Shamik Sarkar

Hi! I am Shamik, I am a postdoc at UCLA working under the supervision of Dr. Danijela Cabric. I received my Ph.D. from the University of Utah in August 2021. I am interested in systems/protocols/algorithms research for wireless communication and networks, with a special emphasis on spectrum sharing. For more details about my research, please refer to my Google Scholar page. The best way to contact me is via email (shamiksarkar@g.ucla.edu).

Publications with CORES:

S. Sarkar, D. Guo and D. Cabric, "RadYOLOLet: Radar Detection and Parameter Estimation Using YOLO and WaveLet," in IEEE Transactions on Cognitive Communications and Networking
Machine LearningRadar
V. Boljanovic, S. Sarkar and D. Cabric, "Joint User Association and Beam Scheduling With Interference Management in Dense Millimeter-Wave Networks," in IEEE Transactions on Mobile Computing, vol. 23, no. 12, pp. 13392-13405, Dec. 2024
Millimeter-Wave
A. Wadaskar, S. Sarkar and D. Cabric, "Millimeter Wave Spectrum Sharing using Analog True Time Delay Array based Wideband Nulling," 2024 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), Washington, DC, USA, 2024
Spectrum sharingcoexistence
T. Zhao, S. Sarkar, Y. Tian and D. Cabric, "Anomaly Transmitter Recognition and Tracking," 2024 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), Washington, DC, USA, 2024
Machine LearningSignal classification
K. Doke, S. Sarkar, B. Okoro, D. Cabric and M. Zheleva, "RadVIEW: Robust radar detection and characterization in high-noise regimes," 2024 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), Washington, DC, USA, 2024
RadarSpectrum Sensing
H. Sallouha, S. Sarkar, E. Krijestorac and D. Cabric, "REM-U-Net: Deep Learning Based Agile REM Prediction With Energy-Efficient Cell-Free Use Case," in IEEE Open Journal of Signal Processing, vol. 5, pp. 750-765, 2024
Machine LearningSpectrum Sensing
T. Zhao, S. Sarkar, E. Krijestorac, and D. Cabric, "GAN-RXA: A Practical Scalable Solution to Receiver-Agnostic Transmitter Fingerprinting" in IEEE Transactions on Cognitive Communications and Networking, April 2024
Signal classificationMachine Learning
S. Sarkar, D. Guo, and D. Cabric, "RadYOLOLet: Radar Detection and Parameter Estimation Using YOLO and WaveLet," submitted
Machine LearningRadarSpectrum Sensing
H. Sallouha, S. Sarkar, E. Krijestorac, and D. Cabric, "REM-U-net: Deep Learning Based Agile REM Prediction with Energy-Efficient Cell-Free Use Case," submitted to IEEE Open Journal of Signal Processing
Machine LearningSpectrum Sensing
S. Sarkar, D. Guo, and D. Cabric, "ProSpire: Proactive Spatial Prediction of Radio Environment Using Deep Learning," submitted
Machine LearningSpectrum Sensing
J. Feng, T. Zhao, S. Sarkar, D. Konrad, T. Jacques, D. Cabric, and N. Sehatbakhsh. "Fingerprinting IoT Devices Using Latent Physical Side-Channels," Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 7, 2, Article 54, June 2023
IoTMachine Learning
E. Krijestorac, H. Sallouha, S. Sarkar, D. Cabric, "Agile Radio Map Prediction Using Deep Learning," in the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), June 2023
Machine LearningSpectrum Sensing
V. Boljanovic, S. Sarkar, D. Cabric, "Millimeter-Wave User Association and Low-Interference Beam Scheduling (Invited Paper)," in the 6th ACM Workshop on Millimeter-Wave Networks and Sensing Systems (mmNets), October 2022
Millimeter-Wave
R. Li, S. Sarkar, D. Cabric, J. McGraw, P. Powers, J. Vitaz, "Fast ML-Assisted Interference Estimation and Suppression for Digital Phased Array Radar," in the 2022 IEEE International Symposium on Phased Array Systems & Technology (PAST)
RadarBeamformingMachine Learning