activity
20142020
most citedDeep Neural Network Based Active User Detection for Grant-free NOMA Systems

7 citations · 7 across the 5 of their papers we have counts for

collaborators

5 papers

eess.SP2020

Sparse Vector Transmission: An Idea Whose Time Has Come

Wonjun Kim, Hyoungju Ji, Hyojin Lee +3

In recent years, we are witnessing bewildering variety of automated services and applications of vehicles, robots, sensors, and machines powered by the artificial intelligence tech…

eess.SP20197 cited

Deep Neural Network Based Active User Detection for Grant-free NOMA Systems

Wonjun Kim, Youngjun Ahn, Byonghyo Shim

As a means to support the access of massive machine-type communication devices, grant-free access and non-orthogonal multiple access (NOMA) have received great deal of attention in…

cs.IT2019

Compressive Sensing Based Channel Estimation for Millimeter-Wave Full-Dimensional MIMO with Lens-Array

Ziwei Wan, Zhen Gao, Byonghyo Shim +3

Channel estimation (CE) for millimeter-wave (mmWave) lens-array suffers from prohibitive training overhead, whereas the state-of-the-art solutions require an extra complicated radi…

eess.SP2019

Channel aware sparse transmission for ultra low-latency communications in TDD systems

Wonjun Kim, Hyoungju Ji, Byonghyo Shim

Major goal of ultra reliable and low latency communication (URLLC) is to reduce the latency down to a millisecond (ms) level while ensuring reliability of the transmission. Since t…

cs.IT2014

Greedy Sparse Signal Recovery with Tree Pruning

Jaeseok Lee, Suhyuk Kwon, Jun Won Choi +1

Recently, greedy algorithm has received much attention as a cost-effective means to reconstruct the sparse signals from compressed measurements. Much of previous work has focused o…