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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
Two-Way Physical Layer Security Protocol for Gaussian Cha...
Masahito Hayashi, Angeles Vazquez-Castro · 2019-11-19 · via cs.IT updates on arXiv.org

In this paper we propose a two-way protocol of physical layer security using the method of privacy amplification against eavesdroppers. First we justify our proposed protocol by analyzing the physical layer security provided by the classic wiretap channel model (i.e. one-way protocol). In the Gaussian channels, the classic one-way protocol requires Eve's channel to be degraded w.r.t. Bob's channel. However, this channel degradation condition depends on Eve's location and whether Eve's receiving antenna is more powerful than Bob's. To overcome this limitation, we introduce a two-way protocol inspired in IEEE TIT (1993) that eliminates the channel degradation condition. In the proposed two-way protocol, on a first phase, via Gaussian channel, Bob sends randomness to Alice, which is partially leaked to Eve. Then, on a second phase, Alice transmits information to Bob over a public noiseless channel. We derive the secrecy capacity of the two-way protocol when the channel to Eve is also Gaussian. We show that the capacity of the two-way protocol is always positive. We present numerical values of the capacities illustrating the gains obtained by our proposed protocol. We apply our result to simple yet realistic models of satellite communication channels.