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

Google DeepMind News
Google DeepMind News
I
InfoQ
Engineering at Meta
Engineering at Meta
D
DataBreaches.Net
L
LangChain Blog
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Recent Announcements
Recent Announcements
GbyAI
GbyAI
爱范儿
爱范儿
Microsoft Security Blog
Microsoft Security Blog
腾讯CDC
美团技术团队
罗磊的独立博客
Microsoft Azure Blog
Microsoft Azure Blog
WordPress大学
WordPress大学
T
The Blog of Author Tim Ferriss
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
雷峰网
雷峰网
M
MIT News - Artificial intelligence
D
Docker
MongoDB | Blog
MongoDB | Blog
F
Fortinet All Blogs
博客园 - 叶小钗

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
Predicting the Channel Access of Bluetooth Low Energy
Julian Karoliny, Thomas Blazek, Andreas Springer, Hans-Peter Ber · 2023-01-19 · via cs.IT updates on arXiv.org

Bluetooth Low Energy (BLE) is one of the key enablers for low-power and low-cost applications in consumer electronics and the Internet of Things. The latest features such as audio and direction finding will introduce more and more devices that rely on BLE for communication. However, like many other wireless standards, BLE relies on the unlicensed 2.4 GHz frequency band where the spectrum is already very crowded and a channel access without collisions with other devices is difficult to guarantee. For applications with high reliability requirements, it will be beneficial to actively consider channel access from other devices or standards. In this work, we present an approach to estimate the connection parameters of multiple BLE connections outside our control and knowledge by passively listening to the channel. With this, we are able to predict future channel access of these BLE connections that can be used by other wireless networks to avoid collisions. We show the applicability of our algorithm with measurements from which we are able to identify unknown BLE connections, reconstruct their specific connection parameters, and predict their future channel access.