3 citations · 6 across the 24 of their papers we have counts for
7 papers · 1 filter
Neural Kalman Filtering for Unknown Dynamics: Task-Aware Learning with a Koopman Backbone
Mintaek Oh, Jeonghun Park, Nir Shlezinger +2
Recent years have witnessed a growing interest in AI-aided Kalman filters. While emerging methodologies, such as KalmanNet, were shown to facilitate tracking in partially known sta…
Fronthaul Compression for Uplink Cloud-RAN with Finite-Alphabet Inputs: A Reverse Mercury/Waterfilling Approach
Subin Shin, Jaehoon Lee, Seok-Hwan Park +1
The cloud radio access network (C-RAN) mitigates inter-cell interference by jointly processing the observations of distributed remote units (RUs) at a centralized unit (CU), but li…
Digital Twin-Aided Prescreening for User Scheduling in MU-MIMO Downlink Systems
Namhyun Kim, Mahmoud Saad Abouamer, Jeonghun Park +2
In dense deployments, massive multi-user multiple-input multiple-output (MU-MIMO) base stations can acquire instantaneous channel state information (CSI) for only a limited subset…
When 5G MIMO Scaling Breaks: Toward 6G Upper-Mid-Band Extreme MIMO
Kwang Soon Kim, Jeonghun Park, Byung-Wook Min +9
The upper-mid band, particularly the 7-8 GHz range within frequency range 3 (FR3), has emerged as a leading spectrum candidate for wide-area sixth-generation (6G) cellular networks…
Scalable and Convergent Generalized Power Iteration Precoding for Massive MIMO Systems
Seunghyeong Yoo, Mintaek Oh, Jeonghun Park +2
In massive multiple-input multiple-output (MIMO) systems, achieving high spectral efficiency (SE) often requires advanced precoding algorithms whose complexity scales rapidly with…
Space-Time Adaptive Beamforming for Satellite Communications: Harnessing Doppler as New Signaling Dimensions
Hyeongtak Yun, Seyong Kim, Jeonghun Park
Low Earth orbit (LEO) satellite downlinks are fundamentally limited by severe channel correlation: the line-of-sight (LoS)-dominant propagation and high orbital altitude confine us…
Fractional Programming for Kullback-Leibler Divergence in Hypothesis Testing
Jeongwoo Park, Seongkyu Jung, Kaiming Shen +1
Maximizing the Kullback-Leibler divergence (KLD) is a fundamental problem in waveform design for active sensing and hypothesis testing, as it directly relates to the error exponent…