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

V
V2EX
P
Proofpoint News Feed
D
DataBreaches.Net
C
Check Point Blog
L
LangChain Blog
量子位
美团技术团队
Vercel News
Vercel News
人人都是产品经理
人人都是产品经理
N
Netflix TechBlog - Medium
V
Visual Studio Blog
Microsoft Security Blog
Microsoft Security Blog
博客园 - 【当耐特】
MongoDB | Blog
MongoDB | Blog
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
Last Week in AI
Last Week in AI
The GitHub Blog
The GitHub Blog
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
U
Unit 42
腾讯CDC
M
MIT News - Artificial intelligence
Microsoft Azure Blog
Microsoft Azure Blog
Blog — PlanetScale
Blog — PlanetScale

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
Active Attack on User Load Achieving Pilot Design in Mass...
Noman Akbar, Shihao Yan · 2017-10-06 · via cs.IT updates on arXiv.org

In this paper, we propose an active attacking strategy on a massive multiple-input multiple-output (MIMO) network, where the pilot sequences are obtained using the user load-achieving pilot sequence design. The user load-achieving design ensures that the signal-to-interference-plus-noise ratio (SINR) requirements of all the users in the massive MIMO networks are guaranteed even in the presence of pilot contamination. However, this design has some vulnerabilities, such as one known pilot sequence and the correlation among the pilot sequences, that may be exploited by active attackers. In this work, we first identify the potential vulnerabilities in the user load-achieving pilot sequence design and then, accordingly, develop an active attacking strategy on the network. In the proposed attacking strategy, the active attackers transmit known pilot sequences in the uplink training and artificial noise in the downlink data transmission. Our examination demonstrates that the per-cell user load region is significantly reduced by the proposed attacking strategy. As a result of the reduced per-cell user load region, the SINR requirements of all the users are no longer guaranteed in the presence of the active attackers. Specifically, for the worst affected users the SINR requirements may not be ensured even with infinite antennas at the base station.