惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

D
Docker
Apple Machine Learning Research
Apple Machine Learning Research
宝玉的分享
宝玉的分享
博客园 - 叶小钗
酷 壳 – CoolShell
酷 壳 – CoolShell
博客园 - 司徒正美
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
博客园 - Franky
爱范儿
爱范儿
罗磊的独立博客
IT之家
IT之家
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
N
Netflix TechBlog - Medium
云风的 BLOG
云风的 BLOG
P
Proofpoint News Feed
U
Unit 42
Engineering at Meta
Engineering at Meta
WordPress大学
WordPress大学
博客园 - 三生石上(FineUI控件)
T
Tailwind CSS Blog
H
Help Net Security
博客园_首页
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
人人都是产品经理
人人都是产品经理

cs.IT updates on arXiv.org

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? Exact Structure of Binary Test-Time Aggregation When Semantic Communication Meets Queueing: Cross-Layer Latency and Task Fidelity Optimization Convexity in Disguise: A Theoretical Framework for Nonconvex Low-Rank Matrix Estimation Conditional Diffusion Under Linear Constraints: Langevin Mixing and Information-Theoretic Guarantees Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval Expert Routing for Communication-Efficient MoE via Finite Expert Banks Contextual Memory-Enhanced Source Coding for Low-SNR Communications Realizable Bayes-Consistency for General Metric Losses Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding A Hierarchical Sampling Framework for bounding the Generalization Error of Federated Learning Dueling DDQN-Based Adaptive Multi-Objective Handover Optimization for LEO Satellite Networks The Causal Description Gap: Information-Theoretic Separations Across Pearl's Hierarchy Optimization of CV-QKD Under Practical Constraints Benchmarking Wireless Representations: High-Dimensional vs. Compressed Embeddings for Efficiency and Robustness Real-Time Text Transmission via LLM-Based Entropy Coding over Fixed-Rate Channels SwiftChannel: Algorithm-Hardware Co-Design for Deep Learning-Based 5G Channel Estimation Evolving Token Communication with Parametric Memory Network Remote Action Generation: Remote Control with Minimal Communication The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions Stabilizing Private LASSO under Heterogeneous Covariates via Anisotropic Objective Perturbation Linear-Readout Floors and Threshold Recovery in Computation in Superposition Soft Graph Diffusion Transformer for MIMO Detection Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design Exponential families from a single KL identity MIFair: A Mutual-Information Framework for Intersectionality and Multiclass Fairness Diffusion-OAMP for Joint Image Compression and Wireless Transmission Decoupled Descent: Exact Test Error Tracking Via Approximate Message Passing
Unified Scaling of Polar Codes: Error Exponent, Scaling E...
Marco Mondelli, S. Hamed Hassani, Rüdiger Urbanke · 2015-01-11 · via cs.IT updates on arXiv.org

Consider the transmission of a polar code of block length $N$ and rate $R$ over a binary memoryless symmetric channel $W$ and let $P_e$ be the block error probability under successive cancellation decoding. In this paper, we develop new bounds that characterize the relationship of the parameters $R$, $N$, $P_e$, and the quality of the channel $W$ quantified by its capacity $I(W)$ and its Bhattacharyya parameter $Z(W)$. In previous work, two main regimes were studied. In the error exponent regime, the channel $W$ and the rate $R<I(W)$ are fixed, and it was proved that the error probability $P_e$ scales roughly as $2^{-\sqrt{N}}$. In the scaling exponent approach, the channel $W$ and the error probability $P_e$ are fixed and it was proved that the gap to capacity $I(W)-R$ scales as $N^{-1/μ}$. Here, $μ$ is called scaling exponent and this scaling exponent depends on the channel $W$. A heuristic computation for the binary erasure channel (BEC) gives $μ=3.627$ and it was shown that, for any channel $W$, $3.579 \le μ\le 5.702$. Our contributions are as follows. First, we provide the tighter upper bound $μ\le 4.714$ valid for any $W$. With the same technique, we obtain $μ\le 3.639$ for the case of the BEC, which approaches very closely its heuristically derived value. Second, we develop a trade-off between the gap to capacity $I(W)-R$ and the error probability $P_e$ as functions of the block length $N$. In other words, we consider a moderate deviations regime in which we study how fast both quantities, as functions of the block length $N$, simultaneously go to $0$. Third, we prove that polar codes are not affected by error floors. To do so, we fix a polar code of block length $N$ and rate $R$. Then, we vary the channel $W$ and we show that the error probability $P_e$ scales as the Bhattacharyya parameter $Z(W)$ raised to a power that scales roughly like $\sqrt{N}$.