arXiv subjects

Jeonghun Park

Publications and source records attributed to Jeonghun Park.

At least 19 recordsLinked to original sources

arXiv2026

Load Balancing in Multi-Shell LEO Satellite Networks with Successive Interference Cancellation

Multi-shell low Earth orbit (LEO) networks can increase service opportunities, but altitude-dependent propagation can concentrate traffic on lower shells and create strong inter-shell interference under full frequency reuse. This paper develops a mathematical framework for load balancing in multi-shell LEO satellite networks. Satellites on each shell form an independent spherical Poisson point process (SPPP), and the typical user associates with one of the per-shell serving satellites through a shell-dependent biased received-power rule, with receiver-side successive interference cancellation (SIC) under full frequency reuse. Shell-wise association probabilities, conditioned serving-distance distributions, and the rate coverage probability under shell-dependent traffic loads are derived and validated by simulation. The results show that shell-dependent biasing alleviates lower-shell traffic concentration and improves rate coverage, while receiver-side SIC mitigates the dominant lower-shell interference experienced by users associated with upper shells. Load balancing provides its largest rate-coverage gain in traffic hotspots, while SIC becomes more valuable as receive-side isolation weakens. With a fixed satellite budget, distributing satellites across multiple shells can further improve hotspot rate coverage by adding shell-wise serving opportunities.

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arXiv2026

Attention-Aided MMSE with Ridge Denoising: How to Train under Noisy Channel Samples

Deep neural channel estimators are typically trained with clean channel state information (CSI), which is unavailable in practical orthogonal frequency-division multiplexing (OFDM) systems. In pilot-based OFDM, naive noisy-target training is structurally biased because the pilot input and noisy full-grid target share the same noise realization, driving the estimator toward identity copying. To address this for Attention-aided MMSE (A-MMSE), we propose a ridge-regularized objective that penalizes the generated filter directly. In a stylized fixed-filter model, this penalty induces scalar shrinkage and recovers the scalar MMSE gain at an explicit penalty value. We further construct surrogate training targets by estimating the channel covariance via eigenvalue clipping of the noisy empirical second-moment matrix, without requiring clean CSI labels. On COST 2100 channels, the proposed Ridge-A-MMSE consistently outperforms the Noise2Noise (N2N) baselines considered in this paper, and the combination of ridge regularization and covariance shrinkage approaches the same network trained with clean CSI labels at high signal-to-noise ratios (SNRs).

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arXiv2026

Digital Twin-Aided Prescreening for User Scheduling in MU-MIMO Downlink Systems

In dense deployments, massive multi-user multiple-input multiple-output (MU-MIMO) base stations can acquire instantaneous channel state information (CSI) for only a limited subset of users per scheduling interval, restricting multiuser diversity. We therefore propose Digital Twin User pre-Screening (DiTUS), a digital-twin (DT)-aided framework that identifies promising users before instantaneous CSI acquisition. DiTUS forms spatial covariances from DT-inferred departure angles and path powers. Optional Gaussian-process (GP) calibration mitigates path-power bias, while the dominant rank-r eigenspace of the aggregate covariance yields common reference beams. It prescreens the pool using DiTUS-P, a low-complexity projection-energy rule, or DiTUS-L, a greedy log-determinant rule that promotes spatial compatibility. A two-level protocol collects scalar beam reports from shortlisted users and requests r-dimensional effective-channel vectors only from the scheduled set. The framework also supports proportional-fair scheduling. At 15 dB under DT imperfections, simulations with 128 candidates, a 64-user effective-CSI acquisition budget, and a 64-user shortlist show that DiTUS-L achieves 35.06 +/- 0.49 bps/Hz versus 30.28 +/- 0.60 bps/Hz for semi-orthogonal user selection (SUS) with full-dimensional CSI from 64 users, demonstrating that DT-based prescreening preserves substantial multiuser-diversity gains by identifying strong, spatially compatible users before acquiring effective-channel vectors.

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arXiv2026

When 5G MIMO Scaling Breaks: Toward 6G Upper-Mid-Band Extreme MIMO

The upper-mid band, particularly the 7-8 GHz range within frequency range 3 (FR3), has emerged as a leading spectrum candidate for wide-area sixth-generation (6G) cellular networks. Its shorter wavelength enables hundreds of antenna elements to be integrated within the physical aperture of an existing 5G base-station panel. In principle, the resulting aperture gain can compensate for the increased path loss and enable extreme MIMO (E-MIMO) with 256 or more antenna ports while reusing current cell sites. In practice, however, simply scaling the 5G New Radio (NR) architecture from tens to hundreds of ports encounters fundamental system-level limitations. This paper identifies where 5G-style MIMO scaling breaks and develops a research roadmap for practical upper-mid-band E-MIMO. We first review the evolution of FR3 spectrum, its propagation and channel characteristics, and the emerging 6G system requirements. We then organize the principal challenges into four coupled areas: maintaining effective coverage across all physical channels and protocol states; implementing wideband, energy-efficient RF devices and radio units; developing new low-power array and beamforming architectures; and acquiring sufficiently refined channel state information with manageable sounding and feedback overhead. Representative system studies illustrate the coverage asymmetry between user-specific data transmission and common or channel-acquisition signals, as well as the spectral- and energy-efficiency tradeoffs among fully digital, hybrid, tri-hybrid, dynamic-metasurface, and fluid-antenna architectures. Finally, we discuss how distributed apertures, integrated sensing, AI-assisted channel acquisition, and environment-aware operation can transform fixed-aperture scaling into a deployable 6G E-MIMO architecture.

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arXiv2026

K-BrowseComp: A Web Browsing Agent Benchmark Grounded in Korean Contexts

Frontier model evaluations are shifting from foundational capabilities (e.g., instruction following and reasoning) toward compositional, agentic ones, but Korean agentic benchmarks remain scarce. We introduce K-BrowseComp, a web-browsing agent benchmark grounded in Korean contexts, consisting of 400 problems. The 300-problem K-BrowseComp-Verified subset is manually constructed and validated by native Korean speakers. On this subset, frontier LLMs, including GPT-5.5, DeepSeek-V4-Pro, and GLM-5.1, reach only 30.00--45.67\%, a substantial drop from BrowseComp, while Korean LLMs released through Korea's Proprietary AI Foundation Model program obtain only 0.00--10.33\%. We further construct a 100-problem synthetic split using hard few-shot exemplars and failure-mode-targeted generation to exploit the asymmetry between solving and creating web browsing problems. On the adversarially filtered synthetic diagnostic split, the strongest model reaches only 26.00\%, and we report this split separately as a targeted stress test. We publicly release our data and code.

cs.CL↗

arXiv2026

Space-Time Adaptive Beamforming for Satellite Communications: Harnessing Doppler as New Signaling Dimensions

Low Earth orbit (LEO) satellite downlinks are fundamentally limited by severe channel correlation: the line-of-sight (LoS)-dominant propagation and high orbital altitude confine users to a narrow angular region, rendering the multiuser channel matrix ill-conditioned. This paper provides a rigorous characterization of this limitation by exploiting the Vandermonde structure of the channel. Specifically, we link the minimum eigenvalue of the channel Gram matrix to user crowding through a balls-and-bins abstraction, and derive asymptotic sum rate scaling laws for both uniform linear arrays and uniform planar arrays. Our analysis reveals a sharp density threshold beyond which zero-forcing (ZF) precoding provably fails. To overcome this spatial multiplexing breakdown, we propose space-time adaptive beamforming (STAB), which exploits user-dependent residual Doppler shifts as an additional discrimination dimension. By constructing a time-extended channel in the joint space-Doppler domain, STAB restores a non-vanishing sum rate in regimes where purely spatial ZF collapses. We further develop a space-Doppler user selection (SDS) algorithm that leverages both spatial and Doppler separability for scheduling. Numerical results corroborate the analytical predictions and demonstrate that STAB with SDS achieves substantial sum rate gains over conventional methods in dense LEO downlink scenarios.

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arXiv2026

Scalable and Convergent Generalized Power Iteration Precoding for Massive MIMO Systems

In massive multiple-input multiple-output (MIMO) systems, achieving high spectral efficiency (SE) often requires advanced precoding algorithms whose complexity scales rapidly with the number of antennas, limiting practical deployment. In this paper, we develop a scalable and computationally efficient generalized power iteration precoding (GPIP) framework for massive MIMO systems under both perfect and imperfect channel state information at the transmitter (CSIT). By exploiting the low-dimensional subspace property of optimal precoders, we reformulate the high-dimensional beamforming problem into a lower-dimensional weight optimization that scales with the number of users rather than antennas. We further extend this framework to the imperfect CSIT scenario by showing that stationary solutions reside in a combined subspace spanned by the estimated channel and error covariance matrices, enabling a robust design via low-rank approximation. To reduce computational cost, we leverage the Sherman-Morrison formula to simplify matrix inversions. Moreover, interpreting the GPIP update as a projected preconditioned gradient ascent method, we establish convergence guarantees and develop a stable and monotonic algorithm using a backtracking line search. Numerical results demonstrate that the proposed methods achieve the highest SE performance compared to state-of-the-art linear precoders with significantly reduced complexity and convergence, highlighting their suitability for large-scale MIMO systems.

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arXiv2026

Fractional Programming for Kullback-Leibler Divergence in Hypothesis Testing

Maximizing the Kullback-Leibler divergence (KLD) is a fundamental problem in waveform design for active sensing and hypothesis testing, as it directly relates to the error exponent of detection probability. However, the associated optimization problem is highly nonconvex due to the intricate coupling of log-determinant and matrix trace terms. Existing solutions often suffer from high computational complexity, typically requiring matrix inversion at every iteration. In this paper, we propose a computationally efficient optimization framework based on fractional programming (FP). Our key idea is to reformulate the KLD maximization problem into a sequence of tractable quadratic subproblems using matrix FP. To further reduce complexity, we introduce a nonhomogeneous relaxation technique that replaces the costly linear system solver with a simple closed-form update, thereby reducing the per-iteration complexity to quadratic order. To compensate for the convergence speed trade-off caused by relaxation, we employ an acceleration method called STEM by interpreting the iterative scheme as a fixed-point mapping. The resulting algorithm achieves significantly faster convergence rates with low per-iteration cost. Numerical results demonstrate that our approach reduces the total runtime by orders of magnitude compared to a state-of-the-art benchmark. Finally, we apply the proposed framework to a multiple random access scenario and a joint integrated sensing and communication scenario, validating the efficacy of our framework in such applications.

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arXiv2025

The MIMO-ME-MS Channel: Analysis and Algorithm for Secure MIMO Integrated Sensing and Communications

This paper addresses precoder design for secure multiple-input multiple-output (MIMO) integrated sensing and communications (ISAC) systems. We introduce a MIMO channel with a multiple-antenna eavesdropper and a multiple-antenna sensing receiver (MIMO-ME-MS) and analyze the fundamental performance limits of this tripartite tradeoff. Using sensing mutual information, we formulate the precoder design as a nonconvex weighted sum rate maximization problem. A high signal-to-noise ratio analysis based on a subspace decomposition characterizes the maximum weighted degrees of freedom. This analysis reveals the structure of a quasi-optimal precoder that must span the ``useful subspace'' and demonstrates the inadequacy of extending known schemes from simpler wiretap or ISAC channels. To solve this nonconvex problem, we develop a practical two-stage iterative algorithm that alternates between a sequential basis construction stage and a power allocation stage that solves the resulting difference-of-convex program. We demonstrate that the proposed method captures the desirable precoder structure identified in our analysis and achieves substantial performance gains in the MIMO-ME-MS channel.

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arXiv2025

Uplink Coordinated Pilot Design for 1-bit Massive MIMO in Correlated Channel

In this paper, we propose a coordinated pilot design method to minimize the channel estimation mean squared error (MSE) in 1-bit analog-to-digital converters (ADCs) massive multiple-input multiple-output (MIMO). Under the assumption that the well-known Bussgang linear minimum mean square error (BLMMSE) estimator is used for channel estimation, we first observe that the resulting MSE leads to an intractable optimization problem, as it involves the arcsin function and a complex multiple matrix ratio form. To resolve this, we derive the approximate MSE by assuming the low signal-to-noise ratio (SNR) regime, by which we develop an efficient coordinated pilot design based on a fractional programming technique. The proposed pilot design is distinguishable from the existing work in that it is applicable in general system environments, including correlated channel and multi-cell environments. We demonstrate that the proposed method outperforms the channel estimation accuracy performance compared to the conventional approaches.

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arXiv2025

Spherical Point Process with Random Heights: New Approach for Modeling and Analysis of Downlink Satellite Networks

The Low Earth Orbit (LEO) satellite industry is undergoing rapid expansion, with operators competitively launching satellites due to the first-come, first-served principle governing orbital rights. This has led to the formation of increasingly large-scale, volumetric constellation where satellites operate across a diverse range of altitudes. To address the need for analyzing such complex networks, this paper establishes a new analytical framework for LEO constellations by leveraging a 3D Poisson point process (PPP). Specifically, we introduce a random height model (RHM) that can capture various altitude distributions by applying a random radial displacement to points generated by a homogeneous PPP on a nominal shell. Building on this, we derive an analytical expression for the downlink coverage probability. To motivate our model, we show that the altitude distributions of several leading satellite constellations, including Starlink, align with our model's assumptions. We then demonstrate through Monte Carlo simulations that the coverage probability of our RHM closely matches that of these real-world networks. Finally, we confirm the accuracy of our analytical expressions by showing their agreement with simulation results. Our work thereby provides a powerful tool for understanding and predict how the statistical distribution of satellite altitudes impacts network performance.

cs.IT↗

arXiv2025

Transmit What You Need: Task-Adaptive Semantic Communications for Visual Information

Recently, semantic communications have drawn great attention as the groundbreaking concept surpasses the limited capacity of Shannon's theory. Specifically, semantic communications probably become crucial in realizing visual tasks that demand massive network traffic. Although highly distinctive forms of visual semantics exist for computer vision tasks, a thorough investigation of what visual semantics can be transmitted in time and which one is required for completing different visual tasks has not yet been reported. To this end, we first scrutinize the achievable throughput in transmitting existing visual semantics through the limited wireless communication bandwidth. In addition, we further demonstrate the resulting performance of various visual tasks for each visual semantic. Based on the empirical testing, we suggest a task-adaptive selection of visual semantics is crucial for real-time semantic communications for visual tasks, where we transmit basic semantics (e.g., objects in the given image) for simple visual tasks, such as classification, and richer semantics (e.g., scene graphs) for complex tasks, such as image regeneration. To further improve transmission efficiency, we suggest a filtering method for scene graphs, which drops redundant information in the scene graph, thus allowing the sending of essential semantics for completing the given task. We confirm the efficacy of our task-adaptive semantic communication approach through extensive simulations in wireless channels, showing more than 45 times larger throughput over a naive transmission of original data. Our work can be reproduced at the following source codes: https://github.com/jhpark2024/jhpark.github.io

cs.NI↗

arXiv2025

Asymptotic Scaling Law Analysis of Multicast Satellite Communications with Massive MIMO

In this paper, we consider a geostationary orbit (GEO) satellite communication system that employs massive multiple-input multiple-output (MIMO) for multicast transmission. By modeling the spatial distribution of ground users using a Poisson point process (PPP) and assuming a fixed-beam precoding is adopted, we find a closed-form expression for the asymptotical rate scaling law as a function of the number of antennas and the scaling factors of user density and multicast users. From the derived analytical expression, we reveal that the rate degradation caused by multicast transmission can be precisely compensated by increasing the user density accordingly.

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arXiv2025

Energy-Efficient State Estimation with 1-Bit Sensing: A Bussgang-Kalman Framework for Internet of Things

Accurate state estimation from heavily quantized measurements is a key challenge in resource-constrained Internet of Things (IoT) sensing and tracking, where battery-powered devices may employ low-resolution analog-to-digital converters (ADCs) to simplify sensor hardware and reduce the amount of data. Existing model-based and hybrid learning-based estimators, however, typically assume high-resolution observations and therefore degrade severely under 1-bit quantization. In this paper, we study nonlinear state estimation with 1-bit observations and develop a Bussgang-aided filtering framework for IoT sensing front-ends with 1-bit quantization. For fully known system models, we propose a Bussgang-aided Kalman Filter (BKF) that explicitly incorporates quantization distortion into recursive estimation, together with a reduced-complexity variant (reduced-BKF) for computationally efficient implementation. For partially known models, we further propose Bussgang-aided KalmanNet (BKNet), a model-based deep learning architecture that combines adaptive dithering with gated recurrent units (GRUs) to mitigate severe quantization effects and model mismatch. Experiments on the Lorenz attractor and the Michigan NCLT dataset, both under 1-bit front-end quantization, demonstrate accurate and robust state estimation under highly nonlinear dynamics, imperfect models, and extreme quantization. These results support the potential of the proposed framework for reliable state estimation in resource-constrained IoT sensing and tracking applications with low-resolution front-ends.

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arXiv2025

Efficient RF Chain Selection for MIMO Integrated Sensing and Communications: A Greedy Approach

In multiple-input multiple-output integrated sensing and communication (MIMO ISAC) systems, radio frequency chain (i.e., RF chain) selection plays a vital role in reducing hardware cost, power consumption, and computational complexity. However, designing an effective RF chain selection strategy is challenging due to the disparity in performance metrics between communication and sensing-mutual information (MI) versus beam-pattern mean-squared error (MSE) or the Cram\'er-Rao lower bound (CRLB). To overcome this, we propose a low-complexity greedy RF chain selection framework maximizing a unified MI-based performance metric applicable to both functions. By decomposing the total MI into individual contributions of each RF chain, we introduce two approaches: greedy eigen-based selection (GES) and greedy cofactor-based selection (GCS), which iteratively identify and remove the RF chains with the lowest contribution. We further extend our framework to beam selection for beamspace MIMO ISAC systems, introducing diagonal beam selection (DBS) as a simplified solution. Simulation results show that our proposed methods achieve near-optimal performance with significantly lower complexity than exhaustive search, demonstrating their practical effectiveness for MIMO ISAC systems.

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arXiv2025

Integrated Sensing and Communications in Downlink FDD MIMO without CSI Feedback

In this paper, we propose a precoding framework for frequency division duplex (FDD) integrated sensing and communication (ISAC) systems with multiple-input multiple-output (MIMO). Specifically, we aim to maximize ergodic sum spectral efficiency (SE) while satisfying a sensing beam pattern constraint defined by the mean squared error (MSE). Our method reconstructs downlink (DL) channel state information (CSI) from uplink (UL) training signals using partial reciprocity, eliminating the need for CSI feedback. To obtain the error covariance matrix of the reconstructed DL CSI, we devise an observed Fisher information-based estimation technique. Leveraging this, to mitigate interference caused by imperfect DL CSI reconstruction and sensing operations, we propose a rate-splitting multiple access (RSMA) aided precoder optimization method. This method jointly updates the precoding vector and Lagrange multipliers by solving the nonlinear eigenvalue problem with eigenvector dependency to maximize SE. The numerical results show that the proposed design achieves precise beam pattern control, maximizes SE, and significantly improves the sensing-communication trade-off compared to the state-of-the-art methods in FDD ISAC scenarios.

cs.IT↗

arXiv2025

Learning MMSE Filters for OFDM Channel Estimation: Attention Transformer Gains at Linear Inference

In orthogonal frequency division multiplexing (OFDM), accurate channel estimation is crucial. Classical signal processing-based approaches, such as linear minimum mean-squared error (LMMSE) estimation, often require second-order statistics that are difficult to obtain in practice. Recent deep neural network (DNN)-based methods have been introduced to address this, but they often suffer from high inference complexity. This paper proposes an Attention-aided MMSE (A-MMSE), a model-based DNN framework that learns the linear MMSE filter via the Attention Transformer. Once trained, the A-MMSE performs channel estimation through a single linear operation, eliminating nonlinear activations during inference and thus reducing computational complexity. To improve the learning efficiency of the A-MMSE, we develop a two-stage Attention encoder that captures the frequency and temporal correlation structure of OFDM channels. We also introduce a rank-adaptive extension that adjusts the filter rank at deployment time, enabling efficient operation under resource-constrained receivers. Numerical simulations show that A-MMSE consistently outperforms baseline methods across a wide range of signal-to-noise ratio (SNR) conditions. In particular, the A-MMSE and its rank-adaptive extension provide an improved performance-complexity trade-off.

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arXiv2025

Multibeam Satellite Communications with Massive MIMO: Asymptotic Performance Analysis and Design Insights

To achieve high performance without substantial overheads associated with channel state information (CSI) of ground users, we consider a fixed-beam precoding approach, where a satellite forms multiple fixed-beams without relying on CSI, then select a suitable user set for each beam. Upon this precoding method, we put forth a satellite equipped with massive multiple-input multiple-output (MIMO), by which inter-beam interference is efficiently mitigated by narrowing corresponding beam width. By modeling the ground users' locations via a Poisson point process, we rigorously analyze the achievable performance of the presented multibeam satellite system. In particular, we investigate the asymptotic scaling laws that reveal the interplay between the user density, the number of beams, and the number of antennas. Our analysis offers critical design insights for the multibeam satellite with massive MIMO: i) If the user density scales in power with the number of antennas, the considered precoding can achieve a linear fraction of the optimal rate in the asymptotic regime. ii) A certain additional scaling factor for the user density is needed as the number of beams increases to maintain the asymptotic optimality.

cs.IT↗