of access class barring for handling massive M2M traffic in LTE-A networks

Israel Leyva-Mayorga, +4 more
TL;DR: This paper evaluated the effect of ACB parameters by means of several key performance indicators (KPI) such as delay, energy consumption, and success probability and observed that ACB is appropriate for handling sporadic congestion intervals in LTE-A networks.
Journal ArticleDOI

A Comprehensive Study of the IoT Cybersecurity in Smart Cities

Roberto Andrade, +3 more
- 22 Dec 2020 - 
TL;DR: In this paper, the authors proposed and validated a model based on risk levels to evaluate the IoT cybersecurity maturity in a smart city, based on a top-down approach of cybersecurity incident response in IoT ecosystems.
Journal ArticleDOI

Efficient Random Access Channel Evaluation and Load Estimation in LTE-A With Massive MTC

Luis Tello-Oquendo, +5 more
- 01 Feb 2019 - 
TL;DR: A closed-form expression and an efficient recursion to obtain this joint PDF are derived and the results validate the effectiveness of the recursive formulation and show that its computational cost is considerably lower than that of other related approaches.