Marco Levorato

145 papers receiving 2.5k citations

Hit Papers

Edge Learning for B5G Networks With Distributed Signal Pr...202220262023202420232022100200300

Peers

Marco Levorato
Comparison fields: 5 of 104
  • Computer Networks and Communications 1.4k
  • Electrical and Electronic Engineering 1.3k
  • Artificial Intelligence 425
  • Computer Vision and Pattern Recognition 402
  • Aerospace Engineering 296
Replace Muhammad Awais Javed with:
Muhammad Awais Javed Pakistan
Gunasekaran Raja India
Dan Wu China
Jianshan Zhou China
Ghulam Abbas Pakistan
Elias Yaacoub Qatar
Abderrahmane Lakas United Arab Emirates
Penglin Dai China
Kai Lin China
Dirk Pesch Ireland
Marco Levorato relative to Muhammad Awais Javed Pakistan Muhammad Awais Javed's profile →
Citations per field
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Citations per year

Countries citing papers authored by Marco Levorato

Since Specialization
Citations

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

Fields of papers citing papers by Marco Levorato

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Marco Levorato

This figure shows the co-authorship network connecting the top 25 collaborators of Marco Levorato. A scholar is included among the top collaborators of Marco Levorato 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 Marco Levorato. Marco Levorato 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
#WorkIndexed citations
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Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensingbreakdown →
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7 10
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Work-in-progress: driving behavior modeling and estimation for battery optimization in electric vehicles
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18 212
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On the Performance of Access Strategies for MIMO Ad Hoc Networks.
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Some Issues Concerning MAC Design in Ad Hoc Networks with MIMO Communications
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About Marco Levorato

Marco Levorato is a scholar working on Computer Networks and Communications, Electrical and Electronic Engineering and Computer Vision and Pattern Recognition, having authored 160 papers that have together received 2.6k indexed citations. Recurring topics across this work include Cooperative Communication and Network Coding (38 papers), Advanced MIMO Systems Optimization (33 papers) and IoT and Edge/Fog Computing (32 papers). The work is most often cited by research in Computer Networks and Communications (1.4k citations), Electrical and Electronic Engineering (1.3k citations) and Computer Vision and Pattern Recognition (402 citations). Marco Levorato has collaborated with scholars based in United States, Italy and Finland. Frequent co-authors include Michele Zorzi, Urbashi Mitra, Yoshitomo Matsubara, Andrea Goldsmith, Wei Xu, Derrick Wing Kwan Ng, Mérouane Debbah, Yonina C. Eldar, Zhaohui Yang and Sabur Baidya. Their work appears in journals such as IEEE Transactions on Information Theory, IEEE Transactions on Signal Processing and IEEE Access.

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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