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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
300 GHz Dual-Band Channel Measurement, Analysis and Model...
Yiqin Wang, Yuanbo Li, Chong Han, Yi Chen, Ziming Yu · 2022-08-08 · via cs.IT updates on arXiv.org

The Terahertz (THz) band (0.1-10 THz) has been envisioned as one of the promising spectrum bands for sixth-generation (6G) and beyond communications. In this paper, a dual-band angular-resolvable wideband channel measurement in an indoor L-shaped hallway is presented and THz channel characteristics at 306-321 GHz and 356-371 GHz are analyzed. It is found that conventional close-in and alpha-beta path loss models cannot take good care of large-scale fading in the non-line-of-sight (NLoS) case, for which a modified alpha-beta path loss model for the NLoS case is proposed and verified in the NLoS case for both indoor and outdoor L-shaped scenarios. To describe both large-scale and small-scale fading, a ray-tracing (RT)-statistical hybrid channel model is proposed in the THz hallway scenario. Specifically in the hybrid model, the deterministic part in hybrid channel modeling uses RT modeling of dominant multi-path components (MPCs), i.e., LoS and multi-bounce reflected paths in the near-NLoS region, while dominant MPCs at far-NLoS positions can be deduced based on the developed statistical evolving model. The evolving model describes the continuous change of arrival angle, power and delay of dominant MPCs in the NLoS region. On the other hand, non-dominant MPCs are generated statistically. The proposed hybrid approach reduces the computational cost and solves the inaccuracy or even missing of dominant MPCs through RT at far-NLoS positions.