Students: Mihir Laghate

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Mihir Laghate is a PhD Candidate (started Fall '12) in Electrical Engineering at the University of California, Los Angeles (UCLA). He is currently working with Prof. Danijela Čabrić on extending radio scene analysis methods to learn higher-layer information about the primary network such as the channel access schemes employed and the network topology. He has also worked on countering statistical attacks in cooperative spectrum sensing.

His latest CV can be downloaded here: CV



  • Received Best-in-Session-Presentation award at IEEE Infocom 2017
  • Received NSF student travel grant to attend IEEE DySPAN 2017
  • Ranked first in the Signals and Systems stream of the UCLA EE Ph.D. examination in 2014
  • Recipient of Guru Krupa Fellowship from the Guru Krupa Foundation
  • Recipient of Department Fellowship from the Electrical Engineering Department, UCLA


Dynamic spectrum access by learning primary network topology

The goal is to learn and utilize higher-layer information about the primary network using cooperation in the cognitive radio network. This includes primary network topology, channel access schemes, and traffic statistics.

DARPA-CLASIC: Cognitive radio Low-energy signal Analysis Sensor ICs

  • Developed methods to classify the primary network's channel access scheme as TDMA, CDMA, OFDMA, or contention-based by observing the signals received at a single wideband cognitive radio. Currently extending work to classify FHSS and FDMA.
  • Implementing modulation type and level classification methods on NI Ettus USRPs using GNURadio, C++, and Python

IARPA-RiM4S: Reliable Inference with Missing, Masked, Malfunctioning or Malicious Sensors

  • Developed methods to identify and counteract the effect of malicious cognitive radios participating in cooperative spectrum sensing
  • Identification was proposed by learning the structure of a Bayesian network representation of the sensor reports while spectrum sensing was proposed using a loopy belief propagation algorithm.



  • M. Laghate and D. Cabric, "Learning Wireless Networks' Topologies Using Asymmetric Granger Causality," submitted to IEEE Journal of Selected Topics in Signal Processing, July 2017.
  • M. Laghate and D. Cabric, "Cooperatively Learning Footprints of Multiple Incumbent Transmitters by Using Cognitive Radio Networks", accepted to IEEE Transactions on Cognitive Communications and Networking, May 2017. [ Preprint ] [ Presentation at ARR ]
  • M. Laghate, P. Urriza, and D. Cabric, "Channel Access Method Classification For Cognitive Radio Applications", submitted to IEEE Wireless Communications Letters, April 2017.
  • M. Laghate and D. Cabric, Cooperative Spectrum Sensing in the Presence of Correlated and Malicious Cognitive Radios, IEEE Transactions on Communications, vol. 63, no. 12, pp. 4666–4681, Dec. 2015. [ Paper ]

Conferences and Workshops

Master's Thesis

  • M. Laghate, "Downlink Stochastic Power Control for Cellular Networks with Femto Base Stations", M.Tech. Thesis, Indian Institute of Technology Bombay, 2012
  • Adviser: Prof. Dr. Abhay Karandikar

Contact Information

  • Email:smvlaghate-at-ucla.eduuu
  • Phone: (Lab) +1 310 206 9252
  • Profiles: