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Research at A1 Information, Learning, and Communications Research Lab
Quantum Information Science
S.Sodagari, "Integrating Quantum and Satellites: A New Era of Connectivity," in IEEE Access, vol. 11, 2023
Privacy and Security of Mobile Crowdsensing
From tracking pandemics to applications, such as Google Maps, Uber, environmental monitoring, journalism, healthcare, crisis/disaster response, air quality control, noise and traffic monitoring, urban planning, etc., mobile crowdsourcing systems are interweaved with the society and daily lives. Major security and privacy challenges in MCS systems along with solutions leveraging the capabilities of blockchains, smart contracts, machine learning, games, incentives, spatio-temporal cloaking, etc., are presented to preserve privacy and security of mobile workers, task requestors, and other aspects of crowdsourcing systems. This is specially important for use cases, such as Industrial IoT, Internet of Vehicles, wireless crowdsensed systems, social crowdsourcing, edge-computing, personalized and privacy-preserving recommendation, and mobile worker recruitment.
S.Sodagari, "Trends for Mobile IoT Crowdsourcing Privacy and Security in the Big Data Era," in IEEE Transactions on Technology and Society, 2022
Nondetectable Communication
This research shows how to achieve covert communication over Nakagami channels in the presence of a warden equipped with low-SNR sensing capabilities. Once the warden is left oblivious about the message exchange, it does not attempt to decode the message. Shabnam Sodagari, "Covert Communications Against an Adversary with Low-SNR Sensing Capability in Nakagami Fading," IEEE Sensors Letters, vol. 4, no. 5, pp.1-4, May 2020 Deep Learning for Channel Estimation Deep neural networks can eliminate the need for feedback for transmitter channel state information (CSI) in frequency division duplex (FDD) by inferring the downlink CSI from the uplink CSI.
M. S. Safari, V. Pourahmadi and S. Sodagari, "Deep UL2DL: Data-Driven Channel Knowledge Transfer from Uplink to Downlink," in IEEE Open Journal of Vehicular Technology, vol.1, pp. 29-44, 2020
Non Orthogonal Multiple Access (NOMA)
NOMA is a promising multiple access method for future generations of communication systems. NOMA can accommodate several users within a resource block. This research identifies fundamental approaches to efficient and stable NOMA allocation schemes. Shabnam Sodagari, "Underpinnings of User-Channel Allocation in Non-Orthogonal Multiple Access for 5G", Springer Nature Int'l Journal on Wireless Information Networks, 2022 N. Madani and S. Sodagari, “Performance Analysis of Non-Orthogonal Multiple Access with Underlaid Device-to-Device Communications,” IEEE Access, vol. 6, 2018
Effects of Delay in Spectrum Sharing Networks
This research looks into effects of delay in cognitive radio networks. It investigates how the realistic assumption of timing misalignment can enhance the performance of spectrum sharing networks. Shabnam Sodagari and H. Jafarkhani, "Enhanced Spectrum Sharing and Cognitive Radio Using Asynchronous Primary and Secondary Users," IEEE Communications Letters, vol. 22, no. 4, pp. 832-835, April 2018 Shabnam Sodagari, H. Jafarkhani, and H. Yousefi'zadeh, "Improved Cognitive Radio Receivers Using Timing Mismatch of Primary and Secondary Users," in IEEE Transactions on Circuits and Systems II, vol. 66, no. 6, pp. 948-952, June 2019 Scheduling in Dynamic Spectrum Access Networks
This research investigates throughput optimized scheduling schemes in cognitive radio networks. The schemes take into account short lived idle primary channels and uncertainty about future primary and secondary users activities. Computational complexity and performance bounds are further investigated. Shabnam Sodagari, "Real Time Scheduling for Cognitive Radio Networks," in IEEE Systems Journal, vol. 12, no. 3, Sept 2018
Connected Health
This research considers the problem of spectrum scarcity and looks into cognitive radio systems for body area networks data transmission. This allows the telemedicine and in-hospital patient monitoring data to use the same resources, while avoiding interference among wireless systems. S. Sodagari, B. Bozorgchami, and H. Aghvami, “Technologies and Challenges for Cognitive Radio Enabled Medical Wireless Body Area Networks," IEEE Access, 2018B. Bozorgchami and S. Sodagari, "Spectrally efficient telemedicine and in-hospital patient data transfer," IEEE International Symposium on Medical Measurements and Applications, May 2017, Mayo Clinic, USA Note to PhD Applicants Highly motivated PhD applicants are encouraged to send their CV to Prof. Shabnam Sodagari.
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