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

G
Google Developers Blog
人人都是产品经理
人人都是产品经理
腾讯CDC
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
WordPress大学
WordPress大学
S
SegmentFault 最新的问题
小众软件
小众软件
B
Blog
博客园 - 叶小钗
Microsoft Azure Blog
Microsoft Azure Blog
Apple Machine Learning Research
Apple Machine Learning Research
A
About on SuperTechFans
J
Java Code Geeks
Blog — PlanetScale
Blog — PlanetScale
博客园 - 司徒正美
博客园 - 【当耐特】
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
Recent Announcements
Recent Announcements
宝玉的分享
宝玉的分享
Martin Fowler
Martin Fowler
Hugging Face - Blog
Hugging Face - Blog
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Last Week in AI
Last Week in AI
V
V2EX

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
Overview of 3GPP Release 19 Study on Channel Modeling Enh...
Hitesh Poddar, Dimitri Gold, Daewon Lee, Nan Zhang, Gokul Sridha · 2025-07-25 · via cs.IT updates on arXiv.org

Channel models are a fundamental component of wireless communication systems, providing critical insights into the physics of radio wave propagation. As wireless systems evolve every decade, the development of accurate and standardized channel models becomes increasingly important for the development, evaluation and performance assessment of emerging technologies. An effort to develop a standardized channel model began around 2000 through the Third Generation Partnership Project (3GPP) and the International Telecommunication Union (ITU) with the aim of addressing a broad range of frequencies from sub-1 GHz to 100 GHz. Prior efforts focused heavily on sub-6 GHz bands and mmWave bands, and there exist some gaps in accurately modeling the 7-24 GHz frequency range, a promising candidate band for 6G. To address these gaps, 3GPP approved a Release (Rel) 19 channel modeling study. This study resulted in several enhancements to the channel models, including the ability to accurately model a Suburban Macrocell (SMa) scenario, realistic User Terminal (UT) antenna models, variability in the number of clusters, variability in the number of rays per cluster, a framework for capturing variability in power among all polarizations, near field (NF) propagation, and spatial non-stationarity (SNS) effects, all of which may be crucial for future 6G deployments. This paper presents the outcomes of this study and provides an overview of the underlying rationale, and key discussions that guided the validation, refinement, and enhancements of the 3GPP TR 38.901 channel models.