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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
Statistical Modeling of the Impact of Underwater Bubbles ...
Myoungkeun Shin, Ki-Hong Park, Mohamed-Slim Alouini · 2020-06-23 · via cs.IT updates on arXiv.org

In underwater wireless optical communications (UWOC), the random obstruction of light propagation by air bubbles can cause fluctuations in the incoming light intensity of a receiver. In this paper, we propose a statistical model for determining the received power by a receiver in the presence of air bubbles. First, based on real experiments of the behavior of air bubbles underwater, we propose statistical models for the generation, size, and horizontal distribution of each air bubble. Second, we mathematically derive the obstruction caused by the shadow of each bubble as it passes over the beam area. We then compute the combined obstruction of all generated air bubbles to determine the total obstructed power, which is a random variable due to the randomness of bubble behavior. Next, we find the first and second moments of the total obstructed power to model the statistical distribution of the obstructed received power by using the method of moments, which shows that the Weibull distribution suitably matches the simulation data. We also estimate the shape and scale parameters by using two derived moments. Furthermore, we also construct a statistical model of the received power with complete blockage in the presence of air bubbles and we derive the distribution of the composite channel model combining the proposed bubble-obstruction model with a Gamma-Gamma turbulence model. Finally, we obtain and verify the analytic forms of the average bit error rate and the capacity of UWOC systems under this newly proposed composite channel model.