Sofiane Kharbech

685 citations
20 papers · 475 · h-index 10

Impact in

Papers in

Sofiane Kharbech

20 papers receiving 459 citations

Peers

Sofiane Kharbech
Comparison fields: 5 of 49
  • Computer Vision and Pattern Recognition 285
  • Artificial Intelligence 182
  • Computational Theory and Mathematics 71
  • Signal Processing 45
  • Mathematical Physics 35
Replace Xiaoyang Chen with:
Xiaoyang Chen China
Weijie Tan China
Xiaoqiang Zhu China
Alaa M. Abbas Egypt
Xianxing Liu China
Mohamed Salah Azzaz Algeria
Jianeng Tang China
Juliano B. Lima Brazil
Hongyu Zhao China
Janier Arias-García Brazil
Sofiane Kharbech relative to Xiaoyang Chen China Xiaoyang Chen's profile →
Citations per field
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Citations per year

Countries citing papers authored by Sofiane Kharbech

Since Specialization
Citations

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

Fields of papers citing papers by Sofiane Kharbech

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sofiane Kharbech. 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 Sofiane Kharbech. The network helps show where Sofiane Kharbech may publish in the future.

Co-authors

The 25 scholars most cited alongside Sofiane Kharbech, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Sofiane Kharbech Line = papers co-authored together Sofiane Kharbech links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 2019203
2 202271
3 201637
4 201435
5 201924
6 201823
7 202114
8 202212
9 202111
10 20209
11 20227
12 20207
13 20237
14 20206
15 20203
16
Emulation of various radio access technologies for zero on site testing in the railway domain - the Emulradio4rail platforms
20202
17 20241
18 20251
19 20221
20 20221

About Sofiane Kharbech

Sofiane Kharbech is a scholar working on Electrical and Electronic Engineering, Signal Processing, Artificial Intelligence, Computer Networks and Communications and Computer Vision and Pattern Recognition, having authored 20 papers that have together received 475 indexed citations. Recurring topics across this work include Blind Source Separation Techniques (8 papers), Power Line Communications and Noise (6 papers), Millimeter-Wave Propagation and Modeling (5 papers), Wireless Signal Modulation Classification (5 papers), Advanced MIMO Systems Optimization (4 papers), Cognitive Radio Networks and Spectrum Sensing (3 papers), Chaos-based Image/Signal Encryption (3 papers) and Cellular Automata and Applications (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (285 citations), Artificial Intelligence (182 citations), Computational Theory and Mathematics (71 citations), Signal Processing (45 citations) and Mathematical Physics (35 citations). Sofiane Kharbech has collaborated with scholars based in France, Tunisia and Australia. Frequent co-authors include Wei Xiang, Muhammad Talha, Akram Belazi, Eric Pierre Simon, Iyad Dayoub, Ahmed A. Abd El‐Latif, Abdullah M. Iliyasu, Laurent Clavier, Kaïs Hassan and Ammar Bouallègue. Their work appears in journals such as IEEE Access, IEEE Wireless Communications Letters, IEEE Communications Letters, Journal of Information Security and Applications and IEEE Transactions on Vehicular Technology.

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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