An LLM-enhanced Viterbi decoder achieves roughly 1.5 dB extra coding gain in block error rate and over 50% better semantic similarity than conventional Viterbi for constraint-length-3 convolutional codes on AWGN channels.
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A mathematical theory of communication
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HealthPoint represents clinical events as points in a 4D space (content, time, modality, case) and applies low-rank relational attention to achieve state-of-the-art mortality prediction from multi-level incomplete multimodal EHRs.
An achievability result is derived for empirical coordination under finite blocklength constraints, yielding both an exact rate bound and its asymptotic expansion.
Decoder-provided pilots reuse decoded codewords to track time-varying channels, with information-theoretic limits derived and simulations showing performance gains over conventional training.
SPATE encodes data via spike trains mapped to quantum phases, yielding stronger feature representations than angle or amplitude encoding on datasets like Blobs and Moons.
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Risk-Calibrated Learning reduces critical error rates in medical AI by 20-92% across four imaging datasets by embedding a severity matrix into the optimization.
Proposes a three-layer framework using formal AI reasoning for verification, derivation, and discovery in wireless communications theory.
Tutorial overview of electromagnetic signal and information theory for continuous-aperture array wireless systems, emphasizing the shift from discrete to continuous models.