Medhat Elsayed

580 total citations
38 papers, 369 citations indexed

About

Medhat Elsayed is a scholar working on Electrical and Electronic Engineering, Computer Networks and Communications and Biomedical Engineering. According to data from OpenAlex, Medhat Elsayed has authored 38 papers receiving a total of 369 indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Electrical and Electronic Engineering, 22 papers in Computer Networks and Communications and 6 papers in Biomedical Engineering. Recurrent topics in Medhat Elsayed's work include Advanced MIMO Systems Optimization (22 papers), Software-Defined Networks and 5G (8 papers) and Advanced Wireless Communication Technologies (8 papers). Medhat Elsayed is often cited by papers focused on Advanced MIMO Systems Optimization (22 papers), Software-Defined Networks and 5G (8 papers) and Advanced Wireless Communication Technologies (8 papers). Medhat Elsayed collaborates with scholars based in Canada, Sweden and United States. Medhat Elsayed's co-authors include Melike Erol‐Kantarci, Hao Zhou, Halim Yanıkömeroğlu, Jie Li, Lei Wu, Burak Kantarcı, Akram Bin Sediq, Gary Boudreau, Hatem Abou-Zeid and Ramy Atawia and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and IEEE Communications Magazine.

In The Last Decade

Medhat Elsayed

32 papers receiving 360 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Medhat Elsayed Canada	12	257	188	44	37	36	38	369
Ruediger Kays Germany	11	343 1.3×	280 1.5×	54 1.2×	34 0.9×	18 0.5×	83	457
Alexander Paramonov Russia	10	125 0.5×	189 1.0×	29 0.7×	18 0.5×	39 1.1×	56	268
Akram A. Almohammedi Malaysia	12	275 1.1×	172 0.9×	16 0.4×	29 0.8×	45 1.3×	34	391
Xiaoting Ma China	9	210 0.8×	187 1.0×	31 0.7×	16 0.4×	38 1.1×	22	334
Laila Nassef Saudi Arabia	9	261 1.0×	282 1.5×	126 2.9×	24 0.6×	20 0.6×	22	451
Dongdai Zhou China	9	186 0.7×	110 0.6×	34 0.8×	15 0.4×	51 1.4×	23	297
Abbas Mirzaei Iran	5	229 0.9×	217 1.2×	26 0.6×	14 0.4×	49 1.4×	10	385
Vesal Hakami Iran	12	186 0.7×	259 1.4×	24 0.5×	34 0.9×	11 0.3×	37	362
Charalambos Sergiou Cyprus	13	243 0.9×	514 2.7×	17 0.4×	28 0.8×	19 0.5×	30	574
Jordi Serra Spain	11	190 0.7×	262 1.4×	24 0.5×	22 0.6×	55 1.5×	23	391

Countries citing papers authored by Medhat Elsayed

Since Specialization

Citations

This map shows the geographic impact of Medhat Elsayed's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Medhat Elsayed with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Medhat Elsayed more than expected).

Fields of papers citing papers by Medhat Elsayed

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Medhat Elsayed. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Medhat Elsayed. The network helps show where Medhat Elsayed may publish in the future.

Co-authorship network of co-authors of Medhat Elsayed

This figure shows the co-authorship network connecting the top 25 collaborators of Medhat Elsayed. A scholar is included among the top collaborators of Medhat Elsayed based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Medhat Elsayed. Medhat Elsayed is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Elsayed, Medhat, et al.. (2025). Harnessing the Power of LLMs, Informers and Decision Transformers for Intent-Driven RAN Management in 6G. IEEE Transactions on Network Science and Engineering. 13. 4187–4206.

Zhang, Han, et al.. (2024). Federated Learning with Dual Attention for Robust Modulation Classification under Attacks. 5238–5243. 3 indexed citations

Elsayed, Medhat, et al.. (2024). Transformer-Based Wireless Traffic Prediction and Network Optimization in O-RAN. 1–6. 10 indexed citations

Elsayed, Medhat, et al.. (2024). Self-Play Ensemble Q-learning enabled Resource Allocation for Network Slicing. 4528–4533.

Elsayed, Medhat, et al.. (2024). Machine Learning-Enabled Traffic Steering in O-RAN: A Case Study on Hierarchical Learning Approach. IEEE Communications Magazine. 63(1). 100–107. 1 indexed citations

Elsayed, Medhat, et al.. (2024). Energy-Efficient Carrier Aggregation in 5G Using Constrained Multi-Agent MDP. IEEE Transactions on Green Communications and Networking. 8(4). 1595–1606.

Zhou, Hao, et al.. (2024). Cooperative Hierarchical Deep Reinforcement Learning-Based Joint Sleep and Power Control in RIS-Aided Energy-Efficient RAN. IEEE Transactions on Cognitive Communications and Networking. 11(1). 489–504. 5 indexed citations

Zhou, Hao, et al.. (2024). Smart Jamming Attack and Mitigation on Deep Transfer Reinforcement Learning Enabled Resource Allocation for Network Slicing. SHILAP Revista de lepidopterología. 2. 1492–1508. 1 indexed citations

Elsayed, Medhat, et al.. (2024). Extended Reality (XR) Codec Adaptation in 5G using Multi-Agent Reinforcement Learning with Attention Action Selection. 1–6.

10.

Zhou, Hao, et al.. (2023). Beam Selection for Energy-Efficient mmWave Network Using Advantage Actor Critic Learning. 5285–5290. 9 indexed citations

11.

Zhou, Hao, et al.. (2023). Traffic Steering for 5G Multi-RAT Deployments using Deep Reinforcement Learning. 164–169. 13 indexed citations

12.

Zhou, Hao, et al.. (2023). Split Learning for Sensing-Aided Single and Multi-Level Beam Selection in Multi-Vendor RAN. 6652–6657. 1 indexed citations

13.

Zhou, Hao, et al.. (2023). Intent-driven Intelligent Control and Orchestration in O-RAN Via Hierarchical Reinforcement Learning. 55–61. 12 indexed citations

14.

Zhou, Hao, et al.. (2022). Hierarchical Reinforcement Learning for RIS-Assisted Energy-Efficient RAN. GLOBECOM 2022 - 2022 IEEE Global Communications Conference. 3326–3331. 9 indexed citations

15.

Elsayed, Medhat, Hatem Abou-Zeid, Ramy Atawia, et al.. (2021). Carrier Aggregation With Optimized UE Power Consumption in 5G. IEEE Networking Letters. 3(2). 61–65. 9 indexed citations

16.

Elsayed, Medhat, Hatem Abou-Zeid, Ramy Atawia, et al.. (2021). QoS-Aware Joint Component Carrier Selection and Resource Allocation for Carrier Aggregation in 5G. 1–6. 16 indexed citations

17.

Zhou, Hao, Medhat Elsayed, & Melike Erol‐Kantarci. (2021). RAN Resource Slicing in 5G Using Multi-Agent Correlated Q-Learning. 1179–1184. 40 indexed citations

18.

Elsayed, Medhat & Melike Erol‐Kantarci. (2019). AI-Enabled Radio Resource Allocation in 5G for URLLC and eMBB Users. 590–595. 38 indexed citations

19.

Elsayed, Medhat & Melike Erol‐Kantarci. (2019). Reinforcement Learning-Based Joint Power and Resource Allocation for URLLC in 5G. 1–6. 20 indexed citations

20.

Elsayed, Medhat & Melike Erol‐Kantarci. (2018). Learning-Based Resource Allocation for Data-Intensive and Immersive Tactile Applications. 10. 278–283. 4 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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