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Bin Han

Publications and source records attributed to Bin Han.

At least 19 recordsLinked to original sources
arXiv2026

Confusion-Erasure Bounds of Error-Bounded Decoders under QAM

6G is expected to push ultra-reliable low-latency communication (URLLC) toward stringent residual-error tar- gets for mission-critical services, where undetected errors and erasures carry fundamentally different costs. Block error rate (BLER) conflates block confusions (undetected errors) and block erasures, which have fundamentally different impacts on system reliability. To characterize the confusion rate analyt- ically, we consider an error-bounded decoder with spherical decision regions. We focus on square quadrature amplitude modulation (QAM) constellations in the finite blocklength (FBL) regime. The confusion rate is governed by the Euclidean distances between modulated codewords, whereas the code structure constrains their Hamming distances. To bridge the two metrics, we introduce a typical distance, which represents the Euclidean distance of a codeword pair at a given Hamming distance on average, and relate it to the Hamming distance through a per-symbol squared-distance coefficient. On this basis, we derive a lower bound on the block confusion rate for random codes and an upper bound for codebooks whose minimum Euclidean distance reaches the typical distance at the non-overlapping threshold. We further characterize how both bounds evolve with the average symbol energy and the block- length. Numerical results, validated by an exact computation for short codes, confirm that as the signal-to-noise ratio (SNR) or redundancy increases, the confusion rate falls many orders of magnitude below the reliability target, leaving detectable erasures as the dominant residual error.

cs.IT↗
arXiv2026

Trustworthiness as a Measurement Structure for Task Conformance in 6G Service Chains

A 6G service crosses a chain of stages from different vendors, and its contract binds class-conditioned guarantees at the terminal interface: in the packet relay chain studied here, a priority class is owed a timely-delivery rate and a loss ceiling. We define trustworthiness as conformance of the delivered per-class distribution of outcomes to the contract and develop the measurement structure this forces. Two results fix its form: a discrepancy that treats outcomes interchangeably cannot express contract preferences, and no single expectation-valued score that determines the contracted terminal expectations can be carried from stage to stage once two or more contracted indicators remain independent at some stage. The minimal record is one signed number per independent contracted indicator, which the downstream design data carry to the terminal exactly; classical scalar trust models are the single-indicator case. The terminal deviation splits exactly into per-stage contributions. Transition logs at a stage estimate its contribution without reconstructing its full deployed behavior, a stage at its design behavior is flagged at most at a declared rate whatever traffic it receives, and the estimates bracket each stage's share of the terminal deviation at explicit confidence. In the three-relay chain, a relay that either deprioritizes premium traffic or has degraded capacity violates the same clause with identical contracted evidence: the transition audit localizes the relay in 300 of 300 windows and brackets its share of the deviation, and a diagnostic separates the causes.

cs.PF↗
arXiv2026

Convergent Compact Finite Difference Methods with Symmetric Matrices and Highest Possible Orders for Variable-Coefficient Elliptic Equations

For the $d$-dimensional variable-coefficient Poisson problem $-\nabla \cdot(a\nabla u)=f$ subject to Dirichlet boundary conditions, we seek compact (i.e., $3^d$-point) symmetric finite difference methods (FDMs) of the highest possible consistency orders, which yield symmetric positive definite (SPD) linear systems so that many fast solvers (e.g., the conjugate gradient method) can be used. Although 2nd-order compact symmetric FDMs for variable-coefficient Poisson equations are known, the construction of higher-order counterparts has, to the best of our knowledge, remained an open problem. This is largely because all stencils must satisfy highly specific interdependent relations and high-order consistency imposes numerous local technical conditions. This paper provides a comprehensive characterization of the highest consistency order that a $d$-dimensional compact symmetric FDM can achieve for variable-coefficient Poisson equations on a uniform grid. For the 1D case, we prove that our symmetric compact FDMs on a uniform grid can achieve arbitrary consistency orders, yield tridiagonal SPD matrices, and converge at the corresponding orders in the $\infty$-norm and the weighted $2$-norm. For any $d$-dimensional case with $d\ge 2$, we prove that compact (not necessarily symmetric) FDMs can achieve 6th-order consistency and that no higher consistency order is possible. If $\tfrac{|\nabla a|^2}{a^2}-\tfrac{2Δa}{a}$ is nonconstant, $d$-dimensional compact symmetric FDMs can achieve 4th-order consistency, but no higher. On the other hand, if $\tfrac{|\nabla a|^2}{a^2}-\tfrac{2Δa}{a}$ is constant, then $d$-dimensional compact symmetric FDMs can achieve 6th-order consistency, which is the highest possible order. For $d=2,3,4$ and $M=4,6$, we prove that our proposed FDMs produce SPD matrices, and achieve $M$th-order convergence in the weighted 2-norm and the $\infty$-norm.

math.NA↗
arXiv2026

Fourier-Curve Constellations under Tangential Perturbation: Covariance-Aware Soft Demapping on Coded Links

A Fourier-curve constellation places $M$ points on a closed curve through $k$ complex slots. A Gaussian perturbation along the curve's tangent, whether injected as artificial noise or arising from first-order jitter of the curve parameter, gives every symbol an observation with a symbol-dependent rank-one covariance, and the maximum-likelihood symbol metric differs from the Euclidean rule by one rank-one correction per candidate. We realize this metric as a max-log soft demapper beside a Euclidean correlator bank at $2kM$ additional multiply--accumulate operations per symbol. On a regular $(3,6)$ LDPC-coded link at $(k,M){=}(20,64)$ it recovers $5.1$\,dB of the Euclidean mismatch at BLER${=}10^{-1}$ under natural labeling and $1.1$\,dB under Gray labeling, of which an average-covariance receiver recovers $0.7$\,dB and nothing measurable, respectively, and it makes the tangential perturbation $0.2$ to $1.0$\,dB cheaper than white noise of the same power on the same codebook. The per-tone phase orientation of the curve acts as an orthogonal rotation, so these results hold at every orientation, and the demapper decodes at the level of an exactly oriented receiver up to $0.2$\,rad of orientation error per component. A bit-interleaved coded-modulation achievable rate corroborates the ordering, a Woodbury extension keeps the rank-one structure under per-tone Rician fading, and $6$-bit lookup-table quantization costs no measurable degradation.

cs.IT↗
arXiv2026

Symbol-Domain Chase Combining on Fourier-Curve Constellations: Exact Identities and Penalties of Per-Round Bit Reduction

A Fourier-curve constellation adds artificial noise along the tangent at the transmitted symbol, giving each symbol candidate its own rank-one noise covariance. A Chase retransmission repeats such a symbol, and the receiver can either accumulate the candidate metrics before forming bit log-likelihood ratios (LLRs) or add bit LLRs formed in every round. The rounds are independent given the symbol but not necessarily given one label bit, so summed exact per-round LLRs do not in general equal the joint LLR. For a fixed labeling, equiprobable symbols, and conditionally independent rounds, we derive exact identities for this difference under exact and max-log reduction, with equality conditions: each difference equals the gap between two nonnegative class corrections, which are nondecreasing in the number of rounds and vanish for binary signaling. With a rate-1/2 LDPC code after four rounds, the per-round matched Fourier receiver needs 1.97 dB more per-slot SNR at block error rate 0.1 than the covariance-ignorant joint Euclidean receiver. After four rounds, per-round exact reduction costs 2.7 dB on the Fourier link and 1.5 dB on Gray 64-QAM in bit-metric generalized mutual information, and 1.6 dB in block error rate with a 5G NR LDPC code, whereas joint max-log reduction costs less than 0.1 dB. Controls with Gray labeling, isotropic noise, and no tangent noise show that this loss does not require candidate-dependent covariance. The Fourier receiver should therefore accumulate the matched candidate metrics and reduce them to bits at each decoding attempt.

eess.SP↗
arXiv2026

Key-Reuse Vulnerability of Phase-Keyed Fourier-Curve Modulation: Relation Leakage and Key-Refresh Cost on Coded Links

Keyed modulation is often judged secure by its key-space size and a key-less receiver's error rate, evidence that fails when the key is reused and the waveform is harmonically coupled. For a phase-keyed Fourier-curve constellation, whose $k$ tones share one data parameter, integer relations among harmonic indices yield data-cancelling mixed moments that expose key characters. A modular relation lattice characterizes them; for consecutive harmonics, third-order moments recover the relative phases and a fourth-order moment completes the key up to cyclic relabeling whenever its coefficient is nonzero, as in all evaluated settings. A non-data-aided relation-moment estimator with code-aided resolution of the relabeling turns this leakage into an attack without key-space enumeration. With one key per 168-symbol codeword, the attack meets a predeclared $0.1$ compromise criterion at eleven of twelve operating points of a $(3,6)$ LDPC-coded link; at the middle noise level, the eavesdropper's block error rate is below $0.04$, and $0.03$ for consecutive harmonics without artificial noise when a key spans only $28$ symbols. Tangent artificial noise and a harmonic set without relations below order four raise her error rate at intermediate reuse lengths but do not remove the one-codeword vulnerability. On a $2^{128}$-key grid at the same noise level, the cheapest tested refresh schedule whose $95\%$ lower confidence bound on her block error rate reaches $0.9$ costs $3.54$ fresh key bits per information bit, and no tested schedule on either grid meets even a $0.5$ target with less key than a one-time pad.

cs.CR↗
arXiv2026

Waveform Randomization for Secure ISAC

This paper studies waveform-level security for Integrated Sensing and Communication (ISAC). Instead of relying on spatial beamforming or power allocation, we randomize the sensing waveform itself using phase keys and tangent Artificial Noise (AN) applied to a Fourier-curve constellation. Coordinated legitimate nodes know the key and recover the sensing reference, while Eve observes only a randomized waveform and cannot construct the correct coherent matched reference. We consider an Unmanned Aerial Vehicle (UAV)-mounted Eve with a dominant Line-of-Sight (LOS) link who fully exploits favorable geometry, including a bypass scenario where Eve listens to both the direct and echo sides to circumvent our design. We further consider a stronger Eve that attempts phase-key reconstruction from long-term observations; even then, residual key mismatch structurally degrades correlation and AN cancellation, while accurate key recovery incurs high computational complexity. Numerical results show that the proposed design reduces Eve's probability of detecting the sensing reference using non-coherent detection and, in the bypass case, substantially suppresses her correct-delay lock probability, while the legitimate receiver reliably locks onto the reference through echo accumulation.

eess.SP↗
arXiv2026

Protocol Integration of Physical Layer Deception into EAP-TEAP Wi-Fi Authentication

Credential-based Extensible Authentication Protocol (EAP) authentication cannot distinguish a legitimate credential holder from an adversary using compromised credentials. Physical Layer Deception (PLD) complements credential-based authentication by exposing a deceptive primary object over a primary transport while a separate recovery object travels with differentiated reliability over a secondary channel. Existing PLD studies remain, to our knowledge, at the physical/link-model level; using PLD's activation/deactivation mechanism as an authentication gate creates an authentication-specific design requirement, since an all-inactive attempt would exercise no recovery path. We present a batched PLD-based re-verification step for Enterprise Wi-Fi's TEAP/RADIUS/IEEE 802.11 authentication chain, implemented end to end across the server, access point, and device in the open-source hostap 2.12 codebase. Each attempt carries three rounds, at least one active, with no dedicated activation flag. Across four campaigns totaling 1593 attempts, the prototype evaluates batched recovery behavior, rejects the implemented naive credential-bearing attacker in all 30 attempts, measures successful-path latency, and evaluates the security-reliability trade-off for one, two, and three active rounds under two modeled recovery regimes. The evaluation exercises the protocol and software-MAC behavior directly and analyzes informed and retry-seeking attackers under the software recovery model.

cs.CR↗
arXiv2026

MMMG: a Comprehensive and Reliable Benchmark for Multitask Multimodal Generation

Automatically evaluating multimodal generation presents a significant challenge, as automated metrics often struggle to align with human evaluation, especially for complex tasks that involve multiple modalities. We present MMMG, the first benchmark to bring the verifiable-task paradigm to multimodal generation, spanning 4 modality combinations (image, audio, interleaved text and image, interleaved text and audio). As few multimodal outputs can be checked by programs alone, MMMG targets tasks that are either verifiable or near-verifiable: by providing references, and constraining model judges with explicit rubrics. We keep tasks challenging for generation models while enabling reliable automatic evaluation through a combination of models and programs. MMMG encompasses 55 tasks (including 31 newly developed ones), each with a carefully designed evaluation pipeline, and 1288 instructions to systematically assess reasoning, controllability, and other key capabilities of multimodal generation models. Extensive validation demonstrates that MMMG is highly aligned with human judgment, achieving an average agreement of 94.4%. Benchmarking results on 29 models reveal that even though the state-of-the-art model, GPT Image, achieves 70.7% accuracy for image generation, it falls short on interleaved generation. Furthermore, results suggest considerable improvement space in audio generation, highlighting an important future direction.

cs.AI↗
arXiv2026

Litter-Masked Block z-Channel: Designing Cover Distributions Against Public-Design Observers

Networking defenses against traffic analysis fill idle slots with cover traffic, yet the physical layer carrying it still hands upper layers the erasure-only model of a silent-idle link, leaving the cover content unexploited. We shape that content: the idle-slot waveform is drawn from the codebook complement under a design distribution, the \emph{litter distribution}, chosen to defeat a public-design passive observer while the upper layer keeps its block z-channel model. The maximum a posteriori (MAP) receiver reduces to a single threshold on the log-likelihood ratio between the best codeword and the prior-weighted litter aggregate; its errors are silent corruptions the upper layer cannot detect and erasures that retransmission absorbs. Receiver design is thus one-dimensional, minimizing erasure under a cap on silent corruption, and the joint design a Stackelberg game against a Kullback-Leibler (KL) indistinguishability metric, solved by an alternating convex relaxation and corroborated by an independent reinforcement-learning policy. On a rate-$3/4$ $(16,12)$ parity-check (LDPC) code against an observer who knows her own channel but whose channel the transmitter knows only statistically, shaped litter cuts the observer's Stein detection exponent by $41$ to $48\%$ relative to uniform cover, flat over a $10$~dB span and in the activity rate. Below the point where the legitimate link turns reliable, no cover distribution admits an operating point; at that point shaping costs about one percent of throughput, and nothing measurable $2$~dB above it.

cs.IT↗
arXiv2026

Block Erasure Channel and Block z-Channel with Bounded Decoders and Finite Blocklength

Block error rate is a standard metric in finite blocklength (FBL) communication, yet it conflates two qualitatively different failure modes: block confusions, where the decoder selects a wrong codeword, and block erasures, where it declares a loss. Higher-layer protocols treat physical-layer failures as erasures, but this cross-layer assumption lacks FBL justification. We derive a rigorous upper bound and a companion lower-side estimate on the block confusion probability (BLCP) and block erasure probability (BLEP) for bounded-distance decoders over additive white Gaussian noise (AWGN) channels at finite blocklength, recasting the coding problem as a geometric sphere packing one. We analyze the sensitivity of these bounds to blocklength and signalto-noise ratio, characterize the envelope of the upper bound, and derive closed-form Chernoff approximations. Extending the model to idle transmission blocks, we bound the false alarm probability (FAP) and show that a block z-channel abstraction emerges from the bounded-decoding geometry. Numerical results confirm that confusion and false alarm probabilities lie far below the error rate constraint, providing quantitative physicallayer support for the block erasure channel and block z-channel abstractions assumed in protocol design.

cs.IT↗
arXiv2026

NSFlow: End-to-End Differentiable Neuro-Symbolic Optical Flow for Visual Odometry

Sparse optical flow provides stable inter-frame correspondence, playing a key role in Visual Odometry (VO) and Visual-Inertial Odometry (VIO). Classical optimization-based methods, such as Lucas-Kanade (LK), perform well under small displacements but are sensitive to large motions and illumination changes. Modern regression-based learning methods, while more robust in complex scenes, are often computationally heavy and lack explicit geometric consistency, making them less suitable for efficient VO/VIO front-ends. To bridge this gap, we propose a hybrid neuro-symbolic framework that combines the strengths of both paradigms. Our method uses a Convolutional Neural Network (CNN) to extract robust feature representations, which is fed into a differentiable LK optimizer to estimate optical flow in an end-to-end trainable manner. Through implicit differentiation, gradients are propagated across the iterative solver, enabling joint optimization of feature extraction and flow estimation. The resulting system integrates seamlessly into existing VO/VIO pipelines and runs in real-time on embedded platforms. Experiments show that our method outperforms conventional optimization-based flow in challenging conditions such as dynamic lighting and low texture, while also achieving higher accuracy and lower latency than purely regression-based alternatives. When deployed in a VIO system, our method demonstrates significant performance improvement, achieving an average error reduction of 42\% on challenging datasets while enhancing tracking stability. The code is publicly available.

cs.CV↗
arXiv2026

Arbitrarily High-Order Convergence of Wavelet-based Galerkin Scheme for 1D Elliptic Interface Problems

The solution $u$ of an elliptic interface problem in a domain $Ω$ is often smooth away from the interface $Γ\subset Ω$, but its gradient is discontinuous across $Γ$. Consequently, $u$ has low global regularity and generally does not belong to $H^{3/2}(Ω)$. This paper studies 1D elliptic interface problems using wavelet methods. We propose a Galerkin method based on compactly supported biorthogonal wavelet bases on bounded intervals with approximation order $m$, for any integer $m \ge 2$. Our approach involves incorporating wavelet basis functions from higher scale levels to capture the singularity in the neighbourhood of the interface $Γ$. A principal contribution of this paper is a rigorous convergence analysis establishing the optimal orders $m-1$ in the $H^1(Ω)$-norm and $m$ in the $L^2(Ω)$-norm. The proof combines careful decay estimates for dual wavelet coefficients associated with wavelets supported away from the interface $Γ$ and with those supported in its neighborhood, wavelet characterizations of Sobolev spaces, and standard convergence arguments from the finite element method (FEM). Various numerical experiments are provided to verify the theoretical findings, including a special two-dimensional elliptic interface problem with vertical interface lines.

math.NA↗
arXiv2026

Orchra: Stateful-aware Cross-slice Workload Migrations in the 6G Control Plane

Network slicing is a foundational capability of Fifth Generation (5G)-Advanced and emerging Sixth Generation (6G) networks, yet practical support for seamless runtime slice transitions remains limited. Standard cloud-native 5G architectures lack native support for stateful inter/intra-slice session migration, relying instead on high-overhead Non-Access Stratum (NAS) re-registrations, container redeployment etc., which disrupt userplane traffic for up to 245.50 ms. To address this limitation, we present Orchra, an intelligent orchestrator for stateful, low-latency context transfer. By externalizing critical user equipment state-including NAS context, security keys, and Protocol Data Unit (PDU) session information-into a transient staging layer, Orchra preserves session continuity across slice boundaries without requiring full re-registration. Experimental evaluation shows that Orchra reduces this userplane interruption by more than twice in comparison to conventional Third Generation Partnership Project (3GPP)-based approaches while incurring negligible security overhead. These results demonstrate a practical and reproducible approach for enabling seamless, state-preserving slice transitions in cloud-native 5G-Advanced networks.

cs.NI↗
arXiv2026

A Communication-Efficient Digital Twin Framework for PSO-Based Swarm Navigation and Obstacle Avoidance

Swarm-based target localization in industrial environments faces two major challenges: navigating obstacle-rich spaces and managing intensive communication among agents. This paper proposes a communication-efficient digital twin (DT) framework for Particle Swarm Optimization (PSO)-based swarm navigation and obstacle avoidance. The DT, deployed on a Multi-Access Edge Computing (MEC) server, maintains a virtual replica of the environment to provide global guidance and obstacle bypassing when agents become trapped or experience poor connectivity. By reducing unnecessary peer-to-peer communication and centralizing environmental information, the proposed framework improves both navigation efficiency and communication resource utilization. Simulation results demonstrate that the DT-assisted PSO with obstacle avoidance achieves faster convergence and significantly lower communication load compared with decentralized P2P and random-walk PSO approaches. These findings highlight the potential of integrating DT with swarm intelligence to enhance cooperative exploration in complex industrial scenarios such as chemical leakage localization.

cs.RO↗
arXiv2026

Global nonlinear stability of the 2D incompressible viscous non-resistive MHD under sheared magnetic field

We study the two-dimensional incompressible viscous non-resistive magnetohydrodynamics in the periodic strip $\mathbb T\times\mathbb R$, subject to a smooth sheared background magnetic field $(ξ(x_2),0)^{\top}$, where $ξ(x_2)$ is bounded and away from zero. For sufficiently smooth perturbations satisfying even-odd symmetry, we prove global-in-time well-posedness and nonlinear stability in Lagrangian coordinates. The spatial inhomogeneity of the shear profile generates persistent linear contributions, most critically a nontrivial pressure term that precludes the uniform-in-time estimates. We straighten the integral curves of the initial magnetic field and construct a volume-preserving corrector. This geometric reduction transforms the intractable linear pressure into a quadratic nonlinearity. These structures yield the global energy bounds and the anisotropic algebraic decay rate for the system. This mechanism appears to provide the first rigorous framework for establishing global nonlinear stability for viscous non-resistive magnetohydrodynamics near the genuinely nonuniform sheared magnetic profile.

math.AP↗
arXiv2026

Galerkin Scheme Using Biorthogonal Wavelets on Intervals for Elliptic Interface Problems

This paper presents a wavelet Galerkin method for solving elliptic interface problems of the form $-\nabla\cdot(a\nabla u)=f$ in $Ω\backslash Γ$, where $Γ$ is a smooth interface within $Ω$. Since the scalar variable coefficient $a>0$ and source term $f$ are often discontinuous across $Γ$, the solution $u$ typically has discontinuous gradient $\nabla u$ across $Γ$ and hence $u\not\in H^{1.5}(Ω)$, posing significant challenges for traditional numerical methods. By utilizing a compactly supported biorthogonal wavelet for $H^1_0(Ω)$, we develop a strategy that incorporates additional wavelet elements (or basis functions) along the interface to resolve the complex geometry of the interface $Γ$ and the resulting gradient discontinuities. For the two-dimensional (2D) elliptic interface problem, the proposed method achieves near-optimal convergence rates: $\mathcal{O}(h |\log(h)|)$ in the $H^1(Ω)$-norm and $\mathcal(h^2 |\log(h)|^2)$ in the $L^{2}$-norm with respect to the approximation order. A key theoretical contribution is the use of the dual biorthogonal wavelet basis to establish the $H^1(Ω)$ convergence results. This is supported by the development of weighted Bessel properties for wavelets and several inequalities in fractional Sobolev spaces. To maintain high accuracy and robustness against high-contrast coefficients, our method leverages an augmented set of wavelet elements, similar to meshfree approaches, thereby eliminating the need for the complex re-meshing required by finite element methods. Unlike existing techniques, this wavelet Riesz basis framework captures the geometry of $Γ$ seamlessly while ensuring that the condition numbers of the coefficient matrices remain small and uniformly bounded, independent of the problem size.

math.NA↗