Skip to main content
SpringerLink
Log in

Decomposable Atomic Norm Minimization Channel Estimation for Millimeter Wave MIMO-OFDM Systems

  • Conference paper
  • First Online:
Book cover Wireless Algorithms, Systems, and Applications (WASA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11604))

Abstract

This paper addresses the problem of downlink channel estimation in millimeter wave (mmWave) massive multiple input multiple output (MIMO)-orthogonal frequency division multiplexing (OFDM) systems, where wideband frequency selective fading channels are considered. By exploiting the sparse scattering nature of mmWave channel, we consider channel estimation as three dimensional (3D) (including angles of departure/arrival and the time delay) line spectrum estimation. To achieve super-resolution channel estimation, we propose a decomposable 3D atomic norm minimization estimation method. This method decomposes the 3D estimation problem into two separate dimensions to reduce the computational complexity, where time delays are estimated only in the OFDM system. Simulation results show that the proposed method can achieve comparable mean square errors as the conventional vectorized ANM at much lower computational complexity.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Since the number of subcarriers used for training is proportional to the computational complexity of channel estimation, the complexity can be mitigated with as few subcarriers as possible.

References

  1. Heath, R.W., Gonzlez-Prelcic, N., Rangan, S., Roh, W., Sayeed, A.M.: An overview of signal processing techniques for millimeter wave MIMO systems. IEEE J. Sel. Topics Sig. Process. 10(3), 436–453 (2016)

    Google Scholar 

  2. Rangan, S., Rappaport, T.S., Erkip, E.: Millimeter-wave cellular wireless networks: potentials and challenges. Proc. IEEE 102(3), 366–385 (2014)

    Google Scholar 

  3. Larsson, E.G., Edfors, O., Tufvesson, F., Marzetta, T.L.: Massive MIMO for next generation wireless systems. IEEE Commun. Mag. 52(2), 186–195 (2014)

    Google Scholar 

  4. Dai, L., Wang, Z., Yang, Z.: Spectrally efficient time-frequency training OFDM for mobile large-scale MIMO systems. IEEE J. Sel. Areas Commun. 31(2), 251–263 (2013)

    Google Scholar 

  5. Alkhateeb, A., Ayach, O.E., Leus, G., Heath, R.W.: Channel estimation and hybrid precoding for millimeter wave cellular systems. IEEE J. Sel. Topics Sig. Process. 8(5), 831–846 (2014)

    Google Scholar 

  6. Alkhateeb, A., Heath, R.W.: Frequency selective hybrid precoding for limited feedback millimeter wave systems. IEEE Trans. Commun. 64(5), 1801–1818 (2016)

    Google Scholar 

  7. Cai, Z., Duan, Y., Bourgeois, A.G.: Delay efficient opportunistic routing in asynchronous multi-channel cognitive radio networks. J. Comb. Optim. 29(4), 815–835 (2015). https://doi.org/10.1007/s10878-013-9623-y

  8. Duan, Y., Liu, G., Cai, Z.: Opportunistic channel-hopping based effective rendezvous establishment in cognitive radio networks. In: Wang, X., Zheng, R., Jing, T., Xing, K. (eds.) WASA 2012. LNCS, vol. 7405, pp. 324–336. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31869-6_28

    Google Scholar 

  9. Cheng, S., Cai, Z., Li, J., Gao, H.: Extracting kernel dataset from big sensory data in wireless sensor networks. IEEE Trans. Knowl. Data Eng. 29(4), 813–827 (2017)

    Google Scholar 

  10. Li, J., Cheng, S., Li, Y., Cai, Z.: Approximate holistic aggregation in wireless sensor networks. In: 2015 IEEE 35th International Conference on Distributed Computing Systems, pp. 740–741, June 2015

    Google Scholar 

  11. He, Z., Cai, Z., Cheng, S., Wang, X.: Approximate aggregation for tracking quantiles and range countings in wireless sensor networks. Theoret. Comput. Sci. 607, 07 (2015)

    Google Scholar 

  12. Cheng, S., Cai, Z., Li, J.: Curve query processing in wireless sensor networks. IEEE Trans. Veh. Technol. 64(11), 5198–5209 (2015)

    Google Scholar 

  13. Mao, J., Tian, W., Li, P., Wei, T., Liang, Z.: Phishing-alarm: robust and efficient phishing detection via page component similarity. IEEE Access 5, 17 020–17 030 (2017)

    Google Scholar 

  14. Mao, J., Tian, W., Jiang, J., He, Z., Zhou, Z., Liu, J.: Understanding structure-based social network de-anonymization techniques via empirical analysis. EURASIP J. Wirel. Commun. Netw. 2018(1), 279 (2018). https://doi.org/10.1186/s13638-018-1291-2

  15. Jia, Y., Chen, Y., Dong, X., Saxena, P., Mao, J., Liang, Z.: Man-in-the-browser-cache: persisting HTTPS attacks via browser cache poisoning. Comput. Secur. 55, 08 (2015)

    Google Scholar 

  16. Mao, J., et al.: Phishing page detection via learning classifiers from page layout feature. EURASIP J. Wirel. Commun. Netw. 2019(1), 43 (2019). https://doi.org/10.1186/s13638-019-1361-0

  17. Berger, C.R., Wang, Z., Huang, J., Zhou, S.: Application of compressive sensing to sparse channel estimation. IEEE Commun. Mag. 48(11), 164–174 (2010)

    Google Scholar 

  18. Wen, C., Jin, S., Wong, K., Chen, J., Ting, P.: Channel estimation for massive MIMO using Gaussian-mixture Bayesian learning. IEEE Trans. Wirel. Commun. 14(3), 1356–1368 (2015)

    Google Scholar 

  19. Zhou, Z., Fang, J., Yang, L., Li, H., Chen, Z., Blum, R.S.: Low-rank tensor decomposition-aided channel estimation for millimeter wave MIMO-OFDM systems. IEEE J. Sel. Areas Commun. 35(7), 1524–1538 (2017)

    Google Scholar 

  20. Gao, Z., Dai, L., Wang, Z., Chen, S.: Spatially common sparsity based adaptive channel estimation and feedback for FDD massive MIMO. IEEE Trans. Sig. Process. 63(23), 6169–6183 (2015)

    Google Scholar 

  21. Nion, D., Sidiropoulos, N.D.: Tensor algebra and multidimensional harmonic retrieval in signal processing for MIMO radar. IEEE Trans. Sig. Process. 58(11), 5693–5705 (2010)

    Google Scholar 

  22. Zhang, Z., Wang, Y., Tian, Z.: Efficient two-dimensional line spectrum estimation based on decoupled atomic norm minimization (2018)

    Google Scholar 

  23. Pejoski, S., Kafedziski, V.: Estimation of sparse time dispersive channels in pilot aided OFDM using atomic norm. IEEE Wirel. Commun. Lett. 4(4), 397–400 (2015)

    Google Scholar 

  24. Wang, Y., Xu, P., Tian, Z.: Efficient channel estimation for massive MIMO systems via truncated two-dimensional atomic norm minimization. In: 2017 IEEE International Conference on Communications (ICC), pp. 1–6, May 2017

    Google Scholar 

  25. Yang, Z., Xie, L., Stoica, P.: Vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super-resolution. IEEE Trans. Inf. Theory 62(6), 3685–3701 (2016)

    Google Scholar 

  26. Akdeniz, M.R., et al.: Millimeter wave channel modeling and cellular capacity evaluation. IEEE J. Sel. Areas Commun. 32(6), 1164–1179 (2014)

    Google Scholar 

  27. Zhang, X., Xu, L., Xu, L., Xu, D.: Direction of departure (DOD) and direction of arrival (DOA) estimation in MIMO radar with reduced-dimension music. IEEE Commun. Lett. 14(12), 1161–1163 (2010)

    Google Scholar 

  28. Grant, M., Boyd, S.: CVX: Matlab software for disciplined convex programming, version 2.1, March 2014. http://cvxr.com/cvx

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 61871023 and 61572070) and the Fundamental Research Funds for the Central Universities (Grant No. 2017YJS035 and 2016JBZ003).

Author information

Authors and Affiliations

  1. School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, 100044, China

    Qianwen An, Tao Jing, Yingkun Wen & Yan Huo

  2. Computing Sciences Department, University of Hartford, West Hartford, CT, USA

    Zhuojun Duan

Authors
  1. Qianwen An
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tao Jing
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yingkun Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhuojun Duan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yan Huo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yan Huo .

Editor information

Editors and Affiliations

  1. University of Hawaii at Manoa, Honolulu, HI, USA

    Edoardo S. Biagioni

  2. University of Hawaii at Manoa, Honolulu, USA

    Yao Zheng

  3. Harbin Institute of Technology, Harbin, China

    Siyao Cheng

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

An, Q., Jing, T., Wen, Y., Duan, Z., Huo, Y. (2019). Decomposable Atomic Norm Minimization Channel Estimation for Millimeter Wave MIMO-OFDM Systems. In: Biagioni, E., Zheng, Y., Cheng, S. (eds) Wireless Algorithms, Systems, and Applications. WASA 2019. Lecture Notes in Computer Science(), vol 11604. Springer, Cham. https://doi.org/10.1007/978-3-030-23597-0_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-23597-0_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-23596-3

  • Online ISBN: 978-3-030-23597-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation