From the 1 of 9 linked papers with an AI index.
9 papers
Scalable Rate-Splitting Precoding via Recurrent Structure-Preserving Graph Neural Networks
Wonseok Choi, Jeongjae Lee, Songnam Hong
The paper introduces a recurrent structure‑preserving graph neural network (RS‑GNN) that learns scalable precoders for rate‑splitting multiple access (RSMA) in multi‑user MISO syst…
Channel Estimation for Reconfigurable Intelligent Surface Assisted Upper Mid-Band MIMO Systems
Jeongjae Lee, Chanwon Kim, Songnam Hong
The upper mid-band (UMB) spectrum is a key enabler for 6G systems, yet reconfigurable intelligent surface (RIS)-assisted UMB communications face severe channel estimation challenge…
CSIT-Free Beamforming for Multi-Group Multicast in Overloaded mmWave Systems
Wonseok Choi, Jeongjae Lee, Songnam Hong
We study downlink multi-group multicast (MGM) transmission in overloaded millimeter-wave (mmWave) systems, where the number of users exceeds the number of transmit antennas. We fir…
CSIT-Free Downlink Transmission for mmWave MU-MISO Systems in High-Mobility Scenario
Jeongjae Lee, Wonseok Choi, Songnam Hong
This paper investigates the downlink (DL) transmission in millimeter-wave (mmWave) multi-user multiple-input single-output (MU-MISO) systems especially focusing on a high speed mob…
Piecewise Beam Training and Channel Estimation for RIS-Aided Near-Field Communications
Jeongjae Lee, Songnam Hong
In this paper, we investigate the channel estimation challenge in reconfigurable intelligent surface (RIS)-aided near-field communication systems. Current channel estimation techni…
Blind Massive MIMO for Dense IoT Networks
Jeongjae Lee, Songnam Hong
In this paper, we investigate the downlink communication challenges in heavy-load Internet-of-Things (IoT) networks supported by frequency-division-duplexing (FDD) millimeter-wave…