156 papers
This paper proposes a reinforcement learning-based decoder for quantum LDPC codes that formulates decoding as a Markov decision process with local syndrome-driven states and second-order neighborhood updates to overcome convergence issues caused by quantum degeneracy and short cycles, achieving superior performance and faster convergence than traditional schedules while maintaining competitive complexity.
This paper proposes a robust, efficient three-point movable antenna placement strategy for near-field wireless sensing that minimizes the worst-case squared position error bound by leveraging centro-symmetric distribution and moment-based analysis, outperforming conventional fixed arrays while matching exhaustive search benchmarks with negligible computational complexity.
This paper proposes a variational quantum sensing framework that utilizes a quantum circuit-optimized probe to learn environmental features from radio-frequency signals, demonstrating through ray-tracing simulations that this approach enables robust localization with no deployment channel measurements and superior sensitivity to weak or obstructed signals compared to classical baselines.
This paper introduces the Epistemic Support-Point Filter (ESPF), a mathematically proven optimal evidence-only filter that synthesizes Jaynesian maximum entropy propagation with Popperian falsification updates to minimize worst-case epistemic ignorance, thereby outperforming Bayesian approaches while recovering the Kalman filter in the Gaussian limit.
LWM-Temporal is a task-agnostic foundation model for wireless channels that leverages a novel Sparse Spatio-Temporal Attention mechanism and physics-informed pretraining to learn universal, geometry-consistent embeddings, achieving superior performance in channel prediction across diverse mobility regimes with limited fine-tuning data.
This paper resolves a 20-year-old conjecture by proving that the volume-averaged Ricci scalar of binary Bayesian networks is universally quantized to positive half-integers for tree and complete-graph structures via a Beta function cancellation mechanism, while demonstrating that this quantization fails in general due to loop counterexamples and contrasting the positive curvature of discrete networks with the negative curvature of Gaussian DAGs.
This paper establishes the first universality-breaking frontier in shuffle privacy by characterizing the asymptotic behavior of concentrated local randomizers that fail classical Gaussian limits, proving convergence to explicit Poisson, Skellam, and compound-Poisson shift experiments and providing a complete three-regime picture of shuffle privacy limits.
This paper introduces a unified framework for analyzing wireless network utility regions based on the spectral radius of nonlinear mappings, offering a powerful mathematical tool to characterize feasible regions, derive tractable conditions for convexity, and optimize sum-rate maximization across cellular, cell-less, and hybrid architectures.
This paper establishes simple necessary and sufficient conditions for information cost functions to be monotone with respect to Blackwell and Lehmann informativeness orders, grounded in their garbling characterizations, and applies these criteria to evaluate several well-known cost functions from the literature.
This paper introduces the Graded Fast Hard Thresholding Pursuit (GFHTP) algorithm, which utilizes a quantile-truncated step size for LAD minimization to achieve exact sparse signal recovery from outlier-contaminated measurements without requiring prior knowledge of the signal's sparsity level.
This paper characterizes the transition of first arrival position channel noise from heavy-tailed Cauchy to exponentially decaying distributions under nonzero drift, identifying a characteristic propagation distance that delineates diffusion-dominated and drift-dominated regimes while demonstrating that Gaussian approximations fail to capture communication potential in low-drift environments.
This paper proposes two novel unequal error protection frameworks for digital semantic communication that leverage learned bit-flip probabilities to assign heterogeneous reliability levels, demonstrating that partitioning semantic bits into short blocks with tailored channel codes significantly outperforms conventional equal-protection schemes in both task performance and transmission efficiency.
This paper establishes a bridge between homotopy type theory and information theory by defining probability types, demonstrating that homotopy cardinality respects dependent sums but not products, and deriving Shannon entropy and its chain rule as the homotopy cardinality of specific types constructed via deloopings of finite cyclic groups.
This paper presents a visual, intuition-driven guide to key information theory concepts such as entropy, mutual information, and channel capacity, demonstrating how they derive from basic probability to define the fundamental limits of data compression and reliable communication.
The paper proposes Alkaid, a provably secure steganographic scheme that achieves deterministic robustness against edit errors by integrating minimum distance decoding into the encoding process, thereby significantly outperforming state-of-the-art methods in decoding success rates, payload capacity, and encoding speed.
This survey provides a comprehensive overview of stacked intelligent metasurfaces (SIMs), detailing their physical principles, modeling frameworks, and hardware realizations while examining their role in enabling advanced 6G functionalities like wave-domain signal processing, integrated sensing, and cell-free networks, alongside identifying key challenges for future research.
This paper proposes a heuristic MIMO-CC delivery framework that enables asymmetric stream allocation while guaranteeing linear decodability through a newly derived criterion, thereby expanding the achievable Degrees of Freedom region beyond the limitations of existing symmetric-constrained designs.
This paper addresses the DNA coverage depth problem by developing combinatorial tools based on duality and extended weight enumerators to derive closed formulas for specific linear codes and a general expression linking coverage depth to the weight distributions of higher-field extensions.
This paper reinterprets NP witness discovery as an information-acquisition process and demonstrates that in a structureless "psocid" model where witnesses are accessed only via equality probes, the exponentially limited information gain per probe fundamentally prevents reliable recovery within polynomial time, thereby revealing an informational origin for exponential search complexity.
This paper establishes a polynomial-based framework linking quasi-twisted codes over finite fields to additive constacyclic codes, providing a one-to-one correspondence that enables the characterization of their respective duals and self-orthogonality conditions through trace and standard inner products.