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

B
Blog RSS Feed
量子位
Y
Y Combinator Blog
大猫的无限游戏
大猫的无限游戏
B
Blog
U
Unit 42
C
Check Point Blog
I
InfoQ
aimingoo的专栏
aimingoo的专栏
雷峰网
雷峰网
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
博客园 - 【当耐特】
人人都是产品经理
人人都是产品经理
The Cloudflare Blog
H
Help Net Security
MongoDB | Blog
MongoDB | Blog
博客园 - Franky
H
Hackread – Cybersecurity News, Data Breaches, AI and More
J
Java Code Geeks
Microsoft Azure Blog
Microsoft Azure Blog
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
云风的 BLOG
云风的 BLOG
宝玉的分享
宝玉的分享
爱范儿
爱范儿

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
Secrecy Capacity Bounds for Visible Light Communications ...
Jin-Yuan Wang, Xian-Tao Fu, Jun-Bo Wang, Min Lin, Julian Cheng, · 2021-09-23 · via cs.IT updates on arXiv.org

In physical-layer security, one of the most fundamental issues is the secrecy capacity. The objective of this paper is to determine the secrecy capacity for an indoor visible light communication system consisting of a transmitter, a legitimate receiver and an eavesdropping receiver. In such a system, both signal-independent and signal-dependent Gaussian noises are considered. Under non-negativity and average optical intensity constraints, lower and upper secrecy capacity bounds are first derived by the variational method, the dual expression of the secrecy capacity, and the concept of "the optimal input distribution that escapes to infinity". Numerical results show that the secrecy capacity upper and lower bounds are tight. By an asymptotic analysis at large optical intensity, there is a small performance gap between the asymptotic upper and lower bounds. Then, by adding a peak optical intensity constraint, we further analyze the exact and asymptotic secrecy capacity bounds. Finally, the tightness of the derived bounds is verified by numerical results.