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Theoretical Limits of Language Model Alignment $f$-Divergence Regularized RLHF: Two Tales of Sampling and Unified Analyses A Unified Measure-Theoretic View of Diffusion, Score-Based, and Flow Matching Generative Models When Can Voting Help, Hurt, or Change Course? 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Design of Convolutional Codes for Varying Constraint Lengths
Parag Dhounde, Avinash Bhute · 2024-10-02 · via cs.IT updates on arXiv.org

This paper explores the design of convolutional codes for varying constraint lengths, focusing on their role in error correction in digital communication systems. Convolutional codes are essential in achieving reliable data transmission across noisy channels. The constraint length, which determines the memory of the encoder, plays a critical role in the performance of convolutional codes. This study investigates the effect of varying constraint lengths on coding performance, including code rate, complexity, and decoding accuracy. Simulation results and theoretical analysis illustrate the trade-offs between constraint length and decoding efficiency.

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