289 citations · 520 across the 41 of their papers we have counts for
41 papers
Low-Complexity Symbol-Level Precoding for MU-MISO Downlink Systems with QAM Signals
Sungyeal Park, Yunseong Cho, Songnam Hong
This study proposes the construction of a transmit signal for large-scale antenna systems with cost-effective 1-bit digital-to-analog converters in the downlink. Under quadrature-a…
Distributed Online Learning with Multiple Kernels
Jeongmin Chae, Songnam Hong
We consider the problem of learning a nonlinear function over a network of learners in a fully decentralized fashion. Online learning is additionally assumed, where every learner r…
Multiple Kernel-Based Online Federated Learning
Jeongmin Chae, Songnam Hong
Online federated learning (OFL) becomes an emerging learning framework, in which edge nodes perform online learning with continuous streaming local data and a server constructs a g…
Bayesian Federated Learning over Wireless Networks
Seunghoon Lee, Chanho Park, Song-Nam Hong +2
Federated learning is a privacy-preserving and distributed training method using heterogeneous data sets stored at local devices. Federated learning over wireless networks requires…
Distributed Online Learning with Multiple Kernels
Jeongmin Chae, Songnam Hong
In the Internet-of-Things (IoT) systems, there are plenty of informative data provided by a massive number of IoT devices (e.g., sensors). Learning a function from such data is of…
Pool-based sequential active learning with multi kernels
Jeongmin Chae, Songnam Hong
We study a pool-based sequential active learning (AL), in which one sample is queried at each time from a large pool of unlabeled data according to a selection criterion. For this…