Pilot allocation for massive MIMO compressed sensing based channel estimation

Document Type : Persian Original Article

Authors

1 Electrical Engineering, KN Toosi university of technology.

2 Electrical engineering, K N Toosi university of technology

Abstract

Massive Multiple-Input Multiple-Output (mMIMO) is a promising approach for the next generation wireless telecommunication systems. In these systems, having a suitable approach for channel estimation is mandatory in order to increase the data rate and spectral efficiency. Distributed Compressed Sensing (DCS) is prominent in extracting joint sparse channel state information (CSI). Here, we have utilized Alternating Direction Method of Multipliers (ADMM) approach to generate quasi-orthogonal pilot sequences, in order to improve the channel estimation approach based on DCS approach. In simulation results, it is represented that ADMM-based pilot sequences are very powerful in extracting CSI of the joint sparse channel ensembles.

Keywords

References

  [1]     L. Chen, A. Liu, X. Yuan, “Structured turbo compressed sensing for massive MIMO channel estimation using a Markov prior,” IEEE Trans. Veh. Technol. vol. 67, no. 5, pp. 4635–4639, Jul. 2018.
  [2]     P. Cheng, Z. Chen, Y. Rui, Y. J. Guo, L. Gui, M. Tao, Q. T. Zhang, “Channel estimation for OFDM systems over doubly selective channels: A distributed compressive sensing based approach,” IEEE Trans. Commun., vol. 61, no. 10, pp. 4173–4185 Nov. 2013.
  [3]     Y. Nan, L. Zhang, and X. Sun, “Efficient downlink channel estimation scheme based on block-0structured compressive sensing for TDD massive MU-MIMO systems,” IEEE Wireless Commun. Lett., vol. 4, no. 4, pp. 345–348, Aug. 2015.
  [4]     X. He, R. Song, W. P. Zhu, “Pilot allocation for sparse channel estimation in MIMO-OFDM systems,” IEEE Trans. Circuits Syst. II, Exp. Briefs. vol. 60, no. 9, pp. 612–616, Oct. 2013.
  [5]     X. He, R. Song, W. P. Zhu, “Pilot allocation for distributed-compressed-sensing-based sparse channel estimation in MIMO-OFDM systems,” IEEE Trans. Veh. Technol., vol. 65, no. 5, pp. 2990–3004, May 2016.
  [6]     C Qi, G Yue, L Wu, N Arumugam, “Pilot design for sparse channel estimation in ofdm-based cognitive radio systems,” IEEE Trans. Veh. Technol., vol. 63, no. 2, pp. 982–987, Feb. 2014.
  [7]     R. Mohammadian, A. Amini, and B. Hossein Khalaj, “Compressive Sensing-Based Pilot Design for Sparse Channel Estimation in OFDM Systems,” IEEE Communications Letters, vol. 21, no. 1, pp. 4–7, Jan. 2017.
  [8]     A. Waseem, A. Naveed, S. Ali, M. Arshad, H. Anis, and I. M. Qureshi, “Compressive Sensing Based Channel Estimation for Massive MIMO Communication Systems,” Wireless Commun. Mobile Comput., vol. 2019, ID 6374764, pp.15.
  [9]     X. Chao, Z. Jianhua, and Y. Changchuan, “Non-orthogonal pilot pattern for sparse channel estimation in large-scale MIMO-OFDM system,” The J. of China Univ. of Posts and Telecommun., vol. 23, no. 4, pp. 63-68, Aug. 2016. 
[10]     D. Lee, “MIMO OFDM channel estimation via block stage-wise orthogonal matching pursuit,” IEEE Commun. Lett., vol. 20, no. 7, pp. 2115–2118, Aug. 2016.
[11]     T. T. Cai and L. Wang, “Orthogonal matching pursuit for sparse signal recovery with noise,” IEEE Trans. Inf. Theory, vol. 57, no. 7, pp. 4680- 4688, Aug. 2011.
[12]     Z. Gao, L. Dai, W. Dai, B. Shim, and Z. Wang, “Structured  compressive sensing-based spatio-temporal joint channel estimation for FDD massive MIMO,” IEEE Trans. Commun., vol. 64, no. 2, pp. 601-617, Feb. 2016.
[13]     Q. Qin, L. Gui, B. Gong, and S. Luo, ‘‘Sparse channel estimation for massive MIMO-OFDM systems over time-varying channels,’’ IEEE Access, vol. 6, pp. 33740–33751, 2018.
[14]     M.F. Duarte, S. Sarvotham, D. Baron, M.B. Wakin, R.G. Baraniuk, “Distributed compressed sensing of jointly sparse signals,” in Proc. Asilomar Conf. Signals, Syst., Comput. (IEEE, Pacific Grove, 2005), pp. 1537–1541.
 
 
 
 
[15]     P. Cheng, Z. Chen, Y. Rui, Y.J. Guo, L. Gui, M. Tao, Q.T. Zhang, “Channel estimation for ofdm systems over doubly selective channels: a distributed compressive sensing based approach,” IEEE Trans. Commun. vol. 61, no. 10, pp. 4173–4185, Oct. 2013.
[16]     L. Xu, K. Niu, Z. He, W. Xu, Z. Zheng, “MIMO channel estimation based on distributed compressed sensing for LTE-advanced,” in Proc. 9th ICICS. (IEEE, Tainan, 2013), pp. 1–5.
[17]     X. Rao, V. K. N. Lau, “Distributed compressive CSIT estimation and feedback for FDD multi-user massive MIMO systems,” IEEE Trans. Signal Process. vol. 62, no. 12, pp. 3261–3271, Dec. 2014.
[18]     T. Zhang, “Adaptive forward-backward greedy algorithm for learning sparse representations,” IEEE Trans. Inf. Theory, vol. 57, no. 7, pp. 4689–4708, Jul. 2011.
[19]     Z. Huang, R. Hu, Y. Guo, E. Chan-Tin, and Y. Gong, “DP-ADMM: ADMM-Based distributed learning with differential privacy,” IEEE Transactions on Information Forensics and Security, vol. 15, no. 4, pp. 1002–1012, Jul. 2019.
[20]     M. V. Afonso, J. M. Bioucas-Dias, and M. A. T. Figueiredo “Fast Image Recovery Using Variable Splitting and Constrained Optimization,” IEEE Trans. on Image Processing,vol. 19, no. 9, pp. 2345–2356, Sept. 2010.   
[21]     A. Akbarpour-Kasgari, M. Ardebilipour, “Massive MIMO-OFDM Channel Estimation via Distributed Compressed Sensing,” IEEE Wireless Commun. Lett., vol. 8, no. 2, pp. 376–379, Feb. 2019.
[22]     3GPP, TS 36.101 (V12.9.0), “User equipment radio transmission and reception (Rel. 12),” Oct. 2015.
Journal of Soft Computing and Information Technology
Volume 10, Issue 1 - Serial Number 31
Spring
April 2021
Pages 63-71

Files

Share

How to cite

Statistics