arXiv subjects

Hyeonho Noh

Publications and source records attributed to Hyeonho Noh.

15 recordsLinked to original sources

arXiv2026

Near-Field Localization Beyond Bandwidth Limits for Large-Aperture Pinching-Antenna Systems

Conventional bandwidth-limited ranging resolves delay on the scale of $c_0/(2B)$, yet a large distributed aperture can support substantially finer localization through near-field carrier-phase diversity. This letter characterizes the coherent main-lobe width of the localization likelihood for pinching-antenna systems and shows that it is governed by the spread of the direction cosines subtended by the aperture at the target. The resulting expression recovers classical far-field angular and Fresnel-range scalings and reduces to a wavelength-scale cell when the aperture subtends a large solid angle. Guided by this characterization, a hierarchical maximum-likelihood search is developed, combining noncoherent coarse localization, coherent lobe selection, and local refinement. Numerical results under scattering demonstrate submillimeter localization above a threshold signal-to-noise ratio without localization failures in the tested ensemble.

eess.SP↗

arXiv2026

Multimodal Large Language Model-guided Constrained Optimization for RAN Intelligent Control

Artificial intelligence (AI)-based radio access network (RAN) controllers are commonly designed for predefined operating scenarios and optimization tasks, limiting their adaptability when network conditions and operator requirements change after deployment. This paper proposes multimodal large language model (MLLM)-guided constrained optimization for RAN intelligent control (MLLM-coRIC), a requirement-adaptive hierarchical Open RAN (O-RAN) framework for joint resource and power allocation. MLLM-coRIC jointly exploits the operator's natural-language specification and radio-frequency (RF)-derived network context as multimodal inputs, enabling a single deployed framework to address different optimization problems without redesigning task-specific algorithms or retraining control policies. At the Non-Real-Time RAN Intelligent Controller (Non-RT RIC), the MLLM performs holistic, longer-timescale reasoning over the operator requirement, predicted network evolution, and measured optimization outcomes to design the numerical control loss. The relative constraint penalties are iteratively refined through a closed-loop process, allowing the optimization criterion to reflect both the intended network behavior and the expected operating context. At the Near-Real-Time RIC (Near-RT RIC), a loss-conditioned hybrid executor translates the synthesized criterion into fast radio actions by combining learned ramp-constrained resource allocation with model-based interference-aware power control. A multi-cell evaluation environment integrating the CARLA urban mobility simulator and the Sionna RT ray-tracing-based wireless propagation simulator is implemented for validation.

eess.SY↗

arXiv2026

MROP: Mask-Region Optimized Purification Against Backdoor Attack in Deep JSCC

Deep joint source and channel coding (JSCC) transmits a source by mapping it directly to channel symbols through an end-to-end deep neural network (DNN) and reconstructing it at the receiver. Taking image transmission as an application, this DNN pipeline behaves as a black box: the receiver cannot readily detect security attacks when the transmitted images are corrupted, thereby introducing a new security vulnerability. In this letter, we study defense against input-patch backdoor attacks on deep JSCC, in which a small trigger patch attached to the input forces the decoder to emit an attacker-chosen target image. Most existing patch-trigger defenses are designed for classification, leaving the reconstruction setting of deep JSCC unaddressed. We adapt the gradient mask defense to this reconstruction setting as a baseline and then propose mask-region optimized purification (MROP), which operates at inference and requires no retraining of the JSCC model. Unlike the baseline, which localizes the trigger from the input--output gradient, MROP instead places a per-pixel mask at the encoder input and optimizes it via a Gumbel-sigmoid relaxation to localize the trigger, then refines the trigger region to reconstruct the pure images better. In numerical results, we evaluate the proposed method on CIFAR-10 and STL-10 datasets along with the DeepJSCC and SwinJSCC models. By doing so, we show that the proposed method substantially lowers the attack success rate (ASR) while preserving the peak signal-to-noise ratio (PSNR) of clean reconstructions.

cs.CR↗

arXiv2026

IMNet: Intercarrier Interference Mitigation Network for Integrated Sensing and Communication in Spectrally Efficient FDM Systems

Spectrally efficient frequency-division multiplexing (SEFDM) is an attractive waveform to improve communication spectral efficiency by compressing the subcarrier spacing, yet its use for integrated sensing and communication (ISAC) poses a fundamental sensing challenge. Specifically, the intentional loss of subcarrier orthogonality generates SEFDM-induced intercarrier interference (S-ICI), which combines with Doppler-induced ICI (D-ICI) from moving targets to blur range--velocity maps and severely degrade sensing accuracy. Building on multi-user multi-input-multi-output (MIMO) SEFDM systems, this paper develops a model-driven ISAC framework that supports spectrally efficient multi-user communication while mitigating both S-ICI and D-ICI in sensing. To this end, an intercarrier interference mitigation network (IMNet) is proposed, which exploits the distinct physical structures of the two interferences. A bank of Doppler correction filters first compensates the velocity-dependent D-ICI over multiple Doppler hypotheses, and an axial-attention network subsequently suppresses the residual D-ICI and the long-range S-ICI to recover reliable sensing signals. To further improve range and velocity estimation accuracy, IMNet with local refinement (IMNet-LR) is proposed, which performs maximum-likelihood refinement with nuisance projection around the IMNet detections to achieve sub-cell precision without an exhaustive global search. Simulation results show that IMNet-LR achieves near-maximum-likelihood range and velocity estimation accuracy with more than three orders of magnitude lower execution time compared to conventional detection methods.

eess.SP↗

arXiv2026

Dispersion-aware Localization Network for Wideband OFDM Pinching-Antenna Integrated Sensing and Communication Systems

Pinching-antenna systems (PASS) provide a large effective aperture and substantial path-loss reduction at low hardware cost, making them attractive for integrated sensing and communication (ISAC). Under wideband OFDM operation, however, the antennas on each waveguide impose nonlinear, position-dependent group delays on the same baseband signal, giving rise to waveguide dispersion that severely degrades range estimation. To this end, this paper proposes a two-stage ISAC framework comprising communication-aware beamforming and antenna placement followed by a dispersion-aware target localization stage. A fractional-programming beamformer and an element-wise coordinate-descent placement jointly maximize the downlink sum rate under a sensing beampattern-gain constraint, and the resulting optimized beamformer and placement determine the effective sensing channel used by the localization stage. The proposed DisPersion-aware Localization Network (DiPL-Net) detects targets from the received signal via a dispersion-aware score map built on a physics-derived range dictionary. Its convolutional backbone employs dual-kernel residual blocks, each pairing a short kernel matched to the OFDM main lobe with a long kernel matched to the dispersion tail, so as to deconvolve the dispersion and restore a sharp target peak at each true target. Simulation results show substantial localization gains over various sensing baselines while preserving the achievable communication rate.

eess.SP↗

arXiv2026

α-Fair Multistatic ISAC Beamforming for Multi-User MIMO-OFDM Systems via Riemannian Optimization

This paper proposes an $α$-fair multistatic integrated sensing and communication (ISAC) framework for multi-user multi-input multi-output (MIMO)-orthogonal frequency division multiplexing (OFDM) systems, where communication users act as passive bistatic receivers to enable multistatic sensing. Unlike existing works that optimize aggregate sensing metrics and thus favor geometrically advantageous targets, we minimize the $α$-fairness utility over per-target Cramér--Rao lower bounds (CRLBs) subject to per-user minimum data rate and transmit power constraints. The resulting non-convex problem is solved via the Riemannian conjugate gradient (RCG) method with a smooth penalty reformulation. Simulation results validate the effectiveness of the proposed scheme in achieving a favorable sensing fairness--communication trade-off.

cs.IT↗

arXiv2026

ALERT Open Dataset and Input-Size-Agnostic Vision Transformer for Driver Activity Recognition using IR-UWB

Distracted driving contributes to fatal crashes worldwide. To address this, researchers are using driver activity recognition (DAR) with impulse radio ultra-wideband (IR-UWB) radar, which offers advantages such as interference resistance, low power consumption, and privacy preservation. However, two challenges limit its adoption: the lack of large-scale real-world UWB datasets covering diverse distracted driving behaviors, and the difficulty of adapting fixed-input Vision Transformers (ViTs) to UWB radar data with non-standard dimensions. This work addresses both challenges. We present the ALERT dataset, which contains 10,220 radar samples of seven distracted driving activities collected in real driving conditions. We also propose the input-size-agnostic Vision Transformer (ISA-ViT), a framework designed for radar-based DAR. The proposed method resizes UWB data to meet ViT input requirements while preserving radar-specific information such as Doppler shifts and phase characteristics. By adjusting patch configurations and leveraging pre-trained positional embedding vectors (PEVs), ISA-ViT overcomes the limitations of naive resizing approaches. In addition, a domain fusion strategy combines range- and frequency-domain features to further improve classification performance. Comprehensive experiments demonstrate that ISA-ViT achieves a 22.68% accuracy improvement over an existing ViT-based approach for UWB-based DAR. By publicly releasing the ALERT dataset and detailing our input-size-agnostic strategy, this work facilitates the development of more robust and scalable distracted driving detection systems for real-world deployment.

cs.CV↗

arXiv2025

Group-wise Semantic Splitting Multiple Access for Multi-User Semantic Communication

In this letter, we propose a group-wise semantic splitting multiple access framework for multi-user semantic communication in downlink scenarios. The framework begins by applying a balanced clustering mechanism that groups users based on the similarity of their semantic characteristics, enabling the extraction of group-level common features and user-specific private features. The base station then transmits the common features via multicast and the private features via unicast, effectively leveraging both shared and user-dependent semantic information. To further enhance semantic separability and reconstruction fidelity, we design a composite loss function that integrates a reconstruction loss with a repulsion loss, improving both the accuracy of semantic recovery and the distinctiveness of common embeddings in the latent space. Simulation results demonstrate that the proposed method achieves up to 3.26x performance improvement over conventional schemes across various channel conditions, validating its robustness and semantic efficiency for next-generation wireless networks.

eess.SP↗

arXiv2025

Large Multimodal Models-Empowered Task-Oriented Autonomous Communications: Design Methodology and Implementation Challenges

Large language models (LLMs) and large multimodal models (LMMs) have achieved unprecedented breakthrough, showcasing remarkable capabilities in natural language understanding, generation, and complex reasoning. This transformative potential has positioned them as key enablers for 6G autonomous communications among machines, vehicles, and humanoids. In this article, we provide an overview of task-oriented autonomous communications with LLMs/LMMs, focusing on multimodal sensing integration, adaptive reconfiguration, and prompt/fine-tuning strategies for wireless tasks. We demonstrate the framework through three case studies: LMM-based traffic control, LLM-based robot scheduling, and LMM-based environment-aware channel estimation. From experimental results, we show that the proposed LLM/LMM-aided autonomous systems significantly outperform conventional and discriminative deep learning (DL) model-based techniques, maintaining robustness under dynamic objectives, varying input parameters, and heterogeneous multimodal conditions where conventional static optimization degrades.

cs.LG↗

arXiv2025

Secure Multi-Hop Relaying in Large-Scale Space-Air-Ground-Sea Integrated Networks

As a key enabler of borderless and ubiquitous connectivity, space-air-ground-sea integrated networks (SAGSINs) are expected to be a cornerstone of 6G wireless communications. However, the multi-tiered and global-scale nature of SAGSINs also amplifies the security vulnerabilities, particularly due to the hidden, passive eavesdroppers distributed throughout the network. In this paper, we introduce a joint optimization framework for multi-hop relaying in SAGSINs that maximizes the minimum user throughput while ensuring a minimum strictly positive secure connection (SPSC) probability. We first derive a closed-form expression for the SPSC probability and incorporate this into a cross-layer optimization framework that jointly optimizes radio resources and relay routes. Specifically, we propose an $\mathcal{O}(1)$ optimal frequency allocation and power splitting strategy-dividing power levels of data transmission and cooperative jamming. We then introduce a Monte-Carlo relay routing algorithm that closely approaches the performance of the numerical upper-bound method. We validate our framework on testbeds built with real-world dataset.

eess.SP↗

arXiv2025

Multiple Active STAR-RIS-Assisted Secure Integrated Sensing and Communication via Cooperative Beamforming

This paper explores an integrated sensing and communication (ISAC) network empowered by multiple active simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs). A base station (BS) furnishes downlink communication to multiple users while concurrently interrogating a sensing target. We jointly optimize the BS transmit beamformer and the reflection/transmission coefficients of every active STAR-RIS in order to maximize the aggregate communication sum-rate, subject to (i) a stringent sensing signal-to-interference-plus-noise ratio (SINR) requirement, (ii) an upper bound on the leakage of confidential information, and (iii) individual hardware and total power constraints at both the BS and the STAR-RISs. The resulting highly non-convex program is tackled with an efficient alternating optimization (AO) framework. First, the original formulation is reformulated into an equivalent yet more tractable representation and partitioned into subproblems. The BS beamformer is updated in closed form via the Karush-Kuhn-Tucker (KKT) conditions, whereas the STAR-RIS reflection and transmission vectors are refined through successive convex approximation (SCA), yielding a semidefinite program that is then solved via semidefinite relaxation. Comprehensive simulations demonstrate that the proposed algorithm delivers substantial sum-rate gains over passive-RIS and single STAR-RIS baselines, all the while rigorously meeting the prescribed sensing and security constraints.

eess.SP↗

arXiv2025

DCFNet: Doppler Correction Filter Network for Integrated Sensing and Communication in Multi-User MIMO-OFDM Systems

Integrated sensing and communication (ISAC) is a headline feature for the forthcoming IMT-2030 and 6G releases, yet a concrete solution that fits within the established orthogonal frequency division multiplexing (OFDM) family remains open. Specifically, Doppler-induced inter-carrier interference (ICI) destroys sub-carrier orthogonality of OFDM sensing signals, blurring range-velocity maps and severely degrading sensing accuracy. Building on multi-user multi-input-multi-output (MIMO) OFDM systems, this paper proposes Doppler-Correction Filter Network (DCFNet), an AI-native ISAC model that delivers fine range-velocity resolution at minimal complexity without altering the legacy frame structure. A bank of DCFs first shifts dominant ICI energy away from critical Doppler bins; a compact deep learning network then suppresses the ICI. To further enhance the range and velocity resolutions, we propose DCFNet with local refinement (DCFNet-LR), which applies a generalized likelihood ratio test (GLRT) to refine target estimates of DCFNet to sub-cell accuracy. Simulation results show that DCFNet-LR runs $143\times$ faster than maximum likelihood search and achieves significantly superior performance, reducing the range RMSE by up to $2.7 \times 10^{-4}$ times and the velocity RMSE by $6.7 \times 10^{-4}$ times compared to conventional detection methods.

eess.SP↗

arXiv2025

Robust Transmission of Punctured Text with Large Language Model-based Recovery

With the recent advancements in deep learning, semantic communication which transmits only task-oriented features, has rapidly emerged. However, since feature extraction relies on learning-based models, its performance fundamentally depends on the training dataset or tasks. For practical scenarios, it is essential to design a model that demonstrates robust performance regardless of dataset or tasks. In this correspondence, we propose a novel text transmission model that selects and transmits only a few characters and recovers the missing characters at the receiver using a large language model (LLM). Additionally, we propose a novel importance character extractor (ICE), which selects transmitted characters to enhance LLM recovery performance. Simulations demonstrate that the proposed filter selection by ICE outperforms random filter selection, which selects transmitted characters randomly. Moreover, the proposed model exhibits robust performance across different datasets and tasks and outperforms traditional bit-based communication in low signal-to-noise ratio conditions.

eess.SP↗

arXiv2025

Adaptive Resource Allocation Optimization Using Large Language Models in Dynamic Wireless Environments

Deep learning (DL) has made notable progress in addressing complex radio access network control challenges that conventional analytic methods have struggled to solve. However, DL has shown limitations in solving constrained NP-hard problems often encountered in network optimization, such as those involving quality of service (QoS) or discrete variables like user indices. Current solutions rely on domain-specific architectures or heuristic techniques, and a general DL approach for constrained optimization remains undeveloped. Moreover, even minor changes in communication objectives demand time-consuming retraining, limiting their adaptability to dynamic environments where task objectives, constraints, environmental factors, and communication scenarios frequently change. To address these challenges, we propose a large language model for resource allocation optimizer (LLM-RAO), a novel approach that harnesses the capabilities of LLMs to address the complex resource allocation problem while adhering to QoS constraints. By employing a prompt-based tuning strategy to flexibly convey ever-changing task descriptions and requirements to the LLM, LLM-RAO demonstrates robust performance and seamless adaptability in dynamic environments without requiring extensive retraining. Simulation results reveal that LLM-RAO achieves up to a 40% performance enhancement compared to conventional DL methods and up to an $80$\% improvement over analytical approaches. Moreover, in scenarios with fluctuating communication objectives, LLM-RAO attains up to 2.9 times the performance of traditional DL-based networks.

eess.SY↗

arXiv2024

Joint Optimization on Uplink OFDMA and MU-MIMO for IEEE 802.11ax: Deep Hierarchical Reinforcement Learning Approach

This letter tackles a joint user scheduling, frequency resource allocation (USRA), multi-input-multi-output mode selection (MIMO MS) between single-user MIMO and multi-user (MU) MIMO, and MU-MIMO user selection problem, integrating uplink orthogonal frequency division multiple access (OFDMA) in IEEE 802.11ax. Specifically, we focus on \textit{unsaturated traffic conditions} where users' data demands fluctuate. In unsaturated traffic conditions, considering packet volumes per user introduces a combinatorial problem, requiring the simultaneous optimization of MU-MIMO user selection and RA along the time-frequency-space axis. Consequently, dealing with the combinatorial nature of this problem, characterized by a large cardinality of unknown variables, poses a challenge that conventional optimization methods find nearly impossible to address. In response, this letter proposes an approach with deep hierarchical reinforcement learning (DHRL) to solve the joint problem. Rather than simply adopting off-the-shelf DHRL, we \textit{tailor} the DHRL to the joint USRA and MS problem, thereby significantly improving the convergence speed and throughput. Extensive simulation results show that the proposed algorithm achieves significantly improved throughput compared to the existing schemes under various unsaturated traffic conditions.

eess.SY↗