Mihir Laghate was a PhD student in Electrical Engineering at the University of California, Los Angeles (UCLA) from September 2012 to June 2017. He is currently working at Qualcomm Technology Inc. While at UCLA, he worked 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 also worked on countering statistical attacks in cooperative spectrum sensing as well as implementing receivers and blind modulation classifiers on Universal Software Defined Radios (USRPs).

His latest CV can be downloaded here: CV

Education

• Ph.D. Electrical Engineering, University of California, Los Angeles, 2017
• M.Tech. Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, 2012
• B.Tech. Electrical Engineering with Minor in Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, 2012

Awards

• 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

Projects

Dynamic spectrum access by learning primary network topologyThe 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.

Publications

Journal

• M. Laghate and D. Cabric, Learning Wireless Networks’ Topologies Using Asymmetric Granger Causality, IEEE Journal of Selected Topics in Signal Processing, vol. 12, no. 1, pp. 233-247, Feb. 2018. [ Preprint] [ Code]
• M. Laghate, P. Urriza, and D. Cabric, Channel Access Method Classification For Cognitive Radio Applications, IEEE Wireless Communications Letters, vol. 7, no. 1, pp. 70-73, Feb. 2018. [ Long Version]
• M. Laghate and D. Cabric, Cooperatively Learning Footprints of Multiple Incumbent Transmitters by Using Cognitive Radio Networks, IEEE Transactions on Cognitive Communications and Networking, vol. 3, no. 3, pp 282-297, Sept. 2017. [ Preprint ] [ Presentation at ARR ]
• 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

• X Wang, S. Chaudhari, M. Laghate, and D. Cabric, “Wideband Spectrum Sensing Measurement Results using Tunable Front-End and FPGA Implementation”, presented at Asilomar Conference on Signals, Systems, and Computers, Nov. 2017.
• T. Vermeulen, M. Laghate, G. Hattab, D. Cabric, and S. Pollin, Towards Instantaneous Collision and Interference Detection using In-Band Full Duplex, in IEEE INFOCOM, May 2017, pp. 1-9. (Received Best-in-Session-Presentation Award) [ Presentation ]
• M. Laghate, S. Chaudhari, and D. Cabric, USRP N210 Demonstration of Wideband Sensing and Blind Hierarchical Modulation Classification, in IEEE DySPAN Workshop: Battle of the ModRecs, Mar. 2017, pp. 1-3. [ Paper ]
• M. Laghate and D. Cabric, Demonstrating Spectrum Sensing in Colored Noise for Signals with Partial Spectral Overlap, in IEEE DySPAN, Mar. 2017, pp. 1-2. [ Paper ]
• M. Laghate and D. Cabric, Using Multiple Power Spectrum Measurements to Sense Signals with Partial Spectral Overlap, in IEEE DySPAN, Mar. 2017, pp. 1-8. [ Paper ] [ Presentation ]
• T. Vermeulen, M. Laghate, G. Hattab, B. van Liempd, D. Cabric, and S. Pollin, Nearly instantaneous collision and interference detection using in-band full duplex, in IEEE DySPAN, Mar. 2017, pp. 1–2.
• M. Laghate and D. Cabric, Using the Time Dimension to Sense Signals with Partial Spectral Overlap, in IEEE Globecom’16, Dec. 2016, pp. 1-7. [ Paper ] [ Presentation ]
• L. Du, Y. Chen, M. Laghate, C.-H. Liu, and D. Cabric, Improved Eigenvalue-based Spectrum Sensing via Sensor Signal Overlapping, in 2016 8th IEEE International Conference on Communication Software and Networks (ICCSN), Beijing, 2016, pp. 122-126.
• M. Laghate and D. Cabric, Identifying the Presence and Footprints of Multiple Incumbent Transmitters, in 2015 Asilomar Conference on Signals, Systems, and Computers, Nov. 2015, pp. 146-150. [ Paper ]
• L. Du, C.-H. Liu, M. Laghate, and D. Cabric, Sequential Detection of Number of Primary Users in Cognitive Radio Networks, in 2015 Asilomar Conference on Signals, Systems, and Computers, Nov. 2015, pp. 151-154.
• M. Laghate and D. Cabric, Using Belief Propagation to Counter Correlated Reports in Cooperative Spectrum Sensing, in 2014 IEEE Global Communications Conference (GLOBECOM), Dec. 2014, pp.1023-1028. [ Paper ] [ Presentation ]
• M. Laghate, C.-H. Huang, C.-K. Yu, L. Dolecek, and D. Cabric, Identifying Statistical Mimicry Attacks in Distributed Spectrum Sensing, in 2013 Asilomar Conference on Signals, Systems and Computers, Nov. 2013, pp.1478-1482.
• C.-K. Yu, M. Laghate, A. Sayed, and D. Cabric, On the Effects of Colluded Statistical Attacks in Cooperative Spectrum Sensing, in 2013 IEEE 14th Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Jun. 2013, pp.275-279.

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:
  • LinkedIn
  • Facebook