arXiv subjects

Runxin Zhang

Publications and source records attributed to Runxin Zhang.

9 recordsLinked to original sources

arXiv2026

Tube-Structured Incremental Semantic HARQ for Generative Video Receivers

Generative semantic communication uses receiver-side generative priors to reconstruct visual content from compact semantics, making it attractive for bandwidth-limited multimedia delivery. For video, reliable recovery remains difficult because errors accumulate over time, useful evidence is temporally correlated, and the receiver must make decisions under limited interaction, retransmission, and reconstruction budgets. Existing generative semantic communication studies mainly emphasize representation, compression, or generative reconstruction, while recent error-resilient and semantic-HARQ methods still largely operate on encoder-defined or frame-block retransmission units. This paper studies receiver-driven semantic HARQ for generative video reconstruction under a budget-constrained AoIS-AUC objective and argues that the retransmission primitive is itself an important system design variable. We propose tube-structured package-native requests, in which temporally local packages are the channel-visible HARQ objects and are transmitted, dropped, received, and committed at package granularity. Under a controlled comparison protocol with matched backbone, budgets, and channel model, this primitive yields lower time-weighted recovery cost than competitive block-based baselines in practically relevant moderate-to-harsh regimes, while the gap naturally shrinks in near-clean channels. The gain mainly appears as earlier stabilization of the recovery trajectory, while final-quality endpoints remain broadly comparable, and it persists even against a tube-aware block-ranking baseline.

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arXiv2026

Multi-Mode Pinching-Antenna Systems: Polarization-Aware Full-Wave Modeling and Optimization

Millimeter-wave and terahertz communications face a fundamental challenge: overcoming severe path loss without sacrificing spectral efficiency. Pinching antenna systems (PASS) address this by bringing radiators physically close to users, yet existing frameworks treat the waveguide as a mere transmission line, overlooking its inherent multi-mode capabilities and the critical role of polarization. This paper develops the first polarization-aware, full-wave electromagnetic model for multi-mode PASS (MMPASS), capturing spatial radiation patterns, modal polarization states, and polarization matching efficiency from first principles. Leveraging this physically grounded model, we reveal fundamental trade-offs among waveguide attenuation, atmospheric absorption, and geometric spreading, yielding closed-form solutions for optimal PA placement and orientation in single-user scenarios. Extending to multi-user settings, we propose a modular optimization framework that integrates fractional programming with closed-form polarization updates, scaling gracefully to arbitrary numbers of waveguides, PAs, and users. Numerical results show that MMPASS achieves up to a 167% increase in spectral efficiency compared with single-mode PASS. Moreover, when comparing MMPASS with its polarization-ignorant counterpart, polarization awareness alone improves the sum rate by up to 23%. By bridging rigorous electromagnetic theory with scalable optimization, MMPASS establishes a physically complete and practically viable foundation for future high-frequency wireless networks.

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arXiv2026

Unanticipated Adversarial Robustness of Semantic Communication

Semantic communication, enabled by deep joint source-channel coding (DeepJSCC), is widely expected to inherit the vulnerability of deep learning to adversarial perturbations. This paper challenges this prevailing belief and reveals a counterintuitive finding: semantic communication systems exhibit unanticipated adversarial robustness that can exceed that of classical separate source-channel coding systems. On the theoretical front, we establish fundamental bounds on the minimum attack power required to induce a target distortion, overcoming the analytical intractability of highly nonlinear DeepJSCC models by leveraging Lipschitz smoothness. We prove that the implicit regularization from noisy training forces decoder smoothness, a property that inherently provides built-in protection against adversarial attacks. To enable rigorous and fair comparison, we develop two novel attack methodologies that address previously unexplored vulnerabilities: a structure-aware vulnerable set attack that, for the first time, exploits graph-theoretic vulnerabilities in LDPC codes to induce decoding failure with minimal energy, and a progressive gradient ascent attack that leverages the differentiability of DeepJSCC to efficiently find minimum-power perturbations. Designing such attacks is challenging, as classical systems lack gradient information while semantic systems require navigating high-dimensional, non-convex spaces; our methods fill these critical gaps in the literature. Extensive experiments demonstrate that semantic communication requires up to $14$-$16\times$ more attack power to achieve the same distortion as classical systems, empirically substantiating its superior robustness.

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arXiv2025

Directional Pinching-Antenna Systems

We propose a directional pinching-antenna system (DiPASS), a comprehensive framework that transitions PASS modeling from idealized abstraction to physical consistency. DiPASS introduces the first channel model that accurately captures the directional, pencil-like radiation of pinching antennas, incorporates a practical waveguide attenuation of 1.3 dB/m, and accounts for stochastic line-of-sight blockage. A key enabler of DiPASS is our new "equal quota division" power allocation strategy, which guarantees predetermined coupling lengths independent of antenna positions, thereby overcoming a critical barrier to practical deployment. Our analysis yields foundational insights: we derive closed-form solutions for optimal antenna placement and orientation in single-PA scenarios, quantifying the core trade-off between waveguide and free-space losses. For multi-PA systems, we develop a scalable optimization framework that leverages directional sparsity, revealing that waveguide diversity surpasses antenna density in enhancing system capacity. Extensive simulations validate our analysis and demonstrate that DiPASS provides a realistic performance benchmark, fundamentally reshaping the understanding and design principles for future PASS-enabled 6G networks.

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arXiv2025

From Link Diversity to Cross-Band Feedback Collaboration: A New Perspective on Hybrid Optical-RF Systems

We suggest a re-examination of the conventional view that hybrid optical-radio frequency (O-RF) systems are primarily diversity-driven networks that switch between RF and optical links for robustness. Instead, we uncover a new architectural opportunity: repurposing the optical downlink to enable real-time feedback channel coding over the RF uplink, where structured decoder feedback is delivered from the access point to guide the transmitter's coding strategy. This insight marks a conceptual paradigm shift from passive link diversity to active cross-band collaboration, where the wideband, interference-free optical wireless communication (OWC) is no longer merely a downlink backup but a functional enabler of uplink reliability. To realize this vision, we propose a novel architecture, O-RF with Cross-Band Feedback (O-RF-CBF), that exploits the optical downlink feedback to facilitate adaptive RF uplink coding. Numerical results reveal that O-RF-CBF achieves significant uplink throughput gains over traditional O-RF systems. Our findings highlight that inter-band synergy, not redundancy, is the key to unlocking the full potential of hybrid wireless networks.

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arXiv2025

Capacity-Optimized Pre-Equalizer Design for Visible Light Communication Systems

Since commercial LEDs are primarily designed for illumination rather than data transmission, their modulation bandwidth is inherently limited to a few MHz. This becomes a major bottleneck in the implementation of visible light communication (VLC) systems necessiating the design of pre-equalizers. While state-of-the-art equalizer designs primarily focus on the data rate increasing through bandwidth expansion, they often overlook the accompanying degradation in signal-to-noise ratio (SNR). Achieving effective bandwidth extension without introducing excessive SNR penalties remains a significant challenge, since the channel capacity is a non-linear function of both parameters. In this paper, we present a fundamental analysis of how the parameters of the LED and pre-equalization circuits influence the channel capacity in intensity modulation and direct detection (IMDD)-based VLC systems. We derive a closed-form expression for channel capacity model that is an explicitly function of analog pre-equalizer circuit parameters. Building upon the derived capacity expression, we propose a systematic design methodology for analog pre-equalizers that effectively balances bandwidth and SNR, thereby maximizing the overall channel capacity across a wide range of channel attenuations. We present extensive numerical results to validate the effectiveness of the proposed design and demonstrate the improvements over conventional bandwidth-optimized pre-equalizer designs.

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arXiv2024

Polarization Aware Movable Antenna

This paper presents a polarization-aware movable antenna (PAMA) framework that integrates polarization effects into the design and optimization of movable antennas (MAs). While MAs have proven effective at boosting wireless communication performance, existing studies primarily focus on phase variations caused by different propagation paths and leverage antenna movements to maximize channel gains. This narrow focus limits the full potential of MAs. In this work, we introduce a polarization-aware channel model rooted in electromagnetic theory, unveiling a defining advantage of MAs over other wireless technologies such as precoding: the ability to optimize polarization matching. This new understanding enables PAMA to extend the applicability of MAs beyond radio-frequency, multipath-rich scenarios to higher-frequency bands, such as mmWave, even with a single line-of-sight (LOS) path. Our findings demonstrate that incorporating polarization considerations into MAs significantly enhances efficiency, link reliability, and data throughput, paving the way for more robust and efficient future wireless networks.

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arXiv2023

Optical Integrated Sensing and Communication with Light-Emitting Diode

This paper presents a new optical integrated sensing and communication (O-ISAC) framework tailored for cost-effective Light-Emitting Diode (LED) for enhanced Internet of Things (IoT) applications. Unlike prior research on ISAC, which predominantly focused on radio frequency (RF) band, O-ISAC capitalizes on the inherent advantages of the optical spectrum, including the ultra-wide license-free bandwidth, immunity to RF interference, and energy efficiency -- attributes crucial for IoT communications. The communication and sensing in our O-ISAC system unfold in two phases: directionless O-ISAC and directional O-ISAC. In the first phase, distributed optical access points emit non-directional light for communication and leverage small-aperture imaging principles for sensing. In the second phase, we put forth the concept of optical beamforming, using collimating lenses to concentrate light, resulting in substantial performance enhancements in both communication and sensing. Numerical and simulation results demonstrate the feasibility and impressive performance of O-ISAC benchmarked against optical separate communication and sensing systems.

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arXiv2022

Phase Code Discovery for Pulse Compression Radar: A Genetic Algorithm Approach

Discovering sequences with desired properties has long been an interesting intellectual pursuit. In pulse compression radar (PCR), discovering phase codes with low aperiodic autocorrelations is essential for a good estimation performance. The design of phase code, however, is mathematically non-trivial as the aperiodic autocorrelation properties of a sequence are intractable to characterize. In this paper, we put forth a genetic algorithm (GA) approach to discover new phase codes for PCR with the mismatched filter (MMF) receiver. The developed GA, dubbed GASeq, discovers better phase codes than the state of the art. At a code length of 59, the sequence discovered by GASeq achieves a signal-to-clutter ratio (SCR) of 50.84, while the best-known sequence has an SCR of 45.16. In addition, the efficiency and scalability of GASeq enable us to search phase codes with a longer code length, which thwarts existing deep learning-based approaches. At a code length of 100, the best phase code discovered by GASeq exhibit an SCR of 63.23.

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