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Compressive Channel Estimation Based on Weighted IRLS in FDD Massive MIMO

  • Wei LuEmail author
  • Yongliang Wang
  • Qiqing Fang
  • Shixin Peng
Article
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Abstract

In this letter a weighted iteratively reweighted least-square (IRLS) algorithm is proposed for FDD massive MIMO channel estimation. The priori support information is merged into the weighted IRLS to improve the recovery performance. The priori support information is obtained from the uplink channel by reciprocity in angle domain, and a support estimation algorithm is proposed from the analysis of basis mismatch and angle deviation between uplink and downlink which is more practical in the real scenario. A brief convergence analysis of weighted IRLS is given out. Simulations show that the proposed weighted IRLS outperforms the standard IRLS, subspace pursuit (SP) and weighted SP.

Keywords

Weighted IRLS Channel estimation Massive MIMO FDD 

Notes

Acknowledgements

This work is supported in part by the National Science Foundation of China (Nos. 61601509 and 61601334), the China Postdoctoral Science Foundation Grant (Nos. 2016M603045 and 2018M632889) and the self-determined research funds of CCNU(CCNU18QN007) from the colleges basic research and operation of MOE.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Air Force Early Warning AcademyWuhanChina
  2. 2.National Engineering Research Centre for E-LearningCentral China Normal UniversityWuhanChina

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