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20112024
most citedMachine Learning-based Urban Canyon Path Loss Prediction using 28 GHz Manhattan Measurements

79 citations · 222 across the 14 of their papers we have counts for

collaborators

15 papers

eess.SY2024

Backscatter Measurements and Models for RF Sensing Applications in Cluttered Environments

Dmitry Chizhik, Jinfeng Du, Jakub Sapis +6

A statistical backscatter channel model for indoor clutter is developed for indoor RF sensing applications based on measurements. A narrowband 28 GHz sounder used a quazi-monostati…

cs.NI2024

Measured and Modeled Outdoor Indoor Coverage at 28 GHz into High Thermal Efficiency Buildings

Dmitry Chizhik, Jinfeng Du, Reinaldo Valenzuela +3

28 GHz outdoor-indoor coverage into modern office buildings with high thermal efficiency windows is found to be severely limited due to 46 dB median penetration loss at normal inci…

cs.NI2023

28 GHz NLOS Channel Measurements Revealing Low Path Loss and High Angular Spread in Container Ports

Andrea Bedin, Dmitry Chizhik, Jinfeng Du +4

This paper presents results from a comprehensive measurement campaign conducted at 28 GHz inside a container canyon within a commercial port environment. The measurements are perfo…

eess.SP2022★ 1 cited

Outdoor-to-Indoor 28 GHz Wireless Measurements in Manhattan: Path Loss, Environmental Effects, and 90% Coverage

Manav Kohli, Abhishek Adhikari, Gulnur Avci +12

Outdoor-to-indoor (OtI) signal propagation further challenges the already tight link budgets at millimeter-wave (mmWave). To gain insight into OtI mmWave scenarios at 28 GHz, we co…

eess.SP2022

Dense Urban Outdoor-Indoor Coverage from 3.5 to 28 GHz

Dipankar Shakya, Dmitry Chizhik, Jinfeng Du +2

In the US, people spend 87% of their time indoors and have an average of four connected devices per person (in 2020). As such, providing indoor coverage has always been a challenge…

cs.IT2022★ 79 cited

Machine Learning-based Urban Canyon Path Loss Prediction using 28 GHz Manhattan Measurements

Ankit Gupta, Jinfeng Du, Dmitry Chizhik +2

Large bandwidth at mm-wave is crucial for 5G and beyond but the high path loss (PL) requires highly accurate PL prediction for network planning and optimization. Statistical models…