arXiv subjects

Juyeop Kim

Publications and source records attributed to Juyeop Kim.

11 recordsLinked to original sources

arXiv2026

Accelerating 5G Synchronization Signal Timing Offset Estimation Using Dual-Rate Sampling

Cell search engineers face significant challenge in reducing computation time to meet the requirements for fast initial access and radio link recovery. Since the majority of cell search time is consumed by Primary Synchronization Signal (PSS) detection, reducing the computational burden of this step is critical for shortening the overall procedure. This paper proposes a novel timing offset estimation scheme designed to accelerate 5G cell search. Leveraging the 5G Synchronization Signal Block (SSB) structure, the proposed scheme employs a two-step estimation process using dual-rate sampling. This approach effectively reduces the PSS detection search space without compromising the performance of subsequent processes. Performance evaluations in practical system and channel environments demonstrate that the proposed scheme reduces the cell search procedure time by 68\% compared to the baseline, while maintaining Physical Broadcast CHannel (PBCH) decoding performance.

eess.SP↗

arXiv2026

Steering Away from Memorization: Reachability-Constrained Reinforcement Learning for Text-to-Image Diffusion

Text-to-image diffusion models often memorize training data, revealing a fundamental failure to generalize beyond the training set. Current mitigation strategies typically sacrifice image quality or prompt alignment to reduce memorization. To address this, we propose Reachability-Aware Diffusion Steering (RADS), an inference-time framework that prevents memorization while preserving generation fidelity. RADS models the diffusion denoising process as a dynamical system and applies concepts from reachability analysis to approximate the "backward reachable tube"--the set of intermediate states that inevitably evolve into memorized samples. We then formulate mitigation as a constrained reinforcement learning (RL) problem, where a policy learns to steer the trajectory away from memorization via minimal perturbations in the caption embedding space. Empirical evaluations show that RADS achieves a superior Pareto frontier between generation diversity (SSCD), quality (FID), and alignment (CLIP) compared to state-of-the-art baselines. Crucially, RADS provides robust mitigation without modifying the diffusion backbone, offering a plug-and-play solution for safe generation. Our website is available at: https://s-karnik.github.io/rads-memorization-project-page/.

cs.CV↗

arXiv2025

Design of Orthogonal Phase of Arrival Positioning Scheme Based on 5G PRS and Optimization of TOA Performance

This study analyzes the performance of positioning techniques based on configuration changes of 5G New Radio signals. In 5G networks, a terminal position is determined from the Time of Arrival of Positioning Reference Signals transmitted by base stations. We propose an algorithm that improves TOA accuracy under low sampling rate constraints and implement 5G PRS for positioning in a software defined modem. We also examine how flexible time frequency resource allocation of PRS affects TOA estimation accuracy and discuss optimal PRS configurations for a given signal environment.

eess.SP↗

arXiv2025

How Diffusion Models Memorize

Despite their success in image generation, diffusion models can memorize training data, raising serious privacy and copyright concerns. Although prior work has sought to characterize, detect, and mitigate memorization, the fundamental question of why and how it occurs remains unresolved. In this paper, we revisit the diffusion and denoising process and analyze latent space dynamics to address the question: "How do diffusion models memorize?" We show that memorization is driven by the overestimation of training samples during early denoising, which reduces diversity, collapses denoising trajectories, and accelerates convergence toward the memorized image. Specifically: (i) memorization cannot be explained by overfitting alone, as training loss is larger under memorization due to classifier-free guidance amplifying predictions and inducing overestimation; (ii) memorized prompts inject training images into noise predictions, forcing latent trajectories to converge and steering denoising toward their paired samples; and (iii) a decomposition of intermediate latents reveals how initial randomness is quickly suppressed and replaced by memorized content, with deviations from the theoretical denoising schedule correlating almost perfectly with memorization severity. Together, these results identify early overestimation as the central underlying mechanism of memorization in diffusion models.

cs.CV↗

arXiv2024

Impact of Regularization on Calibration and Robustness: from the Representation Space Perspective

Recent studies have shown that regularization techniques using soft labels, e.g., label smoothing, Mixup, and CutMix, not only enhance image classification accuracy but also mitigate miscalibration due to overconfident predictions, and improve robustness against adversarial attacks. However, the underlying mechanisms of such improvements remain underexplored. In this paper, we offer a novel explanation from the perspective of the representation space (i.e., the space of the features obtained at the penultimate layer). Based on examination of decision boundaries and structure of features (or representation vectors), our study investigates confidence contours and gradient directions within the representation space. Furthermore, we analyze the adjustments in feature distributions due to regularization in relation to these contours and directions, from which we uncover central mechanisms inducing improved calibration and robustness. Our findings provide new insights into the characteristics of the high-dimensional representation space in relation to training and regularization using soft labels.

cs.CV↗

arXiv2023

Exploring Adversarial Robustness of Vision Transformers in the Spectral Perspective

The Vision Transformer has emerged as a powerful tool for image classification tasks, surpassing the performance of convolutional neural networks (CNNs). Recently, many researchers have attempted to understand the robustness of Transformers against adversarial attacks. However, previous researches have focused solely on perturbations in the spatial domain. This paper proposes an additional perspective that explores the adversarial robustness of Transformers against frequency-selective perturbations in the spectral domain. To facilitate comparison between these two domains, an attack framework is formulated as a flexible tool for implementing attacks on images in the spatial and spectral domains. The experiments reveal that Transformers rely more on phase and low frequency information, which can render them more vulnerable to frequency-selective attacks than CNNs. This work offers new insights into the properties and adversarial robustness of Transformers.

cs.CV↗

arXiv2023

Curved Representation Space of Vision Transformers

Neural networks with self-attention (a.k.a. Transformers) like ViT and Swin have emerged as a better alternative to traditional convolutional neural networks (CNNs). However, our understanding of how the new architecture works is still limited. In this paper, we focus on the phenomenon that Transformers show higher robustness against corruptions than CNNs, while not being overconfident. This is contrary to the intuition that robustness increases with confidence. We resolve this contradiction by empirically investigating how the output of the penultimate layer moves in the representation space as the input data moves linearly within a small area. In particular, we show the following. (1) While CNNs exhibit fairly linear relationship between the input and output movements, Transformers show nonlinear relationship for some data. For those data, the output of Transformers moves in a curved trajectory as the input moves linearly. (2) When a data is located in a curved region, it is hard to move it out of the decision region since the output moves along a curved trajectory instead of a straight line to the decision boundary, resulting in high robustness of Transformers. (3) If a data is slightly modified to jump out of the curved region, the movements afterwards become linear and the output goes to the decision boundary directly. In other words, there does exist a decision boundary near the data, which is hard to find only because of the curved representation space. This explains the underconfident prediction of Transformers. Also, we examine mathematical properties of the attention operation that induce nonlinear response to linear perturbation. Finally, we share our additional findings, regarding what contributes to the curved representation space of Transformers, and how the curvedness evolves during training.

cs.CV↗

arXiv2023

Amicable Aid: Perturbing Images to Improve Classification Performance

While adversarial perturbation of images to attack deep image classification models pose serious security concerns in practice, this paper suggests a novel paradigm where the concept of image perturbation can benefit classification performance, which we call amicable aid. We show that by taking the opposite search direction of perturbation, an image can be modified to yield higher classification confidence and even a misclassified image can be made correctly classified. This can be also achieved with a large amount of perturbation by which the image is made unrecognizable by human eyes. The mechanism of the amicable aid is explained in the viewpoint of the underlying natural image manifold. Furthermore, we investigate the universal amicable aid, i.e., a fixed perturbation can be applied to multiple images to improve their classification results. While it is challenging to find such perturbations, we show that making the decision boundary as perpendicular to the image manifold as possible via training with modified data is effective to obtain a model for which universal amicable perturbations are more easily found.

cs.CV↗

arXiv2021

Designing a Robust Carrier Frequency Offset Estimation Scheme for Meeting Target Decoding Performance in an OFDM System

In a target communication system, a delicately designed frequency offset estimation scheme is required to meet certain decoding performance. In this paper, we proposed at wo-step estimation scheme, coarse and residual, with different value of an time interval parameter. A result of RF conduction test shows that the proposed method has an 1dB gain of SNR compared to coarse-only estimator. A result of the commercial test also indicates the proposed method outperforms coarse-only estimator especially in low SNR condition.

eess.SP↗

arXiv2020

Exploitation of Channel-Learning for Enhancing 5G Blind Beam Index Detection

Proliferation of 5G devices and services has driven the demand for wide-scale enhancements ranging from data rate, reliability, and compatibility to sustain the ever increasing growth of the telecommunication industry. In this regard, this work investigates how machine learning technology can improve the performance of 5G cell and beam index search in practice. The cell search is an essential function for a User Equipment (UE) to be initially associated with a base station, and is also important to further maintain the wireless connection. Unlike the former generation cellular systems, the 5G UE faces with an additional challenge to detect suitable beams as well as the cell identities in the cell search procedures. Herein, we propose and implement new channel-learning schemes to enhance the performance of 5G beam index detection. The salient point lies in the use of machine learning models and softwarization for practical implementations in a system level. We develop the proposed channel-learning scheme including algorithmic procedures and corroborative system structure for efficient beam index detection. We also implement a real-time operating 5G testbed based on the off-the-shelf Software Defined Radio (SDR) platform and conduct intensive experiments with commercial 5G base stations. The experimental results indicate that the proposed channel-learning schemes outperform the conventional correlation-based scheme in real 5G channel environments.

cs.LG↗

arXiv2016

Group Communication Over LTE : A Radio Access Perspective

Long Term Evolution (LTE), which has its root on commercial mobile communications, recently becomes an influential solution to future public safety communications. To verify the feasibility of LTE for public safety, it is essential to investigate whether an LTE system optimized for one-to-one communications is capable of providing group communication, which is one of the most important service concepts in public safety. In general, a number of first responders for public safety need to form a group for communicating with each other or sharing the common data for collaboration on their mission. In this article, we analyze how the current LTE system can support group communication in a radio access aspect. Based on the requirements for group communication, we validate whether each LTE-enabled radio access method can efficiently support group communication. In addition, we propose a new multicast transmission scheme, termed index-coded Hybrid Automatic Retransmission reQuest (HARQ). By applying the index coding concept to HARQ operations, we show that the LTE system can provide group communication more sophisticatedly in terms of radio resource efficiency and scalability. We finally evaluate the performance of LTE-enabled group communication using several radio access methods and show how the proposed transmission scheme brings the performance enhancement via system level simulations.

cs.NI↗