Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Serial concatenation of interleaved codes: performance analysis, design, and iterative decoding
1998886 citationsSergio Benedetto, D. Divsalar et al.profile →
Unveiling turbo codes: some results on parallel concatenated coding schemes
1996781 citationsSergio Benedetto, G. Montorsiprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of G. Montorsi'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 G. Montorsi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites G. Montorsi more than expected).
This network shows the impact of papers produced by G. Montorsi. 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 G. Montorsi. The network helps show where G. Montorsi may publish in the future.
Co-authorship network of co-authors of G. Montorsi
This figure shows the co-authorship network connecting the top 25 collaborators of G. Montorsi.
A scholar is included among the top collaborators of G. Montorsi 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 G. Montorsi. G. Montorsi is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Vanelli‐Coralli, Alessandro, Alessandro Guidotti, Tommaso Foggi, Giulio Colavolpe, & G. Montorsi. (2020). 5G and Beyond 5G Non-Terrestrial Networks: trends and research challenges. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 163–169.55 indexed citations
4.
Ugolini, Alessandro, G. Montorsi, & Giulio Colavolpe. (2018). Next Generation High-Rate Telemetry. IEEE Journal on Selected Areas in Communications. 36(2). 327–337.5 indexed citations
Tarable, Alberto & G. Montorsi. (2009). Multilayer spatial multiplexing in next-generation WiMAX. PORTO Publications Open Repository TOrino (Politecnico di Torino).
10.
Benedetto, Sergio, et al.. (2006). Design issues on the parallel implementation of versatile, highspeed iterative decoders. 1–10.1 indexed citations
Berrou, Claude, Catherine Douillard, G. Gallinaro, et al.. (2003). High Speed Modem Concepts and Demonstrator for Adaptive Coding and Modulation with High Order in Satellite Applications. PORTO Publications Open Repository TOrino (Politecnico di Torino).4 indexed citations
Vassallo, E., et al.. (1998). Improving rosetta's return-link margins. PORTO Publications Open Repository TOrino (Politecnico di Torino).1 indexed citations
Benedetto, Sergio, Roberto Garello, & G. Montorsi. (1997). The Trellis Complexity of Turbo Codes. PORTO Publications Open Repository TOrino (Politecnico di Torino). 60–65.6 indexed citations
19.
Benedetto, Sergio, G. Montorsi, D. Divsalar, & F. Pollara. (1996). Soft-Output Decoding Algorithms in Iterative Decoding of Turbo Codes. NASA Technical Reports Server (NASA). 124. 63–87.103 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.