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

S
SegmentFault 最新的问题
G
Google Developers Blog
H
Help Net Security
月光博客
月光博客
阮一峰的网络日志
阮一峰的网络日志
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
P
Proofpoint News Feed
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
B
Blog RSS Feed
爱范儿
爱范儿
酷 壳 – CoolShell
酷 壳 – CoolShell
博客园 - 三生石上(FineUI控件)
大猫的无限游戏
大猫的无限游戏
人人都是产品经理
人人都是产品经理
GbyAI
GbyAI
D
Docker
Hugging Face - Blog
Hugging Face - Blog
I
InfoQ
博客园 - 司徒正美
Last Week in AI
Last Week in AI
Microsoft Security Blog
Microsoft Security Blog
美团技术团队
Stack Overflow Blog
Stack Overflow Blog
M
MIT News - Artificial intelligence

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
Identification over Permutation Channels
Abhishek Sarkar, Bikash Kumar Dey · 2024-05-15 · via cs.IT updates on arXiv.org

We study message identification over a q-ary uniform permutation channel, where the transmitted vector is permuted by a permutation chosen uniformly at random. For discrete memoryless channels(DMCs), the number of identifiable messages grows doubly exponentially. Identification capacity, the maximum second-order exponent, is known to be the same as the Shannon capacity of the DMC. Permutation channels support reliable communication of only polynomially many messages. A simple achievability result shows that message sizes growing as 2^{ε_nn^{q-1}} are identifiable for any ε_n\rightarrow0. We prove two converse results. A ``soft'' converse shows that for any R>0, there is no sequence of identification codes with message size growing as 2^{Rn^{q-1}} with a power-law decay (n^{-μ}) of the error probability. We also prove a ``strong" converse showing that for any sequence of identification codes with message size 2^{R_n n^{q-1}}, where R_n\rightarrow\infty, the sum of type I and type II error probabilities approaches at least 1 as n\rightarrow\infty. To prove the soft converse, we use a sequence of steps to construct a new identification code with a simpler structure which relates to a set system, and then use a lower bound on the normalized maximum pairwise intersection of a set system. To prove the strong converse, we use results on approximation of distributions. The achievability and converse results are generalized to the case of coding over multiple blocks. We finally study message identification over a q-ary uniform permutation channel in the presence of causal block-wise feedback from the receiver, where the encoder receives an entire n-length received block after the transmission of the block is complete. We show that in the presence of feedback, the maximum number of identifiable messages grows doubly exponentially, and we present a two-phase achievability scheme.