Tensor-Based Near-Field Localization Using Massive Antenna Arrays
IEEE Transactions on Signal Processing(2021)
Kazan Natl Res Tech Univ
Abstract
In this paper, the Tensor-Based Near-Field Localization (TeNFiLoc) algorithm is proposed for channel estimation and user localization on the uplink of a multi-carrier wireless communication system with a massive antenna array. OFDM is used as the modulation scheme and the user can be located in the near-field of the array. The exact spherical model of the impinging wavefronts allows TeNFiLoc to localize and identify the reflected paths and distinguish them from the Line-of-Sight (LoS) path. Some additional processing generally allows TeNFiLoc to localize user even in non-LoS scenarios, when the LoS path may be blocked or shadowed, provided that the number of reflected paths that reach the receiver is not less than three. It is also shown that perfect knowledge of the transmitted data is not necessarily required to perform channel estimation and user localization. Using a specific design of the transmitted signal, an additional low throughput, highly reliable communication link to the receiver can be established. Simulation results demonstrate the excellent localization accuracy of the TeNFiLoc algorithm and its applicability in practical scenarios.
MoreTranslated text
Key words
Tensors,Channel estimation,Antenna arrays,OFDM,Signal processing algorithms,Location awareness,Array signal processing,Near-field,exact spherical wavefront,SECSI,canonical-polyadic,user localization,channel estimation
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