Search Results for author: Taejoon Kim

Found 18 papers, 1 papers with code

Coding for Gaussian Two-Way Channels: Linear and Learning-Based Approaches

no code implementations31 Dec 2023 JungHoon Kim, Taejoon Kim, Anindya Bijoy Das, Seyyedali Hosseinalipour, David J. Love, Christopher G. Brinton

In this work, we aim to enhance and balance the communication reliability in GTWCs by minimizing the sum of error probabilities via joint design of encoders and decoders at the users.

Dynamic and Robust Sensor Selection Strategies for Wireless Positioning with TOA/RSS Measurement

no code implementations30 Apr 2023 Myeung Suk Oh, Seyyedali Hosseinalipour, Taejoon Kim, David J. Love, James V. Krogmeier, Christopher G. Brinton

For dynamic sensor selection, two greedy selection strategies are proposed, each of which exploits properties revealed in the derived CRLB expressions.

Robust Non-Linear Feedback Coding via Power-Constrained Deep Learning

no code implementations25 Apr 2023 JungHoon Kim, Taejoon Kim, David Love, Christopher Brinton

The design of codes for feedback-enabled communications has been a long-standing open problem.

A Decentralized Pilot Assignment Algorithm for Scalable O-RAN Cell-Free Massive MIMO

no code implementations12 Jan 2023 Myeung Suk Oh, Anindya Bijoy Das, Seyyedali Hosseinalipour, Taejoon Kim, David J. Love, Christopher G. Brinton

Radio access networks (RANs) in monolithic architectures have limited adaptability to supporting different network scenarios.

Joint Hybrid Delay-Phase Precoding Under True-Time Delay Constraints in Wideband THz Massive MIMO Systems

no code implementations14 Dec 2022 Dang Qua Nguyen, Taejoon Kim

By fixing the phase shifter (PS) precoder, a common strategy has been designing TTD precoder under the assumption of unbounded time delay values.

Deep Reinforcement Learning-Based Adaptive IRS Control with Limited Feedback Codebooks

no code implementations7 May 2022 JungHoon Kim, Seyyedali Hosseinalipour, Andrew C. Marcum, Taejoon Kim, David J. Love, Christopher G. Brinton

Intelligent reflecting surfaces (IRS) consist of configurable meta-atoms, which can alter the wireless propagation environment through design of their reflection coefficients.

reinforcement-learning Reinforcement Learning (RL)

An Unsupervised Domain Adaptation Model based on Dual-module Adversarial Training

no code implementations31 Dec 2021 Yiju Yang, Tianxiao Zhang, Guanyu Li, Taejoon Kim, Guanghui Wang

In this paper, we propose a dual-module network architecture that employs a domain discriminative feature module to encourage the domain invariant feature module to learn more domain invariant features.

Unsupervised Domain Adaptation

Learning-Based Adaptive IRS Control with Limited Feedback Codebooks

no code implementations3 Dec 2021 JungHoon Kim, Seyyedali Hosseinalipour, Andrew C. Marcum, Taejoon Kim, David J. Love, Christopher G. Brinton

We consider a practical setting where (i) the IRS reflection coefficients are achieved by adjusting tunable elements embedded in the meta-atoms, (ii) the IRS reflection coefficients are affected by the incident angles of the incoming signals, (iii) the IRS is deployed in multi-path, time-varying channels, and (iv) the feedback link from the base station to the IRS has a low data rate.

Dual Optimization for Kolmogorov Model Learning Using Enhanced Gradient Descent

no code implementations11 Jul 2021 Qiyou Duan, Hadi Ghauch, Taejoon Kim

To make our method more scalable to large-dimensional problems, we propose two acceleration schemes, namely, the eigenvalue decomposition (EVD) elimination strategy and an approximate EVD algorithm.

Parallel Scale-wise Attention Network for Effective Scene Text Recognition

no code implementations25 Apr 2021 Usman Sajid, Michael Chow, Jin Zhang, Taejoon Kim, Guanghui Wang

To address these issues, we propose a new multi-scale and encoder-based attention network for text recognition that performs the multi-scale FE and VA in parallel.

Scene Text Recognition

Channel Estimation via Successive Denoising in MIMO OFDM Systems: A Reinforcement Learning Approach

no code implementations25 Jan 2021 Myeung Suk Oh, Seyyedali Hosseinalipour, Taejoon Kim, Christopher G. Brinton, David J. Love

Our methodology includes a new successive channel denoising process based on channel curvature computation, for which we obtain a channel curvature magnitude threshold to identify unreliable channel estimates.

Denoising Q-Learning +2

Enhanced Beam Alignment for Millimeter Wave MIMO Systems: A Kolmogorov Model

no code implementations27 Jul 2020 Qiyou Duan, Taejoon Kim, Hadi Ghauch

We present an enhancement to the problem of beam alignment in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems, based on a modification of the machine learning-based criterion, called Kolmogorov model (KM), previously applied to the beam alignment problem.

Two-sample testing

Minimum Overhead Beamforming and Resource Allocation in D2D Edge Networks

no code implementations25 Jul 2020 JungHoon Kim, Taejoon Kim, Morteza Hashemi, Christopher G. Brinton, David J. Love

Device-to-device (D2D) communications is expected to be a critical enabler of distributed computing in edge networks at scale.

Distributed Computing Management

Joint Optimization of Signal Design and Resource Allocation in Wireless D2D Edge Computing

no code implementations27 Feb 2020 JungHoon Kim, Taejoon Kim, Morteza Hashemi, Christopher G. Brinton, David J. Love

In this paper, unlike previous mobile edge computing (MEC) approaches, we propose a joint optimization of wireless MIMO signal design and network resource allocation to maximize energy efficiency.

Networking and Internet Architecture Signal Processing

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