Research on Determinisitic Channel Capacity of MIMO System Based on Ray-Tracing Model in Complex Indoor Environment
2024 14th International Symposium on Antennas, Propagation and EM Theory (ISAPE)(2024)
School of Physics
Abstract
In this paper, the channel capacity is investigated as a crucial metric for evaluating the transmission capability of channel information, which is essential for the design and assessment of communication system performance. A deterministic channel model based on the reverse ray tracing method has been employed to establish a multi-input multi-output (MIMO) system in an indoor scenario. The multipath information of rays in the MIMO system under a deterministic channel was accurately calculated through the algorithm. The channel matrix was constructed using the data output by the algorithm, and the channel capacities of different MIMO systems in indoor scenarios were computed based on the MIMO system channel capacity formula. To verify the significance of MIMO systems in enhancing channel capacity, the channel capacities of single-input single-output (SISO), multi-input single-output (MISO), and MIMO systems, as well as MIMO systems with different numbers of antennas, were compared at different frequency points while keeping the horizontal distance of the transceiver antennas constant. Cumulative distribution function (CDF) graphs of each system were plotted, and the average capacity of each system was calculated. The results confirmed that MIMO systems can significantly improve the channel capacity of the system, and the average channel capacity increases with the order of the MIMO system. Additionally, various factors affecting the channel capacity of MIMO systems under deterministic channels, including frequency, line-of-sight (LOS) and non-line-of-sight (NLOS) propagation environments, and the number of antennas, were thoroughly discussed. The performance of MIMO systems was analyzed in terms of the singular values of the H matrix, the condition number of the channel matrix, and the correlation coefficients between antennas in the R normalized autocorrelation matrix, with the simulation results being explained and discussed.
MoreTranslated text
Key words
ray-tracing,MIMO system,deterministic channel model,channel capacity,channel matrix
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