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Yo-Seb Jeon

Publications and source records attributed to Yo-Seb Jeon.

At least 19 recordsLinked to original sources
arXiv2026

On the Performance of AoA-Based Physical-Layer Authentication Under Multi-Source Impersonation Attacks

This paper investigates cooperative impersonation attacks against angle-of-arrival (AoA)-based authentication for a wireless communication receiver equipped with a digital uniform linear array. Specifically, we characterize the vulnerability and robustness of this authentication system in relation to adversarial angular diversity and the relative numbers of adversarial sources and receive antennas. To this end, we first derive the optimal precoding vector for multiple spatially distributed adversarial sources by minimizing the mean squared error between legitimate and adversarial received signals. By means of the rank property of Vandermonde matrices, we then establish necessary and sufficient conditions under which the adversarial sources can reproduce the legitimate transmitter's spatial signature. With fewer adversarial sources than receive antennas, perfect reproduction is not possible without at least one adversarial source satisfying a specific AoA condition. In the other case, increasing the number of receive antennas can reduce impersonation success probabilities when none of the adversarial sources has an AoA satisfying this condition. Numerical results validate the theoretical analysis and evaluate the security performance of AoA-based authentication.

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arXiv2026

Unequal Error Protection for Digital Semantic Communication with Channel Coding

This paper investigates unequal error protection (UEP) in digital semantic communication, where semantically important bits require substantially higher reliability than less critical ones. To characterize this heterogeneity, we introduce a novel perspective that treats learned bit-flip probabilities of semantic bits as target error protection levels, thereby directly linking semantic importance to bit-level reliability. This formulation reveals that the required protection levels of the semantic bits may differ by several orders of magnitude, making short-block coding more advantageous than conventional long-block designs. Motivated by this, we develop two UEP frameworks that minimize total blocklength under heterogeneous reliability constraints. First, we propose a bit-level UEP framework based on repetition coding, providing an analytically tractable solution that precisely meets per-bit protection requirements. Second, to improve energy and blocklength efficiency, we design a block-level UEP framework in which the semantic bits are partitioned into short blocks with similar protection levels. Guided by finite blocklength capacity analysis, we derive a closed-form threshold condition for beneficial partitioning and develop a systematic algorithm for integrating modern channel codes. Simulation results on image transmission tasks demonstrate substantial gains in both task performance and transmission efficiency compared with conventional equal-protection schemes.

cs.IT↗
arXiv2026

Semantic Feature Channel Optimization: A Unified Framework for Analog and Digital Semantic Communications

Semantic communication (SC) aims to improve communication efficiency by transmitting only task-relevant information, termed semantic features (SFs). Most existing SC frameworks achieve this by optimizing the encoder and decoder while treating the channel between them as fixed. In this paper, we introduce a new perspective by explicitly modeling the entire process from the encoder output to the decoder input as an SF channel, resulting in an encoder-SF channel-decoder pipeline. We observe that the SF channel is configurable through transceiver operations, such as power allocation, thereby providing an additional degree of freedom for improving task performance. Inspired by this, we formulate a joint optimization problem for the encoder, SF channel, and decoder under a mutual information constraint between the transmitted and reconstructed SFs. To provide analytical insight, we derive the optimal SF channel in closed form for an analytically tractable setting. Based on this formulation, we develop a unified semantic feature channel optimization framework applicable to both analog and digital SC systems. To realize the SF channel in general communication systems, we further propose a physical-layer calibration strategy that aligns the actual SF channel with the trained one. Simulation results demonstrate that the proposed framework consistently improves task performance across various communication environments.

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arXiv2026

KV-COBRA: KV Cache Compression via Co-Optimized Bit-Rank Allocation

What limits KV-cache compression at extreme bit-rates? We argue that it is not the choice of compression scheme, but how its budget is allocated across attention heads. Existing methods apply rank and bit-width uniformly, ignoring that each head has a different optimal mix of rank truncation and quantization. We show that co-optimizing rank and bit-width per head, using only standard low-rank projection and scalar quantization, dominates uniform allocation, with the largest gains at low bit-rates. Our method, KV-COBRA (Co-Optimized Bit-Rank Allocation), formalizes this as a resource-allocation problem: it balances rank-truncation loss against quantization loss within each head, then redistributes budget across heads to minimize total distortion. A fused Hadamard rotation equalizes per-channel variance, and reordering the SVD basis by attention-KL importance makes the solver query-aware. The same allocator extends to joint $K{+}V$ compression. On perplexity, zero-shot, and long-context benchmarks from $0.5$ to $4$ bits per dimension (bpd), KV-COBRA shows the smallest accuracy degradation among evaluated methods at low bpd, with no per-token overhead.

cs.LG↗
arXiv2026

Token-Oriented Semantic Communication with Pretrained Vision Transformers

Token communications realize the semantic communication principle at the granularity of transformer tokens, providing a promising direction for client--server collaborative inference in resource-constrained edge systems. However, directly transmitting token embeddings presents two practical challenges: substantial communication cost and limited interoperability across model-specific token embedding spaces. To address these challenges, we propose a \emph{token-oriented} semantic communication framework. In this framework, token-level task relevance determines which compressed image latents are transmitted, enabling token-granular transmission without directly transmitting token embeddings. The framework is modular, coordinating three pretrained components---a lightweight client-side vision transformer (ViT), a learned image compression (LIC) model, and a large server-side ViT---without end-to-end training. The key enabler is the one-to-one spatial alignment between ViT patch tokens and the LIC latent vectors, which allows token-level task relevance to directly determine which latent vectors are transmitted. Building on this alignment, token-aligned LIC selectively transmits task-relevant latents, layer-selective attention rollout estimates token relevance from a selected range of attention layers in a single forward pass, and surrogate token substitution adapts the frozen server model by optimizing a single learnable token. Experiments on ImageNet show that the proposed framework achieves a more favorable rate--accuracy trade-off than recent semantic communication schemes, hand-crafted codecs, and task-agnostic LIC models.

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arXiv2026

Rethinking the Foundations of Two-Sided AI Models for 6G

For next-generation air interfaces, two-sided artificial intelligence (AI) models have received growing attention, with AI models deployed at both the transmitter and receiver for efficient channel feedback and data communication. However, their practical deployment is complicated by assumptions commonly made in existing studies, including isolation from legacy users, training under predefined channel conditions, and gradient-based fine-tuning requiring substantial cross-vendor communication. This article revisits these assumptions and presents practical alternatives. First, for legacy coexistence, we integrate two-sided model processing into the 5G New Radio (NR) protocol stack and validate its operation alongside conventional NR on a real-world testbed. Second, instead of training under a massive number of predefined channel conditions, we construct a compact model table by jointly optimizing two-sided models with trainable surrogate channels, and select the best model according to the current channel condition to enable channel adaptation with high task performance and low training/storage overhead. Finally, unlike existing fine-tuning that exchanges large gradient vectors containing potentially private model information, we present gradient-free zeroth-order fine-tuning that requires only scalar feedback, facilitating multi-vendor interoperability. Together, these approaches advance the practical deployment of two-sided AI models while highlighting key open challenges.

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arXiv2026

Anti-Jamming Modulation for OFDM Systems under Jamming Attacks

Orthogonal frequency division multiplexing (OFDM) systems are inherently vulnerable to jamming attacks due to the independent transmission of data symbols across subcarriers. In this paper, we propose a novel anti-jamming OFDM scheme to ensure robust communication even under severe jamming attacks while maintaining high spectral efficiency. The core idea is to utilize a spreading matrix that transforms a data symbol vector into a higher-dimensional modulated vector, thereby exploiting both the spreading gain and the frequency diversity gain to mitigate jamming attacks. To recover the transmitted data symbols, we develop an efficient maximum likelihood detection (MLD) method that achieves optimal detection performance with significantly reduced computational complexity. Furthermore, we derive the theoretical bit error rate (BER) upper bound and the optimal modulation order that minimizes the BER while preserving spectral efficiency according to the jamming environment. To address practical scenarios where jamming attacks are unknown and dynamic, we establish a jamming-adaptive communication framework. This framework enables the system to estimate the jamming parameters and adapt to the dynamic environment with the optimal modulation order. Simulation results demonstrate that the proposed scheme significantly outperforms existing OFDM schemes in both BER and effective throughput, validating its robustness under various and dynamic jamming scenarios.

cs.IT↗
arXiv2026

Cooperative Multi-Satellite ISAC Networks: Centralized vs. Distributed Sensing

This paper investigates a downlink multi-satellite integrated sensing and communication (ISAC) network, in which multiple satellites simultaneously transmit ISAC signals to provide communication services to ground user equipments and enable cooperative sensing of airborne targets through multiple gateways. To support this dual functionality, we introduce communication and sensing beamforming designs based on uniform planar arrays with optimized power allocation. Building on these designs, we propose two cooperative sensing frameworks, namely centralized and distributed. In the centralized framework, each gateway forwards its sensing observations to a central unit (CU), where the positions of multiple targets are jointly estimated from the aggregated data. To mitigate the signaling overhead inherent in centralized processing, a distributed framework is further proposed, in which each gateway independently estimates target positions and transmits only the local estimates to the CU. To associate estimates from different gateways, a data association problem based on the squared Euclidean distance is formulated and efficiently solved using the Hungarian algorithm. The final target positions are then obtained by minimizing the distance error. Simulation results demonstrate that the proposed centralized and distributed frameworks significantly outperform existing sensing schemes while satisfying communication performance requirements. Finally, we evaluate the sensing-communication trade-off from the viewpoints of sensing accuracy and communication power consumption under the proposed frameworks.

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arXiv2026

Geometric Cross-Modal Token Selection for Latency-Constrained Multimodal Token Communication

This paper proposes a geometry-based joint cross-modal token selection framework for latency-constrained multimodal token communications. To capture cross-modal token dependencies, we leverage the cross-attention mechanism to project modality-specific tokens into a shared query-key space, where the modality with the fewest tokens serves as the anchor modality and the others as non-anchor modalities. Inspired by germ-grain models, we define an angular-distance metric and construct semantic grain regions around anchor queries. Based on this geometric representation, we identify cross-modal evidence shared across multiple anchor queries in this space and develop an intersection-based token selection (IBS) strategy that prioritizes non-anchor tokens whose key are covered by multiple grain regions. We further develop an erasure-aware extension, termed robust-IBS (R-IBS), for token-wise erasure channels using an expected angular-distance formulation. In both IBS and R-IBS, the grain regions are optimized for individual queries under a latency constraint, using block coordinate descent and a low-complexity greedy algorithm. Simulations corroborate the effectiveness of IBS and R-IBS under latency-constrained and token-wise erasure channels, achieving up to 31.6% and 29.2% task accuracy gains on visual question answering (VQA) and audio-visual question answering (AVQA) tasks, respectively, over existing token selection baselines.

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arXiv2026

Context-Aware Wireless Token Communication via Joint Token Masking and Detection

The increasing use of token-based representations in language-driven applications has motivated wireless token communication, where tokens are treated as fundamental units for transmission. However, conventional communication systems overlook dependencies among tokens and allocate transmission resources uniformly, leading to inefficient use of limited wireless resources under channel impairments. In this paper, we propose a context-aware token communication framework that leverages a masked language model (MLM) as a shared contextual model between the transmitter (Tx) and receiver (Rx). At the Rx, we develop a context-aware token detection method that integrates channel likelihoods with MLM-based contextual priors under a Bayesian formulation, enabling robust token inference over noisy channels. At the Tx, we propose a context-aware token masking strategy that selectively omits tokens that can be reliably inferred at the Rx, allowing the available power budget to be concentrated on more informative tokens. These components are jointly designed through a shared MLM, establishing a unified Tx-Rx framework for efficient token transmission and detection. Simulation results demonstrate that the proposed framework significantly improves reconstruction performance compared to conventional and existing token communication schemes, achieving up to 1.77X and 1.63X performance gains on the Europarl corpus and WikiText-103 datasets, respectively.

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arXiv2026

Robust Nonlinear Transform Coding: A Framework for Generalizable Joint Source-Channel Coding

This paper proposes robust nonlinear transform coding (Robust-NTC), a generalizable digital joint source-channel coding (JSCC) framework that couples variational latent modeling with channel-adaptive transmission. Unlike learning-based JSCC methods that implicitly absorb channel variations, Robust-NTC explicitly models element-wise latent distributions via a variational objective with a Gaussian proxy for quantization and channel noise, allowing encoder-decoder to capture latent uncertainty without channel-specific training. Using the learned statistics, Robust-NTC also facilitates rate-distortion optimization to adaptively select element-wise quantizers and bit depths according to online channel conditions. To support practical deployment, Robust-NTC is integrated into an orthogonal frequency-division multiplexing (OFDM) system, where a unified resource allocation framework jointly optimizes latent quantization, bit allocation, modulation order, and power allocation to minimize transmission latency while guaranteeing learned distortion targets. Simulation results demonstrate that for practical OFDM systems, Robust-NTC achieves superior rate-distortion efficiency and stable reconstruction fidelity compared to both a conventional separated coding scheme and digital JSCC baselines across various channel conditions.

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arXiv2026

Context-Aware Iterative Token Detection and Masked Transmission for Wireless Token Communication

The success of large-scale language models has established tokens as compact and meaningful units for natural-language representation, which motivates token communication over wireless channels, where tokens are considered fundamental units for wireless transmission. We propose a context-aware token communication framework that uses a pretrained masked language model (MLM) as a shared contextual probability model between the transmitter (Tx) and receiver (Rx). At Rx, we develop an iterative token detection method that jointly exploits MLM-guided contextual priors and channel observations based on a Bayesian perspective. At Tx, we additionally introduce a context-aware masking strategy which skips highly predictable token transmission to reduce transmission rate. Simulation results demonstrate that the proposed framework substantially improves reconstructed sentence quality and supports effective rate adaptation under various channel conditions.

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arXiv2025

Beam-Squint-Aided Hierarchical Sensing for Integrated Sensing and Communications with Uniform Planar Arrays

In this paper, we propose a novel hierarchical sensing framework for wideband integrated sensing and communications with uniform planar arrays (UPAs). Leveraging the beam-squint effect inherent in wideband orthogonal frequency-division multiplexing (OFDM) systems, the proposed framework enables efficient two-dimensional angle estimation through a structured multi-stage sensing process. Specifically, the sensing procedure first searches over the elevation angle domain, followed by a dedicated search over the azimuth angle domain given the estimated elevation angles. In each stage, true-time-delay lines and phase shifters of the UPA are jointly configured to cover multiple grid points simultaneously across OFDM subcarriers. To enable accurate and efficient target localization, we formulate the angle estimation problem as a sparse signal recovery problem and develop a modified matching pursuit algorithm tailored to the hierarchical sensing architecture. Additionally, we design power allocation strategies that minimize total transmit power while meeting performance requirements for both sensing and communication. Numerical results demonstrate that the proposed framework achieves superior performance over conventional sensing methods with reduced sensing power.

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arXiv2025

Rate-Adaptive Semantic Communication via Multi-Stage Vector Quantization

This paper proposes a novel framework for rate-adaptive semantic communication based on multi-stage vector quantization (VQ), termed \textit{MSVQ-SC}. Unlike conventional single-stage VQ approaches, which require exponentially larger codebooks to achieve higher fidelity, the proposed framework decomposes the quantization process into multiple stages and dynamically activates both stages and individual VQ modules. This design enables fine-grained rate adaptation under varying bit constraints while mitigating computational complexity and the codebook collapse problem. To optimize performance, we formulate a module selection problem that minimizes task loss subject to a rate constraint and solve it using an incremental allocation algorithm. Furthermore, we extend the framework by incorporating entropy coding to exploit non-uniform codeword distributions, further reducing communication overhead. Simulation results on the CIFAR-10 dataset demonstrate that the proposed framework outperforms existing digital semantic communication methods, achieving superior semantic fidelity with lower complexity while providing flexible and fine-grained rate control.

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arXiv2025

Online Data Generation for MIMO-OFDM Channel Denoising: Transfer Learning vs. Meta Learning

Channel denoising is a practical and effective technique for mitigating channel estimation errors in multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems. However, adapting denoising techniques to varying channel conditions typically requires prior knowledge or incurs significant training overhead. To address these challenges, we propose a standard-compatible strategy for generating online training data that enables online adaptive channel denoising. The key idea is to leverage high-quality channel estimates obtained via data-aided channel estimation as practical substitutes for unavailable ground-truth channels. Our data-aided method exploits adjacent detected data symbols within a specific time-frequency neighborhood as virtual reference signals, and we analytically derive the optimal size of this neighborhood to minimize the mean squared error of the resulting estimates. By leveraging the proposed strategy, we devise two channel denoising approaches, one based on transfer learning, which fine-tunes a pre-trained denoising neural network, and the other based on meta learning, which rapidly adapts to new channel environments with minimal updates. Simulation results demonstrate that the proposed methods effectively adapt to dynamic channel conditions and significantly reduce channel estimation errors compared to conventional techniques.

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arXiv2025

Importance-Aware Semantic Communication in MIMO-OFDM Systems Using Vision Transformer

This paper presents a novel importance-aware quantization, subcarrier mapping, and power allocation (IA-QSMPA) framework for semantic communication in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, empowered by a pretrained Vision Transformer (ViT). The proposed framework exploits attention-based importance extracted from a pretrained ViT to jointly optimize quantization levels, subcarrier mapping, and power allocation. Specifically, IA-QSMPA maps semantically important features to high-quality subchannels and allocates resources in accordance with their contribution to task performance and communication latency. To efficiently solve the resulting nonconvex optimization problem, a block coordinate descent algorithm is employed. The framework is further extended to operate under finite blocklength transmission, where communication errors may occur. In this setting, a segment-wise linear approximation of the channel dispersion penalty is introduced to enable efficient joint optimization under practical constraints. Simulation results on a multi-view image classification task using the MVP-N dataset demonstrate that IA-QSMPA significantly outperforms conventional methods in both ideal and finite blocklength transmission scenarios, achieving superior task performance and communication efficiency.

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arXiv2025

Beam-Hopping Pattern Design for Grant-Free Random Access in LEO Satellite Communications

Increasing demand for massive device connectivity in underserved regions drives the development of advanced low Earth orbit (LEO) satellite communication systems. Beam-hopping LEO systems without connection establishment provide a promising solution for achieving both demand-aware resource allocation and low access latency. This paper investigates beam-hopping pattern design for the grant-free random access systems to dynamically allocate satellite resources according to traffic demands across serving cells. We formulate a binary optimization problem that aims to maximize the minimum successful transmission probability across cells, given limited satellite beam generation capacity. To solve this problem, we propose novel beam-hopping design algorithms that alternately enhance the collision avoidance rate and decoding success probability within an alternating optimization framework. Specifically, the algorithms employ a bisection method to optimize illumination allocation for each cell based on demand, while using the alternating direction method of multipliers (ADMM) to optimize beam-hopping patterns for maximizing decoding success probability. Furthermore, we enhance the ADMM by replacing the strict binary constraint with two equivalent continuous-valued constraints. Simulation results demonstrate the superiority of the proposed algorithms compared to other beam-hopping methods and verify robustness in managing traffic demand imbalance.

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arXiv2025

Deep Learning-Based CSI Feedback for Wi-Fi Systems With Temporal Correlation

To achieve higher throughput in next-generation Wi-Fi systems, a station (STA) needs to efficiently compress channel state information (CSI) and feed it back to an access point (AP). In this paper, we propose a novel deep learning (DL)-based CSI feedback framework tailored for next-generation Wi-Fi systems. Our framework incorporates a pair of encoder and decoder neural networks to compress and reconstruct the angle parameters of the CSI. To enable an efficient finite-bit representation of the encoder output, we introduce a trainable vector quantization module, which is integrated after the encoder network and jointly trained with both the encoder and decoder networks in an end-to-end manner. Additionally, we further enhance our framework by leveraging the temporal correlation of the angle parameters. Specifically, we propose an angle-difference feedback strategy which transmits the difference between the current and previous angle parameters when the difference is sufficiently small. This strategy accounts for the periodicity of the angle parameters through proper preprocessing and mitigates error propagation effects using novel feedback methods. We also introduce a DL-based CSI refinement module for the AP, which improves the reconstruction accuracy of the angle parameters by simultaneously utilizing both the previous and current feedback information. Simulation results demonstrate that our framework outperforms the standard method employed in current Wi-Fi systems. Our results also demonstrate significant performance gains achieved by the angle-difference feedback strategy and the CSI refinement module.

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