·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Mathematicians want proof OpenAI didn’t use their work2h◆Powering AI is an architecture problem2h◆Gaussian Linear Functional Manifold Method for Massive Point Cloud Data9h◆Mind the Gap: Navigating Inference with Optimal Transport Maps9h◆WhisTLE: Deeply Supervised, Text-Only Domain Adaptation for Small Pretrained Speech Recognition Transformers9h◆Learning Multi-Index Models with Hyper-Kernel Ridge Regression9h◆DAGLFNet: Deep Feature Attention Guided Global and Local Feature Fusion for Pseudo-Image Point Cloud Segmentation9h◆RePro: Training Language Models to Faithfully Recycle the Web for Pretraining9h◆Generalized infinite dimensional Alpha-Procrustes based geometries9h◆Unexplored flaws in multiple-choice VQA make benchmarking unreliable9h◆Bringing Value Models Back: Generative Critics for Value Modeling in LLM Reinforcement Learning9h◆Residual-augmented flow matching operators for probabilistic partial differential equations9h◆Decoder-Side Semantic Conditioning for Low-Bitrate Neural Speech Compression9h◆Solving the Offline and Online Min-Max Problem of Non-smooth Submodular-Concave Functions: A Zeroth-Order Approach9h◆Observing Health Outcomes Using Remote Sensing Imagery and Geo-Context Guided Visual Transformer9h◆Rotation-free Online Handwritten Character Recognition Using Linear Recurrent Units9h◆Jointly Optimizing Debiased CTR and Uplift for Coupons Marketing: A Unified Causal Framework9h◆Discriminative Span as a Predictor of Synthetic Data Utility via Classifier Reconstruction9h◆Backdoor Channels Hidden in Latent Space: Extending Cryptographic Undetectability to Modern Neural Networks9h◆Amortized Neural Optimization for Pre-Layout Signal Integrity Design Space Exploration using Differentiable Surrogates9h◆Mathematicians want proof OpenAI didn’t use their work2h◆Powering AI is an architecture problem2h◆Gaussian Linear Functional Manifold Method for Massive Point Cloud Data9h◆Mind the Gap: Navigating Inference with Optimal Transport Maps9h◆WhisTLE: Deeply Supervised, Text-Only Domain Adaptation for Small Pretrained Speech Recognition Transformers9h◆Learning Multi-Index Models with Hyper-Kernel Ridge Regression9h◆DAGLFNet: Deep Feature Attention Guided Global and Local Feature Fusion for Pseudo-Image Point Cloud Segmentation9h◆RePro: Training Language Models to Faithfully Recycle the Web for Pretraining9h◆Generalized infinite dimensional Alpha-Procrustes based geometries9h◆Unexplored flaws in multiple-choice VQA make benchmarking unreliable9h◆Bringing Value Models Back: Generative Critics for Value Modeling in LLM Reinforcement Learning9h◆Residual-augmented flow matching operators for probabilistic partial differential equations9h◆Decoder-Side Semantic Conditioning for Low-Bitrate Neural Speech Compression9h◆Solving the Offline and Online Min-Max Problem of Non-smooth Submodular-Concave Functions: A Zeroth-Order Approach9h◆Observing Health Outcomes Using Remote Sensing Imagery and Geo-Context Guided Visual Transformer9h◆Rotation-free Online Handwritten Character Recognition Using Linear Recurrent Units9h◆Jointly Optimizing Debiased CTR and Uplift for Coupons Marketing: A Unified Causal Framework9h◆Discriminative Span as a Predictor of Synthetic Data Utility via Classifier Reconstruction9h◆Backdoor Channels Hidden in Latent Space: Extending Cryptographic Undetectability to Modern Neural Networks9h◆Amortized Neural Optimization for Pre-Layout Signal Integrity Design Space Exploration using Differentiable Surrogates9h◆
News/Data driven approach for Outdoor Channel Prediction in 5G and Beyond
arxiv
PublishedMay 6, 2026 at 4:00 AM
—neutral

Data driven approach for Outdoor Channel Prediction in 5G and Beyond

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2605.01777v1 Announce Type: cross Abstract: An evolution of Wireless Communications towards 5G and beyond provides improved user experience in terms of quality of services. Understanding and estimating Channel information plays crucial role in providing better user experience. Traditional meth

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Mentioned models
03
  • 01
    Linear Regression
  • 02
    Support Vector Regression
  • 03
    Decision Tree Regression
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#wireless-communications#5g#machine-learning#signal-processing

No replies yet. Be first.

Mentioned models
03
  • 01
    Linear Regression
  • 02
    Support Vector Regression
  • 03
    Decision Tree Regression
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#wireless-communications#5g#machine-learning#signal-processing

Related coverage

More from ARXIV
arxivGaussian Linear Functional Manifold Method for Massive Point Cloud Data9harxivMind the Gap: Navigating Inference with Optimal Transport Maps9harxivWhisTLE: Deeply Supervised, Text-Only Domain Adaptation for Small Pretrained Speech Recognition Transformers9harxivLearning Multi-Index Models with Hyper-Kernel Ridge Regression9h
The Bubble Brief
WEEKLY

Read wireless-communications insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews