arXiv subjects

Juseong Park

Publications and source records attributed to Juseong Park.

8 recordsLinked to original sources

arXiv2026

Expected performance of a water Cherenkov detector for reactor antineutrino--electron scattering

We evaluate the expected performance of a 170-ton water Cherenkov detector for reactor antineutrino--electron elastic scattering at the RENO near-detector site. The reactor-spectrum normalization is extracted from the reconstructed recoil-electron directional distribution using a constrained profile-likelihood fit for an exposure of 365.25 days. For the minimum prompt-hit multiplicity $N_{\mathrm{prompt}}\geq8$ scenario, which neglects PMT radioactivity, the total uncertainty on the reactor-spectrum normalization is ${}^{+1.08\%}_{-1.07\%}$. For the more restrictive $N_{\mathrm{prompt}}\geq20$ scenario, which includes the simulated PMT-radioactivity components, the total uncertainty is ${}^{+1.49\%}_{-1.47\%}$. These results demonstrate that directional information from a water Cherenkov detector can provide percent-level sensitivity to the reactor elastic-scattering normalization.

hep-ex↗

arXiv2026

Channel Geometry Preserving Generative Models for CSI Feedback in MU-MIMO

Under limited feedback, channel state information (CSI) reconstruction for multiuser multiple-input multiple-output (MU-MIMO) precoding is challenging, since the precoder should provide not only beamforming gain, but also robust suppression of inter-user interference. This paper revisits this classic problem by developing powerful decompression techniques at the base station (BS) that harness modern deep generative models. We propose two novel BS-side flow-matching generative CSI decoders that progressively transform either a simple prior or an initial CSI estimate into a reconstruction consistent with the feedback-conditioned channel distribution. We further show theoretically that conventional minimum mean-squared-error (MMSE)-based reconstructions of CSI often result in centroid-like compromises that fail to preserve the posterior geometry needed for inter-user interference suppression. In other words, MU-MIMO precoding based on MSE-oriented CSI reconstructions can be suboptimal, since such reconstructions frequently fail to maintain user orthogonality. Numerical results in FR3 spectrum show that the proposed flow-based methods consistently outperform MSE-based baselines in downlink sum-rate, with the advantage especially pronounced in interference-limited and spatially dense regimes. These results suggest that posterior-guided flow reconstruction is better aligned with MU-MIMO precoding than traditional MMSE-oriented CSI feedback, since it better preserves the channel geometry needed for user separation.

eess.SP↗

arXiv2025

A comparative study of physics capabilities of a liquid argon and a water based liquid scintillator at DUNE

We present a comprehensive comparison of the physics sensitivities of a Liquid Argon Time Projection Chamber (LArTPC) and a Water-based Liquid Scintillator (WbLS) detector, considering their potential deployment as the fourth far detector module in the DUNE facility. Using GLoBES-based simulations, we evaluate their performance in measuring standard neutrino oscillation parameters ($\theta_{23}, \delta_{13}$ and $\Delta m^{2}_{31}$), both in standard 3-neutrino case, as well as in presence of new physics scenarios involving light sterile neutrinos and neutral-current non-standard interactions (NC NSI). Our findings show that THEIA (a WbLS-based detector) significantly outperforms LArTPC in resolving the CP phase $\delta_{13}$,- especially near maximal CP violation, and in lifting the octant degeneracy of $\theta_{23}$ due to its superior energy resolution and ability to clearly identify the second oscillation maximum. Furthermore, THEIA offers competitive reconstruction precision even with relatively moderate energy resolutions ($7-10\%/\sqrt{E}$) and demonstrates enhanced robustness under new physics scenarios. These results support the physics-driven case for a hybrid DUNE configuration utilizing both LArTPC and WbLS technologies for optimized sensitivity across the full spectrum of neutrino oscillation and physics beyond the standard model.

hep-ph↗

arXiv2025

Self-Nomination: Deep Learning for Decentralized CSI Feedback Reduction in MU-MIMO Systems

This paper introduces a novel deep learning-based user-side feedback reduction framework, termed self-nomination. The goal of self-nomination is to reduce the number of users (UEs) feeding back channel state information (CSI) to the base station (BS), by letting each UE decide whether to feed back based on its estimated likelihood of being scheduled and its potential contribution to precoding in a multiuser MIMO (MU-MIMO) downlink. Unlike SNR- or SINR-based thresholding methods, the proposed approach uses rich spatial channel statistics and learns nontrivial correlation effects that affect eventual MU-MIMO scheduling decisions. To train the self-nomination network under an average feedback constraint, we propose two different strategies: one based on direct optimization with gradient approximations, and another using policy gradient-based optimization with a stochastic Bernoulli policy to handle non-differentiable scheduling. The framework also supports proportional-fair scheduling by incorporating dynamic user weights. Numerical results confirm that the proposed self-nomination method significantly reduces CSI feedback overhead. Compared to baseline feedback methods, self-nomination can reduce feedback by as much as 65%, saving not only bandwidth but also allowing many UEs to avoid feedback altogether (and thus, potentially enter a sleep mode). Self-nomination achieves this significant savings with negligible reduction in sum-rate or fairness.

eess.SP↗

arXiv2024

Probing Large Extra Dimension at DUNE using beam tunes

The Deep Underground Neutrino Experiment (DUNE) is a leading experiment in neutrino physics which is presently under construction. DUNE aims to measure the yet unknown parameters in the three flavor oscillation case which includes discovery of leptonic CP violation, determination of the neutrino mass hierarchy and measuring the octant of $\theta_{23}$. Additionally, the ancillary goals of DUNE include probing the subdominant effects induced by possible physics beyond the Standard Model (BSM). One such new physics scenario is the possible presence of Large Extra Dimension (LED) which can naturally give rise to tiny neutrino masses. LED impacts neutrino oscillation through two new parameters, - namely the lightest Dirac mass $m_{0}$ and the radius of the extra dimension $R_{\text{ED}}$ ($< 2$ $\mu$m). At the DUNE baseline of 1300 km, the probability seems to be modified more at the higher energy ($\gtrsim 4-5$ GeV) in presence of LED. In this work, we attempt to constrain the parameter space of $m_{0}$ and $R_{\text{ED}}$ by performing a statistical analysis of neutrino data simulated at DUNE far detector (FD). We illustrate how a combination of the standard low energy (LE) neutrino beam and a medium energy (ME) neutrino beam can take advantage of the relatively large impact of LED at higher energy and improve the constraints. In the analysis we also show the role of the individual oscillation channels ($\nu_{\mu} \to \nu_{e}, \nu_{\mu} \to \nu_{\mu}, \nu_{\mu} \to \nu_{\tau}$), as well as the two neutrino mass hierarchies.

hep-ph↗

arXiv2024

Generating High Dimensional User-Specific Wireless Channels using Diffusion Models

Deep neural network (DNN)-based algorithms are emerging as an important tool for many physical and MAC layer functions in future wireless communication systems, including for large multi-antenna channels. However, training such models typically requires a large dataset of high-dimensional channel measurements, which are very difficult and expensive to obtain. This paper introduces a novel method for generating synthetic wireless channel data using diffusion-based models to produce user-specific channels that accurately reflect real-world wireless environments. Our approach employs a conditional denoising diffusion implicit model (cDDIM) framework, effectively capturing the relationship between user location and multi-antenna channel characteristics. We generate synthetic high fidelity channel samples using user positions as conditional inputs, creating larger augmented datasets to overcome measurement scarcity. The utility of this method is demonstrated through its efficacy in training various downstream tasks such as channel compression and beam alignment. Our diffusion-based augmentation approach achieves over a 1-2 dB gain in NMSE for channel compression, and an 11dB SNR boost in beamforming compared to prior methods, such as noise addition or the use of generative adversarial networks (GANs).

cs.IT↗

arXiv2024

End-to-End Deep Learning for TDD MIMO Systems in the 6G Upper Midbands

This paper proposes and analyzes novel deep learning methods for downlink (DL) single-user multiple-input multiple-output (SU-MIMO) and multi-user MIMO (MU-MIMO) systems operating in time division duplex (TDD) mode. A motivating application is the 6G upper midbands (7-24 GHz), where the base station (BS) antenna arrays are large, user equipment (UE) array sizes are moderate, and theoretically optimal approaches are practically infeasible for several reasons. To deal with uplink (UL) pilot overhead and low signal power issues, we introduce the channel-adaptive pilot, as part of an analog channel state information feedback mechanism. Deep neural network (DNN)-generated pilots are used to linearly transform the UL channel matrix into lower-dimensional latent vectors. Meanwhile, the BS employs a second DNN that processes the received UL pilots to directly generate near-optimal DL precoders. The training is end-to-end which exploits synergies between the two DNNs. For MU-MIMO precoding, we propose a DNN structure inspired by theoretically optimum linear precoding. The proposed methods are evaluated against genie-aided upper bounds and conventional approaches, using realistic upper midband datasets. Numerical results demonstrate the potential of our approach to achieve significantly increased sum-rate, particularly at moderate to high signal-to-noise ratio (SNR) and when UL pilot overhead is constrained.

cs.IT↗

arXiv2022

Decentralized Computation Offloading With Cooperative UAVs: Multi-Agent Deep Reinforcement Learning Perspective

Limited computing resources of internet-of-things (IoT) nodes incur prohibitive latency in processing input data. This triggers new research opportunities toward task offloading systems where edge servers handle intensive computations of IoT devices. Deploying the computing servers at existing base stations may not be sufficient to support IoT nodes operating in a harsh environment. This requests mobile edge servers to be mounted on unmanned aerial vehicles (UAVs) that provide on-demand mobile edge computing (MEC) services. Time-varying offloading demands and mobility of UAVs need a joint design of the optimization variables for all time instances. Therefore, an online decision mechanism is essential for UAV-aided MEC networks. This article presents an overview of recent deep reinforcement learning (DRL) approaches where decisions about UAVs and IoT nodes are taken in an online manner. Specifically, joint optimization over task offloading, resource allocation, and UAV mobility is addressed from the DRL perspective. For the decentralized implementation, a multi-agent DRL method is proposed where multiple intelligent UAVs cooperatively determine their computations and communication policies without central coordination. Numerical results demonstrate that the proposed decentralized learning strategy is superior to existing DRL solutions. The proposed framework sheds light on the viability of the decentralized DRL techniques in designing self-organizing IoT networks.

cs.IT↗