Zobrazeno 1 - 10
of 60
pro vyhledávání: '"Hong, Songnam"'
Autor:
Lee, Jeongjae, Hong, Songnam
We study the channel estimation problem for a reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) multi-user multiple-input multiple-output (MU-MIMO) system. In particular, it is assumed that the channel between a RIS and a bas
Externí odkaz:
http://arxiv.org/abs/2410.13806
Autor:
Lee, Jeongjae, Hong, Songnam
Hybrid beamforming is an emerging technology for massive multiple-input multiple-output (MIMO) systems due to the advantages of lower complexity, cost, and power consumption. Recently, intelligent reflection surface (IRS) has been proposed as the cos
Externí odkaz:
http://arxiv.org/abs/2403.09083
Channel estimation is one of the key challenges for the deployment of extremely large-scale reconfigurable intelligent surface (XL-RIS) assisted multiple-input multiple-output (MIMO) systems. In this paper, we study the channel estimation problem for
Externí odkaz:
http://arxiv.org/abs/2401.06966
Autor:
Hwang, Ukjo, Hong, Songnam
Robust reinforcement learning (RRL) aims at seeking a robust policy to optimize the worst case performance over an uncertainty set of Markov decision processes (MDPs). This set contains some perturbed MDPs from a nominal MDP (N-MDP) that generate sam
Externí odkaz:
http://arxiv.org/abs/2305.06657
In federated learning (FL), it is commonly assumed that all data are placed at clients in the beginning of machine learning (ML) optimization (i.e., offline learning). However, in many real-world applications, it is expected to proceed in an online f
Externí odkaz:
http://arxiv.org/abs/2205.06491
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-amplitude-modulation constellations, it is still an open problem to ove
Externí odkaz:
http://arxiv.org/abs/2106.00433
Autor:
Chae, Jeongmin, Hong, Songnam
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 receives continuous streaming data locally. This learning model is call
Externí odkaz:
http://arxiv.org/abs/2102.12733
Autor:
Chae, Jeongmin, Hong, Songnam
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 global model from the aggregated local models. Online multiple kernel l
Externí odkaz:
http://arxiv.org/abs/2102.10861
Autor:
Chae, Jeongmin, Hong, Songnam
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 great interest in machine learning tasks for IoT systems. Focusing on
Externí odkaz:
http://arxiv.org/abs/2011.08930
Autor:
Chae, Jeongmin, Hong, Songnam
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 framework, we propose two selection criteria, named expected-kernel-di
Externí odkaz:
http://arxiv.org/abs/2010.11421