Multi-Frequency Wireless Channel Measurements and Modeling in Urban Macro Scenarios
IEEE Transactions on Vehicular Technology(2025)
Abstract
Channel similarity between different frequency bands can be utilized by the beyond fifth generation (B5G) and sixth generation (6 G) multi-frequency collaboration technologies to improve the capacity, coverage, and spectrum efficiency of wireless communication systems. To investigate the similarity of channels across different frequency bands, multi-frequency channel measurements at 0.7, 2.3, and 3.7 GHz in urban macro (UMa) scenarios are conducted. Besides, quantitative metrics for characterizing channel similarity at different frequency bands are given. Channel characteristics such as the delay power spectral density (PSD), angular PSD, large-scale parameters (LSPs), and clustering results at different frequency bands are compared. Results show that most multipath components (MPCs) exist across all frequency bands, while certain MPCs appear at one band and disappear at others. Based on the observation, a novel multi-frequency channel model (MFCM) considering the birth and death of clusters at different frequency bands is proposed. The proposed model is compared with two 5 G standard channel models, i.e., 3GPP TR 38.901 and quasi deterministic radio channel generator (QuaDRiGa), which assume that the MPCs observed at different frequency bands are exactly the same and are inconsistent with measurements. Results indicate that the proposed model outperforms two 5 G standard channel models in characterizing the similarity of channels at different frequency bands.
MoreTranslated text
Key words
Multi-frequency channel measurements and modeling,LSPs,clustering,channel similarity,birth-death process
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined