Prabhat K. Upadhyay

2.6k total citations
125 papers, 2.0k citations indexed

About

Prabhat K. Upadhyay is a scholar working on Electrical and Electronic Engineering, Computer Networks and Communications and Aerospace Engineering. According to data from OpenAlex, Prabhat K. Upadhyay has authored 125 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 104 papers in Electrical and Electronic Engineering, 64 papers in Computer Networks and Communications and 46 papers in Aerospace Engineering. Recurrent topics in Prabhat K. Upadhyay's work include Cooperative Communication and Network Coding (56 papers), Advanced Wireless Communication Technologies (47 papers) and Advanced MIMO Systems Optimization (40 papers). Prabhat K. Upadhyay is often cited by papers focused on Cooperative Communication and Network Coding (56 papers), Advanced Wireless Communication Technologies (47 papers) and Advanced MIMO Systems Optimization (40 papers). Prabhat K. Upadhyay collaborates with scholars based in India, Brazil and South Africa. Prabhat K. Upadhyay's co-authors include Pankaj Sharma, Vinay Bankey, Vibhum Singh, Daniel Benevides da Costa, Shankar Prakriya, Petros S. Bithas, Αthanasios G. Kanatas, Sourabh Solanki, Suneel Yadav and Min Lin and has published in prestigious journals such as International Journal of Heat and Mass Transfer, IEEE Access and IEEE Transactions on Communications.

In The Last Decade

Prabhat K. Upadhyay

120 papers receiving 2.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Prabhat K. Upadhyay India 26 1.6k 986 875 110 96 125 2.0k
Haifan Yin China 16 1.7k 1.1× 538 0.5× 694 0.8× 40 0.4× 28 0.3× 52 1.9k
Angeliki Alexiou Greece 22 1.9k 1.2× 642 0.7× 686 0.8× 20 0.2× 241 2.5× 108 2.1k
Trinh Van Chien Vietnam 19 1.3k 0.8× 496 0.5× 485 0.6× 39 0.4× 62 0.6× 107 1.5k
Özlem Tuğfe Demir Türkiye 17 1.3k 0.8× 447 0.5× 438 0.5× 26 0.2× 101 1.1× 86 1.4k
Gui Zhou China 19 2.3k 1.5× 1.4k 1.4× 379 0.4× 33 0.3× 67 0.7× 52 2.7k
Nicolò Michelusi United States 17 915 0.6× 93 0.1× 505 0.6× 74 0.7× 151 1.6× 85 1.2k
Prabhat Kumar Sharma India 21 1.2k 0.8× 476 0.5× 404 0.5× 17 0.2× 154 1.6× 114 1.4k
Young‐Chai Ko South Korea 25 2.0k 1.2× 559 0.6× 1.3k 1.5× 18 0.2× 55 0.6× 188 2.2k
Wonjae Shin South Korea 20 1.6k 1.0× 432 0.4× 745 0.9× 19 0.2× 56 0.6× 155 1.8k

Countries citing papers authored by Prabhat K. Upadhyay

Since Specialization
Citations

This map shows the geographic impact of Prabhat K. Upadhyay'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 Prabhat K. Upadhyay with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Prabhat K. Upadhyay more than expected).

Fields of papers citing papers by Prabhat K. Upadhyay

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Prabhat K. Upadhyay. 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 Prabhat K. Upadhyay. The network helps show where Prabhat K. Upadhyay may publish in the future.

Co-authorship network of co-authors of Prabhat K. Upadhyay

This figure shows the co-authorship network connecting the top 25 collaborators of Prabhat K. Upadhyay. A scholar is included among the top collaborators of Prabhat K. Upadhyay 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 Prabhat K. Upadhyay. Prabhat K. Upadhyay is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Upadhyay, Prabhat K., et al.. (2025). Deep Learning-Enabled Secrecy Performance Analysis of UAV-Aided Reconfigurable Intelligent Surfaces With Non-Orthogonal Multiple Access. IEEE Transactions on Cognitive Communications and Networking. 11(6). 3797–3810. 1 indexed citations
2.
Moualeu, Jules M., et al.. (2024). On Secure Hybrid RF-FSO MIMO-NOMA Systems With Colluding and Non-Colluding Eavesdroppers. IEEE Wireless Communications Letters. 13(9). 2472–2476. 2 indexed citations
3.
Upadhyay, Prabhat K., et al.. (2023). Gaussian kernel quadrature Kalman filter. European Journal of Control. 71. 100805–100805. 5 indexed citations
4.
5.
Upadhyay, Prabhat K., et al.. (2023). Exploiting Deep Learning in the Performance Evaluation of EH-Based Coordinated Direct and Relay Transmission System With Cognitive NOMA. IEEE Communications Letters. 27(6). 1501–1505. 10 indexed citations
6.
Upadhyay, Prabhat K., et al.. (2022). Exploiting SWIPT-Enabled IoT-Based Cognitive Nonorthogonal Multiple Access With Coordinated Direct and Relay Transmission. IEEE Sensors Journal. 22(19). 18988–18999. 14 indexed citations
7.
Upadhyay, Prabhat K., et al.. (2022). Outage Performance with Deep Learning Analysis for UAV-Borne IRS Relaying NOMA Systems with Hardware Impairments. 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall). 1–7. 4 indexed citations
8.
Upadhyay, Prabhat K., et al.. (2022). On Anomalous Diffusion of Devices in Molecular Communication Systems. IEEE Transactions on Molecular Biological and Multi-Scale Communications. 8(3). 207–211. 2 indexed citations
9.
Singh, Vibhum, et al.. (2021). Energy Harvesting in Overlay Cognitive NOMA Systems With Hardware Impairments. IEEE Systems Journal. 16(2). 2648–2659. 28 indexed citations
10.
Singh, Vibhum, et al.. (2021). Performance Analysis of Energy Harvesting-Assisted Overlay Cognitive NOMA Systems With Incremental Relaying. IEEE Open Journal of the Communications Society. 2. 1558–1576. 30 indexed citations
11.
Sahu, Santosh Kumar, et al.. (2021). Experimental and numerical investigation of the thermal performance of impinging synthetic jets with different waveforms. Experimental Heat Transfer. 36(2). 121–142. 13 indexed citations
12.
Singh, Vibhum, Prabhat K. Upadhyay, & Min Lin. (2020). On the Performance of NOMA-Assisted Overlay Multiuser Cognitive Satellite-Terrestrial Networks. IEEE Wireless Communications Letters. 9(5). 638–642. 63 indexed citations
13.
Lin, Min, et al.. (2020). Outage Performance for Mixed FSO-RF Transmission in Satellite-Aerial- Terrestrial Networks. IEEE Photonics Technology Letters. 32(21). 1349–1352. 21 indexed citations
14.
15.
Sahu, Santosh Kumar, et al.. (2019). Experimental investigation on thermal characteristics of hot surface by synthetic jet impingement. Applied Thermal Engineering. 165. 114596–114596. 41 indexed citations
16.
Bankey, Vinay, Prabhat K. Upadhyay, Daniel Benevides da Costa, et al.. (2018). Performance Analysis of Multi-Antenna Multiuser Hybrid Satellite-Terrestrial Relay Systems for Mobile Services Delivery. IEEE Access. 6. 24729–24745. 58 indexed citations
17.
Solanki, Sourabh, et al.. (2018). Wireless Power Transfer in Two-Way AF Relaying with Maximal-Ratio Combining under Nakagami-m Fading. 169–173. 11 indexed citations
18.
Gurjar, Devendra S., et al.. (2018). Energy harvesting in hybrid two-way relaying with direct link under Nakagami-m fading. 1–6. 27 indexed citations
19.
Bithas, Petros S., Αthanasios G. Kanatas, Daniel Benevides da Costa, & Prabhat K. Upadhyay. (2017). Transmit antenna selection in vehicle-to-vehicle time-varying fading channels. 1–6. 6 indexed citations
20.
Gurjar, Devendra S. & Prabhat K. Upadhyay. (2016). Impact of channel estimation error on zero‐forcing‐based multiple‐input–multiple‐output two‐way relaying. IET Signal Processing. 10(3). 210–217. 6 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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