Mohamed Benzaghta

Data-Driven Cellular Mobility Management Via Bayesian Optimization and Reinforcement Learning

Mohamed Benzaghta, Sahar Ammar, David López-Pérez, Basem Shihada et autres

Mobility management in cellular networks faces increasing complexity due to network densification and heterogeneous user mobility characteristics. Traditional handover (HO) mechanisms, which rely on predefined parameters such as A3-offset and time-to-trigger (TTT), often fail to optimize mobility performance across varying speeds and …

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Data-Driven Design of Mobile Radio Access Networks via Bayesian Optimization

Mohamed Benzaghta

Aquesta tesi aborda el disseny de les xarxes d'accés ràdio (RAN) mitjançant tècniques basades en dades. Motivats per la creixent heterogeneïtat dels usuaris de la xarxa (UEs) i els nous casos d'ús —en particular, la connectivitat d’aeronaus no tripulades (UAVs)— proposem un …

Data-Driven Design of 3GPP Handover Parameters with Bayesian Optimization and Transfer Learning

Mohamed Benzaghta, Sahar Ammar, David López‐Pérez, Basem Shihada et autres

Mobility management in dense cellular networks is challenging due to varying user speeds and deployment conditions. Traditional 3GPP handover (HO) schemes, relying on fixed A3-offset and time-to-trigger (TTT) parameters, struggle to balance radio link failures (RLFs) and ping-pongs. We propose a data-driven …

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Data-Driven Cellular Mobility Management via Bayesian Optimization and Reinforcement Learning

Mohamed Benzaghta, Sahar Ammar, David López‐Pérez, Basem Shihada et autres

Mobility management in cellular networks faces increasing complexity due to network densification and heterogeneous user mobility characteristics. Traditional handover (HO) mechanisms, which rely on predefined parameters such as A3-offset and time-to-trigger (TTT), often fail to optimize mobility performance across varying speeds and …

Cellular Network Design for UAV Corridors via Data-Driven High-Dimensional Bayesian Optimization

Mohamed Benzaghta, Giovanni Geraci, David López-Pérez, Álvaro Valcarce

We address the challenge of designing cellular networks for uncrewed aerial vehicles (UAVs) corridors through a novel data-driven approach. We assess multiple state-of-the-art high-dimensional Bayesian optimization (HD-BO) techniques to jointly optimize the cell antenna tilts and half-power beamwidth (HPBW). We find that …

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Cellular Network Design for UAV Corridors via Data-driven High-dimensional Bayesian Optimization

Mohamed Benzaghta, Giovanni Geraci, David López‐Pérez, Álvaro Valcarce

We address the challenge of designing cellular networks for uncrewed aerial vehicles (UAVs) corridors through a novel data-driven approach. We assess multiple state-of-the-art high-dimensional Bayesian optimization (HD-BO) techniques to jointly optimize the cell antenna tilts and half-power beamwidth (HPBW). We find that …

Data-Driven Design of 3GPP Handover Parameters with Bayesian Optimization and Transfer Learning

Mohamed Benzaghta, Sahar Ammar, David López‐Pérez, Basem Shihada et autres

Mobility management in dense cellular networks is challenging due to varying user speeds and deployment conditions. Traditional 3GPP handover (HO) schemes, relying on fixed A3-offset and time-to-trigger (TTT) parameters, struggle to balance radio link failures (RLFs) and ping-pongs. We propose a data-driven …

Data-driven Optimization and Transfer Learning for Cellular Network Antenna Configurations

Mohamed Benzaghta, Giovanni Geraci, David López-Pérez, Álvaro Valcarce

We propose a data-driven approach for large-scale cellular network optimization, using a production cellular network in London as a case study and employing Sionna ray tracing for site-specific channel propagation modeling. We optimize base station antenna tilts and half-power beamwidths, resulting in …

Data-Driven Optimization and Transfer Learning for Cellular Network Antenna Configurations

Mohamed Benzaghta, Giovanni Geraci, David López-Pérez, Álvaro Valcarce

We propose a data-driven approach for large-scale cellular network optimization, using a production cellular network in London as a case study and employing Sionna ray tracing for site-specific channel propagation modeling. We optimize base station antenna tilts and half-power beamwidths, resulting in …

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Large-Scale AI in Telecom: Charting the Roadmap for Innovation, Scalability, and Enhanced Digital Experiences

Adnan Shahid, Adrian Kliks, Ahmed Al-Tahmeesschi, Ahmed Elbakary et autres

This white paper discusses the role of large-scale AI in the telecommunications industry, with a specific focus on the potential of generative AI to revolutionize network functions and user experiences, especially in the context of 6G systems. It highlights the development and …

TSpec-LLM: An Open-source Dataset for LLM Understanding of 3GPP Specifications

Rasoul Nikbakht, Mohamed Benzaghta, Giovanni Geraci

Understanding telecom standards involves sorting through numerous technical documents, such as those produced by the 3rd Generation Partnership Project (3GPP), which is time-consuming and labor-intensive. While large language models (LLMs) can assist with the extensive 3GPP knowledge base, an inclusive dataset is …

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Evaluating the Educational Effectiveness of Radar Systems Laboratory Sessions in the Undergraduate Curriculum

Bengisu Yalçınkaya, Mohamed Benzaghta, Remziye Büşra Çoruk, Ali Kara

Introductory courses regarding radar technologies are very popular in the undergraduate curriculum of many electrical and electronics engineering departments. Hands-on experience is an essential part for understanding the theoretical concepts given in lectures. In most cases, it is not affordable for universities …

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