Compressive Channel Estimation Based on Weighted IRLS in FDD Massive MIMO

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


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.


Weighted IRLS Channel estimation Massive MIMO FDD 



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.


  1. 1.
    Chartrand, R., Yin, W. (2008). Iteratively reweighted algorithms for compressed sensing. In Proceedings of 2008 IEEE international conference on acoustics, speech and signal processing.Google Scholar
  2. 2.
    Borries, R. V., Miosso, C., Potes, C. (2007). Compressed sensing using priori information. In Proceedings of the 2nd IEEE international workshop on computational advances in multi-sensor adaptive.Google Scholar
  3. 3.
    Friedlander, M. P., Mansour, H., Saab, R., & Yilmaz, Ö. (2012). Recovering compressively sampled signal using partial support information. IEEE Transactions on Information Theory, 58(2), 1122–1134.MathSciNetCrossRefGoogle Scholar
  4. 4.
    Tseng, C. C., Wu, J. Y., & Lee, T. S. (2016). Enhanced compressive downlink CSI recovery for FDD masssive MIMO systems using weighted block l1 minimization. IEEE Transactions on Communications, 64(3), 1055–1066.CrossRefGoogle Scholar
  5. 5.
    Hugl, K., Kalliola, K., Laurila, J. (2002). Spatial reciprocity of uplink and downlink radio channel in FDD systems. In Proceedings of 2002 COST 273 technical document TD (Vol. 66, p. 7).Google Scholar
  6. 6.
    Ugurlu, U., Wichman, R., Ribeiro, C. B., & Wijting, C. (2016). A multipath extraction-based CSI acquisition method for FDD cellular networks with massive antenna arrays. IEEE Transactions on Wireless Communications, 15(4), 2940–2953.CrossRefGoogle Scholar
  7. 7.
    Ding, Y., Rao, B. D. (2015). Channel estimation using joint dictionary learning in FDD massive MIMO system. In Proceedings of IEEE global conference on signal and information processing.Google Scholar
  8. 8.
    Liu, A., Zhu, F. B., & Lau, V. K. N. (2017). Close-loop autonomous pilot and compressive CSIT feedback resource adaption in Multi-user FDD massive MIMO systems. IEEE Transactions on Signal Processing, 65(1), 173–183.MathSciNetCrossRefGoogle Scholar
  9. 9.
    Lu, W., Liu, Y. Z., Wang, D. S. (2011). Compressed sensing in spatial MIMO channels. In Proceedings of 2011 2nd international conference on wireless VITAE.Google Scholar
  10. 10.
    Gorodnitsky, I. F., & Rao, B. D. (1997). Sparse signal reconstruction from limited data using FOCUSS: A reweighted minimum norm algorithm. IEEE Transaction on Signal Processing, 45(3), 600–616.CrossRefGoogle Scholar
  11. 11.
    Campbell, S. L., & Meyer, C. D. (1979). Generalized inverse of linear transformations. London, UK: Pitman.zbMATHGoogle Scholar
  12. 12.
    Dai, W., & Milenkovic, O. (2009). Subspace pursuit for compressive sensing signal reconstruction. IEEE Transaction on Information Theory, 55(5), 2230–2249.MathSciNetCrossRefGoogle Scholar
  13. 13.
    Nan, Y., Zhang, L., & Sun, X. (2017). Weighted compressive sensing based uplink channel estimation for time division duplex massive multi-input multi-output systems. IET Communications, 11(3), 355–361.CrossRefGoogle Scholar
  14. 14.
    Qin, L., Lin, Z., She, Y., & Zhang, C. (2013). A comparision of typical lp minimization algorithms. Nerocomputing, 119, 413–424.CrossRefGoogle Scholar

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

Personalised recommendations