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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
Characterization of Spatial-Temporal Channel Statistics f...
Chathuri Weragama, Joonas Kokkoniemi, Mar Francis De Guzman, Kat · 2024-03-28 · via cs.IT updates on arXiv.org

Millimeter-wave (mmWave) and D Band (110--170~GHz) frequencies are poised to play a pivotal role in the advancement of sixth-generation (6G) systems and beyond, owing to their ability to enhance performance metrics such as capacity, ultra-low latency, and spectral efficiency. This paper concentrates on deriving statistical insights into power, delay, and the number of paths based on measurements conducted across four distinct locations at a center frequency of 143.1 GHz. The findings underscore the suitability of various distributions in characterizing power behavior in line-of-sight (LOS) scenarios, including lognormal, Nakagami, gamma, and beta distributions, whereas the loglogistic distribution gives the optimal fit for power distribution in non-line-of-sight (NLOS) scenarios. Moreover, the exponential distribution shows to be the most appropriate model for the delay distribution in both LOS and NLOS scenarios. In terms of the number of paths, observations indicate a tendency for the highest concentration within the 10 m to 30 m distance range between the transmitter (Tx) and receiver (Rx). These insights shed light on the statistical nature of D band propagation characteristics, which are vital for informing the design and optimization of future 6G communication systems