Search Results for author: Cicek Cavdar

Found 6 papers, 0 papers with code

Ultra-Reliable Low-Latency Communication for Aerial Vehicles via Multi-Connectivity

no code implementations12 May 2022 Fateme Salehi, Mustafa Ozger, Naaser Neda, Cicek Cavdar

In our numerical study, we find that providing requirements by single connectivity to AVs is very challenging due to the line-of-sight (LoS) interference and reduced gains of downtilt ground base station (BS) antenna.

Cell-Free Massive MIMO in Virtualized CRAN: How to Minimize the Total Network Power?

no code implementations18 Feb 2022 Özlem Tuğfe Demir, Meysam Masoudi, Emil Björnson, Cicek Cavdar

This paper proposes a new cell-free architecture that can be implemented on top of a virtualized cloud radio access network (V-CRAN).

Energy and Resource Efficiency by User Traffic Prediction and Classification in Cellular Networks

no code implementations2 Nov 2021 Amin Azari, Fateme Salehi, Panagiotis Papapetrou, Cicek Cavdar

There is a lack of research on the analysis of per-user traffic in cellular networks, for deriving and following traffic-aware network management.

feature selection Time Series +1

Towards a Rigorous Evaluation of Explainability for Multivariate Time Series

no code implementations6 Apr 2021 Rohit Saluja, Avleen Malhi, Samanta Knapič, Kary Främling, Cicek Cavdar

Machine learning-based systems are rapidly gaining popularity and in-line with that there has been a huge research surge in the field of explainability to ensure that machine learning models are reliable, fair, and can be held liable for their decision-making process.

Decision Making Explainable artificial intelligence +2

Machine Learning assisted Handover and Resource Management for Cellular Connected Drones

no code implementations22 Jan 2020 Amin Azari, Fayezeh Ghavimi, Mustafa Ozger, Riku Jantti, Cicek Cavdar

Here, we first present the major challenges in co-existence of terrestrial and drone communications by considering real geographical network data for Stockholm.

Risk-Aware Resource Allocation for URLLC: Challenges and Strategies with Machine Learning

no code implementations22 Dec 2018 Amin Azari, Mustafa Ozger, Cicek Cavdar

The results further provide insights on the benefits of leveraging intelligent RRM, e. g. a 75% increase in data rate with respect to the conservative design approach for the scheduled traffic is achieved, while the 99. 99% reliability of both scheduled and nonscheduled traffic types is satisfied.

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