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Shivan Mukherjee

dblp:320/8433 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 2 · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer networks
2 papers
Physical-layer communications · 100%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Physical-layer communications › radio propagation
millimeter-wave propagation
1.622025
Around-Corner and Over-Top 28 GHz Measurement in Manhattan: Path Loss and AoA for MU-MIMO · INFOCOM 2025
Outdoor-to-Indoor 28 GHz Wireless Measurements in Manhattan: Path Loss, Environmental Effects, and 90% Coverage · IEEE/ACM Trans. Netw. 2024
Physical-layer communications › channel modeling
path loss modeling
1.622025
Around-Corner and Over-Top 28 GHz Measurement in Manhattan: Path Loss and AoA for MU-MIMO · INFOCOM 2025
Outdoor-to-Indoor 28 GHz Wireless Measurements in Manhattan: Path Loss, Environmental Effects, and 90% Coverage · IEEE/ACM Trans. Netw. 2024
Physical-layer communications › signal processing for communications › array signal processing
direction-of-arrival estimation
0.912025
Around-Corner and Over-Top 28 GHz Measurement in Manhattan: Path Loss and AoA for MU-MIMO · INFOCOM 2025
Physical-layer communications
MIMO
0.912025
Around-Corner and Over-Top 28 GHz Measurement in Manhattan: Path Loss and AoA for MU-MIMO · INFOCOM 2025
Physical-layer communications › MIMO
multiuser MIMO
0.912025
Around-Corner and Over-Top 28 GHz Measurement in Manhattan: Path Loss and AoA for MU-MIMO · INFOCOM 2025
Physical-layer communications › channel modeling › channel characterization › channel measurement
millimeter-wave channel measurement
0.812024
Outdoor-to-Indoor 28 GHz Wireless Measurements in Manhattan: Path Loss, Environmental Effects, and 90% Coverage · IEEE/ACM Trans. Netw. 2024

Methods — techniques the papers use, named apart from their topics

channel measurement · 0.9measurement campaign · 0.8
YearPublicationVenuePosition
2025 Around-Corner and Over-Top 28 GHz Measurement in Manhattan: Path Loss and AoA for MU-MIMO
Abhishek Adhikari, Shivan Mukherjee, Aahan Mehta, Manav Kohli, Rodolfo Feick, Reinaldo A. Valenzuela, Dmitry Chizhik, Jinfeng Du, Gil Zussman
INFOCOM2
2024 Outdoor-to-Indoor 28 GHz Wireless Measurements in Manhattan: Path Loss, Environmental Effects, and 90% Coverage
abstract
Outdoor-to-indoor signal propagation poses significant challenges to millimeter-wave link budgets. To gain insight into outdoor-to-indoor millimeter-wave at 28GHz, we conducted an extensive measurement campaign consisting of over 2,200 link measurements in West Harlem, New York City, covering seven highly diverse buildings. A path loss model constructed over all measured links shows an average of 30dB excess loss over free space at distances beyond 50m. We find the type of glass to be the dominant factor in outdoor-to-indoor loss, with 20dB observed difference between grouped scenarios with low-and high-loss glass. Other factors such as the presence of scaffolding, tree foliage, or elevated subway tracks, as well as difference in floor height are also found to have a 5–10dB impact. We show that for urban buildings with high-loss glass, outdoor-to-indoor downlink capacity up to 400Mb/s is supported for 90% of indoor customer premises equipment by a base station up to 40m away. For buildings with low-loss glass, such as our case study covering multiple classrooms of a public school, downlink capacity over 2.8/1.4Gb/s is possible from a base station 57/133m away within line-of-sight. We expect these results to help inform the planning of millimeter-wave networks targeting outdoor-to-indoor deployments in dense urban environments, as well as provide insight into the development of scheduling and beam management algorithms.
Manav Kohli, Abhishek Adhikari, Gulnur Avci, Sienna Brent, Aditya Dash, Jared Moser, Sabbir Hossain, Igor Kadota, Carson Garland, Shivan Mukherjee, Rodolfo Feick, Dmitry Chizhik, Jinfeng Du, Reinaldo A. Valenzuela, Gil Zussman
IEEE/ACM Trans. Netw.10