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

IT之家
IT之家
博客园 - 聂微东
雷峰网
雷峰网
Microsoft Azure Blog
Microsoft Azure Blog
WordPress大学
WordPress大学
Hugging Face - Blog
Hugging Face - Blog
S
SegmentFault 最新的问题
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
T
Tailwind CSS Blog
博客园 - 三生石上(FineUI控件)
V
Visual Studio Blog
博客园 - 司徒正美
爱范儿
爱范儿
月光博客
月光博客
阮一峰的网络日志
阮一峰的网络日志
博客园_首页
博客园 - 【当耐特】
Jina AI
Jina AI
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
酷 壳 – CoolShell
酷 壳 – CoolShell
大猫的无限游戏
大猫的无限游戏
小众软件
小众软件
人人都是产品经理
人人都是产品经理
V
V2EX

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
MIMO DF Relay Beamforming for Secrecy with Artificial Noi...
Sanjay Vishwakarma, A. Chockalingam · 2015-05-04 · via cs.IT updates on arXiv.org

In this paper, we consider decode-and-forward (DF) relay beamforming with imperfect channel state information (CSI), cooperative artificial noise (AN) injection, and finite-alphabet input in the presence of an user and $J$ non-colluding eavesdroppers. The communication between the source and the user is aided by a multiple-input-multiple-output (MIMO) DF relay. We use the fact that a wiretap code consists of two parts: i) common message (non-secret), and ii) secret message. The source transmits two independent messages: i) common message (non-secret), and ii) secret message. The common message is transmitted at a fixed rate $R_{0}$, and it is intended for the user. The secret message is also intended for the user but it should be kept secret from the $J$ eavesdroppers. The source and the MIMO DF relay operate under individual power constraints. In order to improve the secrecy rate, the MIMO relay also injects artificial noise. The CSI on all the links are assumed to be imperfect and CSI errors are assumed to be norm bounded. In order to maximize the worst case secrecy rate, we maximize the worst case link information rate to the user subject to: i) the individual power constraints on the source and the MIMO relay, and ii) the best case link information rates to $J$ eavesdroppers be less than or equal to $R_{0}$ in order to support a fixed common message rate $R_{0}$. Numerical results showing the effect of perfect/imperfect CSI, presence/absence of AN with finite-alphabet input on the secrecy rate are presented.