Muhammad Shehab

Dottorato in Ingegneria Elettrica, Elettronica E Delle Comunicazioni , 36^o ciclo (2020-2023)

Dottorato concluso nel 2024

Profilo

Argomento di ricerca

Sulle Applicazioni dell'Apprendimento Automatico nelle Architetture IRS NOMA per 5G e Oltre

Interessi di ricerca

Big Data, Machine Learning, Neural Networks and Data Science

Communication and Computer Networks

Radio frequency and microwave electronics

Biografia

Muhammad Shehab, an IEEE member since 2019, was born on April 20, 1985, in Ra's-Beirut, a district within Beirut city, Lebanon. He earned his Bachelor of Engineering (B.E.) and Master of Electrical/Communication and Electronics Engineering (M.E.) degrees from Beirut Arab University (BAU) in 2007 and 2011, respectively. Later, he joined the Faculty of Electrical Engineering, Qatar University (QU), Qatar, in 2018, and the Department of Electronics and Telecommunications, Politecnico di Torino (POLITO), to continue his study to towards his Doctor of Philosophy (Ph.D.) degree. in the field of Wireless Communication. In 2013, he joined the Department of Information Technology, Community College of Qatar (CCQ), as a Network Engineer Lecturer till date. His research interests span a wide array of subjects, including 5G and 6G Networks, Non-orthogonal multiple access (NOMA), Intelligent reflecting surface (IRS), mmWave, THz, Massive MIMO, Beamforming, Machine learning (ML), Deep reinforcement learning (DRL), Long Range (LoRA), Satellite communication, and sustainability in 5G networks and smart cities. He has actively collaborated with researchers in Italy, Qatar, and internationally.

Premi e riconoscimenti

Qatar Foundation - Researchers Exchange and Mobility Program (REMP) - The award was withdrawn due to COVID-19. (2023)

Pubblicazioni

Pubblicazioni durante il dottorato Vedi tutte le pubblicazioni su Porto@Iris

Shehab, Muhammad; Elsayed, Mohamed; Almohamad, Abdullateef; Badawy, Ahmed; Khattab, ... (2024)
Terahertz Multiple Access: A Deep Reinforcement Learning Controlled Multihop IRS Topology. In: IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY, vol. 5, pp. 1072-1087. ISSN 2644-125X
Contributo su Rivista
Shehab, Muhammad (2024)
On Applications of Machine Learning In IRS NOMA Architectures For 5G and Beyond. relatore: TRINCHERO, Daniele; , 36. XXXVI Ciclo, P.: 134
Doctoral Thesis
Shehab, Muhammad; Badawy, Ahmed; Elsayed, Mohamed; Khattab, Tamer; Trinchero, Daniele (2023)
DDPG Performance in THz Communications over Cascaded RISs: A Machine Learning Solution to the Over-Determined System. In: 2023 International Wireless Communications and Mobile Computing (IWCMC), Marrakesh, Morocco, 19-23 June 2023, pp. 210-215. ISBN: 979-8-3503-3339-8
Contributo in Atti di Convegno (Proceeding)
Shehab, Muhammad; Kassem, I.; Kutty, A. A.; Kucukvar, M.; Onat, N; Khattab, T. (2022)
5G Networks Towards Smart and Sustainable Cities: A Review of Recent Developments, Applications and Future Perspectives. In: IEEE ACCESS, vol. 10, pp. 2987-3006. ISSN 2169-3536
Contributo su Rivista
Shehab, Muhammad; Trinchero, Daniele (2022)
Deep Reinforcement Learning Powered IRS-Assisted Downlink NOMA. In: IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY, vol. 3, pp. 729-739. ISSN 2644-125X
Contributo su Rivista
Shehab, Muhammad; Khattab, Tamer; Kucukvar, Murat; Trinchero, Daniele (2022)
The Role of 5G/6G Networks in Building Sustainable and Energy-Efficient Smart Cities. In: 2022 IEEE 7th International Energy Conference (ENERGYCON), Riga, Latvia, 09-12 May 2022, pp. 1-7. ISBN: 978-1-6654-7982-0
Contributo in Atti di Convegno (Proceeding)

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Muhammad Shehab

Dottorato in Ingegneria Elettrica, Elettronica E Delle Comunicazioni , 36o ciclo (2020-2023)

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