28 citations · 163 across the 86 of their papers we have counts for
10 papers · 1 filter
FLAME: A Federated Learning Approach for Multi-Modal RF Fingerprinting
Kasra Borazjani, Kiarash Kianfar, Seyyedali Hosseinalipour +1
Authorization systems are increasingly relying on processing radio frequency (RF) waveforms at receivers to fingerprint (i.e., determine the identity of) the corresponding transmit…
Dynamic and Robust Sensor Selection Strategies for Wireless Positioning with TOA/RSS Measurement
Myeung Suk Oh, Seyyedali Hosseinalipour, Taejoon Kim +3
Emerging wireless applications are requiring ever more accurate location-positioning from sensor measurements. In this paper, we develop sensor selection strategies for 3D wireless…
A Decentralized Pilot Assignment Algorithm for Scalable O-RAN Cell-Free Massive MIMO
Myeung Suk Oh, Anindya Bijoy Das, Seyyedali Hosseinalipour +3
Radio access networks (RANs) in monolithic architectures have limited adaptability to supporting different network scenarios. Recently, open-RAN (O-RAN) techniques have begun addin…
Learning-Based Adaptive IRS Control with Limited Feedback Codebooks
Junghoon Kim, Seyyedali Hosseinalipour, Andrew C. Marcum +3
Intelligent reflecting surfaces (IRS) consist of configurable meta-atoms, which can change the wireless propagation environment through design of their reflection coefficients. We…
Channel Estimation via Successive Denoising in MIMO OFDM Systems: A Reinforcement Learning Approach
Myeung Suk Oh, Seyyedali Hosseinalipour, Taejoon Kim +2
In general, reliable communication via multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) requires accurate channel estimation at the receiver.…
Multi-IRS-assisted Multi-Cell Uplink MIMO Communications under Imperfect CSI: A Deep Reinforcement Learning Approach
Junghoon Kim, Seyyedali Hosseinalipour, Taejoon Kim +2
Applications of intelligent reflecting surfaces (IRSs) in wireless networks have attracted significant attention recently. Most of the relevant literature is focused on the single…