9 citations · 16 across the 3 of their papers we have counts for
7 papers · 1 filter
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…
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.…
Minimum Description Feature Selection for Complexity Reduction in Machine Learning-based Wireless Positioning
Myeung Suk Oh, Anindya Bijoy Das, Taejoon Kim +2
Recently, deep learning approaches have provided solutions to difficult problems in wireless positioning (WP). Although these WP algorithms have attained excellent and consistent p…
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…
Error Analysis for Over-the-Air Federated Learning under Misaligned and Time-Varying Channels
Xiaoyan Ma, Shahryar Zehtabi, Taejoon Kim +1
This paper investigates an OFDM-based over-the-air federated learning (OTA-FL) system, where multiple mobile devices, e.g., unmanned aerial vehicles (UAVs), transmit local machine…
Mitigating Evasion Attacks in Federated Learning-Based Signal Classifiers
Su Wang, Rajeev Sahay, Adam Piaseczny +1
Recent interest in leveraging federated learning (FL) for radio signal classification (SC) tasks has shown promise but FL-based SC remains susceptible to model poisoning adversaria…