Publications (as of 10 March 2026)

26 Journal papers - 17 conference papers - 1 Nokia patent - 3 Thesis books

Google scholar: https://scholar.google.com/citations?user=PtIlCGYAAAAJ&hl=en

Research Gate: https://www.researchgate.net/profile/Mohammad-Shehab-4

Patents:

1. M. Shehab, F. Qasmi, M. Asad Ullah, and H. Alves, “Optimum resource block and power allocation for ultra-reliable transmission of heterogeneous packets,” Nokia Invention, patent: Finland (NP) - Jan 2020 - 20205049

Journal Articles:

1. O. Rosabal, A. Azarbahram, M. Ashraf, M. Shehab, A. Khattak, O. López, M-S. Alouini, “Power from the Space: Coordinated Charging for Future Wireless Systems” submitted to IEEE Communications Magazine 

2. M. Shehab, D. Gunduz, and M-S. Alouini, “A Semantic Generative Model for Underwater to Satellite Image Transmission”, to be submitted to IEEE Trasactions on Machine Learning in Communications (Tentative)

3. Cian Blake, Eslam Eldeeb, M. Shehab, et al. Semantic Communication Frameworks for Autonomous Vehicles. TechRxiv. February 14, 2025 (10.36227/techrxiv.173950918.82257989/v1), accepted in IEEE IoT Magazine (May 2026).

4. Mohamed Afouene Melki, M. Shehab, and Mohamed-Slim Alouini, "Underwater Trajectory Planning for Simultaneous Acoustic Power Transfer and Age Minimization", accepted in IEEE Internet of Things Journal.

5. M. Shehab, Mustafa Kishk, Maurilio Matracia, Mehdi Bennis, and Mohamed-Slim Alouini, "Five Key Enablers for Communication during and after Disasters", accepted in IEEE Wireless Communications Magazine (Apr 2026) https://doi.org/10.48550/arXiv.2409.06822

6. Yaya Etiabi, Eslam Eldeeb, M. Shehab, Wafa Njima, Hirley Alves, Mohamed-Slim Alouini, "MetaGraphLoc: A Graph-based Meta-learning Scheme for Indoor Localization via Sensor Fusion", submitted to IEEE Transactions on Wireless Communication (under review).

7. Eslam Eldeeb, M. Shehab, Hirley Alves, and Mohamed-Slim Alouini, "Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classification", in IEEE Transactions on Machine Learning in Communications and Networking, vol. 3, pp. 491-501, 2025, doi: 10.1109/TMLCN.2025.3557734.

8. M. V. da Silva, E. Eldeeb, M. Shehab, H. Alves and R. D. Souza, "Distributed Learning Methodologies for Massive Machine Type Communication," in IEEE Internet of Things Magazine, vol. 8, no. 1, pp. 102-108, January 2025.

9. E. Eldeeb, H . O simone, M. Shehab, and H. Alves, "Conservative and Risk-Aware Offline Multi-Agent Reinforcement Learning for Digital Twins", submitted to IEEE Transactions on Cognitive Communications and Networking. Available: https://arxiv.org/pdf/2402.08421.pdf

10. E. Eldeeb, M. Shehab and H. Alves, "A Multi-Task Oriented Semantic Communication Framework for Autonomous Vehicles," in IEEE Wireless Communications Letters, vol. 13, no. 12, pp. 3469-3473, Dec. 2024, doi: 10.1109/LWC.2024.3472211.

11. O. López, N. Mahmood, M. Shehab; et al. (2023): "Statistical  Tools  and  Methodologies  for  URLLC-  A  Tutorial". submitted to Proceedings of IEEE. TechRxiv. Preprint. https://doi.org/10.36227/techrxiv.21436332.v1

12. E. Eldeeb, M. Shehab, H. Alves (2022), “Traffic Learning and Proactive UAV Trajectory Planning for Data Uplink in Markovian IoT Models.  Submitted to IEEE Transactions on Wireless Communications". TechRxiv. Preprint. https://doi.org/10.36227/techrxiv.21842319.v1

13. E. Eldeeb, J. M. d. S. Sant'Ana, D. E. Pérez, M. Shehab, N. H. Mahmood and H. Alves, "Multi-UAV Path Learning for Age and Power Optimization in IoT with UAV Battery Recharge", in IEEE Transactions on Vehicular Technology, 2022, doi: 10.1109/TVT.2022.3222092.

14. E. Eldeeb, M. Shehab, A. E. Kalør, P. Popovski, and H. Alves, “Traffic Prediction and Fast Uplink for Hidden Markov IoT Models,” submitted   to IEEE Internet of Things Journal, January 2022.

15. F. Qasmi, M. Shehab, H. Alves, M. Latva-aho, “Effective Energy Efficiency and Statistical QoS Provisioning under Markovian Arrivals and Finite Blocklength Regime” IEEE Internet of Things Journal, Feb 2022.

16. Osmel Rosabal, Onel López, Dian Perez, M. Shehab, Henrique Hilleshein, and H. Alves, “Minimization of the Worst-Case Average Energy Consumption in UAV-Assisted IoT Networks”, IEEE Internet of Things Journal, Jan 2022.

17. E. Eldeeb, M. Shehab, and H. Alves, "A Learning-Based Fast Uplink Grant for Massive IoT via Support Vector Machines and Long Short-Term Memory," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2021.3101978.

18. M. Shehab, H. Alves, E. Jorswieck, E. Dosti, M. Latva-aho, “Effective Energy Efficiency of Ultra-reliable Low Latency Communication”, IEEE Internet of Things journal, Jan 2021

19. E. Dosti, M. Shehab, H. Alves, M. Latva-aho, “Ultra Reliable Communication via Optimum Power Allocation for HARQ Retransmission Schemes”, IEEE access, 2020.

20. M. Shehab, Hirley Alves, and Matti Latve-aho, “Effective Capacity and Power Allocation for Machine-Type Communication” IEEE Transactions on Vehicular Technology, 2019.

21. E. Dosti, M. Shehab, H. Alves, and M. Latva-aho, “On the performance of non-orthogonal multiple access in the finite block-length regime”, in ELSEVIER ad-hoc networks (2018).

22. M. Shehab, E. Dosti, H. Alves, M. Latva-aho, “Statistical QoS provisioning for MTC networks under finite blocklength”, published in EURASIP JWCN (2018).

Conference papers:

1. A. Radwan, M. Shehab, and M-S. Alouini, "TinyML NLP Scheme for Semantic Wireless Sentiment Classification with Privacy Preservation", in EUCNC 2025, Poznan, Poland.

2. A. Radwan, M. Shehab, and M-S. Alouini, "TinyML NLP Scheme for Semantic Wireless Sentiment Classification with Privacy Preservation", in EUCNC 2025.

3. A. Elbakary, C. B. Issaid, M. Shehab, K. Seddik, T. ElBatt and M. Bennis, "Fed-Sophia: A Communication-Efficient Second-Order Federated Learning Algorithm," ICC 2024 - IEEE International Conference on Communications, Denver, CO, USA, 2024, pp. 950-955, doi: 10.1109/ICC51166.2024.10622862.

4. H. Issa, M. Shehab, H. Alves, " Meta-Learning Based Few Pilots Demodulation and Interference Cancellation For NOMA Uplink”, in EUCNC 2023.

5. E. Eldeeb, M. Shehab, H. Alves, " Age Minimization in Massive IoT via UAV Swarm: A Multi-agent Reinforcement Learning Approach” in PIMRC 2023.

6. E. Eldeeb, D. Pérez, J. Sant’Ana, M. Shehab, N. Mahmood, H. Alves, and M. Latva-aho, " A Learning-Based Trajectory Planning of Multiple UAVs for AoI Minimization in IoT Networks," in EUCNC 2022.

7. M. Shehab, A. K. Hagelskjær, A. E. Kalør, P. Popovski, and H. Alves, “Traffic Prediction Based Fast Uplink Grant for Massive IoT,” in PIMRC 2020.

8. N. Mahmood, H. Alves, O. López, M. Shehab, D. Osorio, M. Latva-aho, “Six Key Features of Machine Type Communication in 6G”, published in 6G summit, 2020, Finland.

9. F. Qasmi, M. Shehab, H. Alves, M. Latva-aho, “Fixed Rate Statistical QoS Provisioning for Markovian Sources in Machine Type Communication”, ISWCS 2019.

10. F. Qasmi, M. Shehab, H. Alves, M. Latva-aho, “Optimum Transmission Rate in Fading Channels with Markovian Sources and QoS Constraints” ISWCS 2018.

11. M. Shehab, H. Alves, and M. Latva-aho, “Ultra Reliable Communication via Opportunistic Transmission with Successive Interference Cancellation”, WCNC 2018.

12. M. Shehab, E. Dosti, H. Alves, M. Latva-aho, “On the Effective Energy Efficiency of Ultra-reliable Networks in the Finite Blocklength Regime”, ISWCS 2017 (won the best student paper award).

13. M. Shehab, E. Dosti, H. Alves, M. Latva-aho, “On the Effective Capacity of MTC Networks in the Finite Blocklength Regime”, EUCNC 2017 (nominated for the best student paper award).

14. E. Dosti, M. Shehab, H. Alves, M. Latva-aho, “Ultra Reliable Communication via CC-HARQ in Finite Block-Length”, EUCNC 2017, Oulu, Finland.

15. E. Dosti, M. Shehab, H. Alves, M. Latva-aho, “On the performance of non-orthogonal multiple access in the finite block-length regime”, BalkanCom 2017, Tirana, Albania, May 2017.

16. M. Shehab, E. Badran, and A. Zaki, “On The Performance Analysis of Symbol-Level Regressive Coding Technique via Variational Bayesian Expectation Maximization”, NRSC 2015, Egypt (nominated for the best paper award).

17. M. Shehab, E. Badran, and A. Zaki, “A Novel Coding Scheme for QAM Using Variational Bayesian Inference”, ISWTA 2014.

Thesis books:

1. M. Shehab, "Energy efficient QoS provisioning and resource allocation for machine type communication", Doctoral Thesis, University of Oulu, 2022.  available: http://jultika.oulu.fi/files/isbn9789526230467.pdf

2. M. Shehab, “Performance of delay constrained multi-user networks under block fading channels”, Master thesis, University of Oulu, 2017.

3. M. Shehab, “A Novel Symbol Level-Coding Scheme via Variational Bayesian Expectation Maximization with Error Analysis”, Master thesis, Arab Academy for Science and Technology, 2014.