154 papers
This paper demonstrates that for approximately memoryless surjective POST channels, feedback does not increase channel capacity, thereby extending Shannon's classical result on discrete memoryless channels to a broader class of finite-state channels.
The paper presents Midicoth, a lossless compression system that enhances online probability estimation by applying a lightweight, multi-stage micro-diffusion denoising layer to correct systematic biases in adaptive statistical models through a data-efficient, binary-tree decomposition of byte predictions.
This paper demonstrates that pre-shared entanglement using two-mode squeezed vacuum states significantly reduces the latency of optical channel transmittance change detection by leveraging quantum relative entropy scaling, while simultaneously enhancing the fundamental trade-off between communication capacity and detection speed.
This paper establishes an information-theoretic no-go theorem proving that classical ontological models constrained to reuse a single ontic state space across multiple interventions inevitably incur an irreducible contextual information cost, thereby identifying contextuality as a fundamental limitation of such classical representations that quantum theory circumvents by relaxing the single-variable assumption.
This paper introduces "bipredictability" (P) as a fundamental, bounded measure of shared information between observations, actions, and outcomes to distinguish mere agency from true intelligence, demonstrating that current AI systems lack the self-monitoring feedback loops necessary for adaptive learning and proposing a thalamocortical-inspired architecture to restore it.
This paper introduces the concept of generalized pinching-antenna systems as a transformative, flexible architecture for next-generation wireless networks, providing a comprehensive tutorial on their physical principles, diverse realizations, design strategies, integration with emerging technologies, and future research directions.
This paper proposes an improved Contact Graph Routing algorithm for Delay Tolerant Networks that incorporates contact splitting and edge pruning to proactively enforce capacity and buffer constraints during route search, thereby ensuring optimal delivery times and reducing collisions compared to traditional reactive methods.
This paper provides a self-contained tutorial on finite block length rate-distortion theory for a Bernoulli source with Hamming distortion, deriving the classical rate-distortion function, illustrating its computation via the Blahut-Arimoto algorithm, and analyzing finite-length refinements governed by rate-distortion dispersion with accompanying numerical examples.
This paper introduces Probability Matching Interval Coding (PMATIC), a model-agnostic algorithm that enables robust, high-performance lossless compression by tolerating bounded prediction mismatches between compressor and decompressor in deep learning-based systems.
This paper proposes a deep reinforcement learning-based approach using proximal policy optimisation to enhance physical layer security in indoor visible light communication systems by leveraging realistic IRS-induced time delays to constructively boost signals for legitimate users while intentionally creating intersymbol interference for both colluding and non-colluding eavesdroppers.
This paper clarifies the abstract geometrical formulation of thermodynamics on non-compact symmetric spaces used in Cartan Neural Networks by proving that only Kähler spaces support Gibbs distributions, explicitly characterizing their generalized temperature spaces via adjoint orbits, and demonstrating the equivalence between various information and thermodynamical geometries while establishing the covariance of these distributions under the full symmetry group.
This paper introduces AirCNN, a novel paradigm that leverages reconfigurable intelligent surfaces (RISs) to perform over-the-air analog convolution operations, demonstrating through joint optimization and simulations that multi-RIS MISO architectures effectively emulate both standard and depthwise separable CNN layers with high classification performance.
This paper proposes optimization-based and deep reinforcement learning (specifically DDQN) methods to achieve positive-rate keyless covert communications in Rayleigh block-fading channels by solving power and rate allocation problems under both non-causal and causal channel state information scenarios.
This paper proposes an efficient iterative stochastic precoding algorithm for MIMO networks over generalized fading channels that maximizes long-term average weighted sum rates by utilizing a novel matrix fractional programming-based lower bound on expected data rates, requiring only the first and second moments of channel statistics rather than specific distribution models.
This paper investigates the Schur product of monomial-Cartesian codes via Minkowski sums of exponent sets to construct improved CSS-T quantum codes and more efficient Private Information Retrieval schemes using -affine variety codes and their subfield-subcodes.
This paper characterizes Martin-Löf and Schnorr randomness by establishing that these notions correspond to the weak merging of computable probability measures under the summable Kullback-Leibler divergence, a result derived by linking the divergence to the incremental growth of the Doob decomposition of a specific submartingale.
This paper proposes a novel generative diffusion model-based framework for high-dimensional MIMO channel estimation that leverages deep generative priors and unsupervised learning to achieve high-fidelity recovery with significantly reduced latency and pilot overhead, even under low-resolution quantization and without requiring ground truth data.
This paper proposes a unified multi-axis concentration modulation framework (MAxCM) and its ratio-based subclass (MAxRSK) to enhance spectral efficiency and error performance in mobile molecular communication systems by enabling robust, channel-independent decoding in dynamic environments.
This paper proposes a coherence-aware federated learning framework that mitigates heterogeneous link impairments by employing product superposition for efficient downlink delivery and partial model reception with local filling for robust uplink aggregation, thereby achieving improved communication efficiency and learning accuracy under varying channel conditions.
This paper proposes FedPASS, a novel framework that leverages pinching-antenna systems to dynamically optimize physical-layer connectivity, thereby jointly minimizing end-to-end latency and convergence gaps in multi-user wireless federated learning through a specialized two-tier iterative algorithm.