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Sobre

Sobre

He is a Project Manager and Innovation Specialist with a strong R&D background in emerging technologies, advanced network solutions, and AI-driven optimization. With 20+ years of leadership in managing complex technical projects across academia and industry, he brings proven expertise in scenario planning, system design, and technology integration. He is also highly skilled in C++, Python, and MATLAB, and serves as a trusted reviewer for IEEE and Elsevier.


He contributes to international projects, including HURRICANE (Horizon Research and Innovation Actions) as leader, CONVERGE, Nexus, and previously H2020-ResponDrone, focusing on innovative solutions for airborne communications and next-generation networks.


His career spans cutting-edge research and implementation in advanced communication systems, high-frequency networks (#5G, #6G), aerial and airborne technologies, and applied artificial intelligence across complex systems.


As an invited professor and technologist, he has taught a wide range of university-level courses in telecommunications, programming, ML/AI, and electronics in both public and private institutions.


In the industry, he has held prominent positions, including Network Specialist and Infrastructure Supervisor at Telecommunication Company of Iran (#TCI), and was the Founder, CEO, and Chairman of Hiva Sanaat Noor R&D Institute. He has managed 20+ R&D and projects, organized specialized workshops, and delivered keynote lectures on topics such as research methods, programming, and AI applications.


He also served as Director of TCI’s Research Center, with domain knowledge in GPON, FTTH, telecom infrastructure, and maintenance strategies. Additionally, he was Head of Huawei’s Digital Switching Research Center and a senior PSDN Switching expert.


He has authored 10+ technical books, academic papers, and lab manuals, and has deep knowledge in computer networks, cybersecurity, traffic engineering, and network software development.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Kamran Shafafi
  • Cargo

    Assistente de Investigação
  • Desde

    01 dezembro 2021
Publicações

2025

Autonomous Vision-Aided UAV Positioning for Obstacle-Aware Wireless Connectivity

Autores
Shafafi, K; Ricardo, M; Campos, R;

Publicação
CoRR

Abstract

2025

Joint Optimization of Multi-UAV Deployment and 3D Positioning in Traffic-Aware Aerial Networks

Autores
Shafafi, K; Abdellatif, AA; Ricardo, M; Campos, R;

Publicação
CoRR

Abstract

2025

PEL: Population-Enhanced Learning Classification for ECG Signal Analysis

Autores
Pourvahab, M; Mousavirad, SJ; Lashgari, F; Monteiro, A; Shafafi, K; Felizardo, V; Pais, S;

Publicação
Studies in Computational Intelligence

Abstract
In the study, a new method for analyzing Electrocardiogram (ECG) signals is suggested, which is vital for detecting and treating heart diseases. The technique focuses on improving ECG signal classification, particularly in identifying different heart conditions like arrhythmias and myocardial infarctions. An enhanced version of the differential evolution (DE) algorithm integrated with neural networks is leveraged to classify these signals effectively. The process starts with preprocessing and extracting key features from ECG signals. These features are then processed by a multi-layer perceptron (MLP), a common neural network for ECG analysis. However, traditional MLP training methods have limitations, such as getting trapped in suboptimal solutions. To overcome this, an advanced DE algorithm is used, incorporating a partition-based strategy, opposition-based learning, and local search mechanisms. This improved DE algorithm optimizes the MLP by fine-tuning its weights and biases, using them as starting points for further refinement by the Gradient Descent with Momentum (GDM) local search algorithm. Extensive experiments demonstrate that this novel training approach yields better results than the traditional method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

2024

Traffic and Obstacle-Aware UAV Positioning in Urban Environments Using Reinforcement Learning

Autores
Shafafi, K; Ricardo, M; Campos, R;

Publicação
IEEE ACCESS

Abstract
Unmanned Aerial Vehicles (UAVs) are suited as cost-effective and adaptable platforms for carrying Wi-Fi Access Points (APs) and cellular Base Stations (BSs). Implementing aerial networks in disaster management scenarios and crowded areas can effectively enhance Quality of Service (QoS). Maintaining Line-of-Sight (LoS) in such environments, especially at higher frequencies, is crucial for ensuring reliable communication networks with high capacity, particularly in environments with obstacles. The main contribution of this paper is a traffic- and obstacle-aware UAV positioning algorithm named Reinforcement Learning-based Traffic and Obstacle-aware Positioning Algorithm (RLTOPA), for such environments. RLTOPA determines the optimal position of the UAV by considering the positions of ground users, the coordinates of obstacles, and the traffic demands of users. This positioning aims to maximize QoS in terms of throughput by ensuring optimal LoS between ground users and the UAV. The network performance of the proposed solution, characterized in terms of mean delay and throughput, was evaluated using the ns-3 simulator. The results show up to 95% improvement in aggregate throughput and 71% in delay without compromising fairness.

2023

UAV-Assisted Wireless Communications: An Experimental Analysis of A2G and G2A Channels

Autores
Shafafi, K; Almeida, EN; Coelho, A; Fontes, H; Ricardo, M; Campos, R;

Publicação
Simulation Tools and Techniques - 15th EAI International Conference, SIMUtools 2023, Seville, Spain, December 14-15, 2023, Proceedings

Abstract
Unmanned Aerial Vehicles (UAVs) offer promising potential as communications node carriers, providing on-demand wireless connectivity to users. While existing literature presents various wireless channel models, it often overlooks the impact of UAV heading. This paper provides an experimental characterization of the Air-to-Ground (A2G) and Ground-to-Air (G2A) wireless channels in an open environment with no obstacles nor interference, considering the distance and the UAV heading. We analyze the received signal strength indicator and the TCP throughput between a ground user and a UAV, covering distances between 50 m and 500 m, and considering different UAV headings. Additionally, we characterize the antenna’s radiation pattern based on UAV headings. The paper provides valuable perspectives on the capabilities of UAVs in offering on-demand and dynamic wireless connectivity, as well as highlights the significance of considering UAV heading and antenna configurations in real-world scenarios.