Wireless Personal Communications

, Volume 94, Issue 4, pp 3303–3325 | Cite as

Compressed Sensing of Sparse Multipath MIMO Channels with Superimposed Training Sequence

  • Bilal Amin
  • Babar Mansoor
  • Syed Junaid NawazEmail author
  • Shree K. Sharma
  • Mohmammad N. Patwary


Recent advances in multiple-input multiple-output (MIMO) systems have renewed the interests of researchers to further explore this area for addressing various dynamic challenges of emerging radio communication networks. Various measurement campaigns reported recently in the literature show that physical multipath MIMO channels exhibit sparse impulse response structure in various outdoor radio propagation environments. Therefore, a comprehensive physical description of sparse multipath MIMO channels is presented in first part of this paper. Superimposing a training sequence (low power, periodic) over the information sequence offers an improvement in the spectral efficiency by avoiding the use of dedicated time/frequency slots for the training sequence, which is unlike the traditional schemes. The main contribution of this paper includes three superimposed training (SiT) sequence based channel estimation techniques for sparse multipath MIMO channels. The proposed techniques exploit the compressed sensing theory and prior available knowledge of channel’s sparsity. The proposed sparse MIMO channel estimation techniques are named as, SiT based compressed channel sensing (SiT-CCS), SiT based hardlimit thresholding with CCS (SiT-ThCCS), and SiT training based match pursuit (SiT-MP). Bit error rate (BER) and normalized channel mean square error are used as metrics for the simulation analysis to gauge the performance of proposed techniques. A comparison of the proposed schemes with a notable first order statistics based SiT least squares (SiT-LS) estimation technique is presented to establish the improvements achieved by the proposed schemes. For sparse multipath time-invariant MIMO communication channels, it is observed that SiT-CCS, SiT-MP, and SiT-ThCCS can provide an improvement up to 2, 3.5, and 5.2 dB in the MSE at signal to noise ratio (SNR) of 12 dB when compared to SiT-LS, respectively. Moreover, for \(\mathrm {BER}=10^{-1.9}\), the proposed SiT-CCS, SiT-MP, and SiT-ThCCS, compared to SiT-LS, can offer a gain of about 1, 2.5, and 3.5 dB in the SNR, respectively. The performance gain in MSE and BER is observed to improve with an increase in the channel sparsity.


MIMO Superimposed training First-order statistics Compressed sensing Channel estimation 



The first three authors would like to acknowledge the partial support by EU ATOM-690750 research project approved under the call H2020-MSCA-RISE-2015. The fourth author would like to acknowledge the partial financial support by the National Research Fund, Luxembourg under the CORE project SeMiGod.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Bilal Amin
    • 1
  • Babar Mansoor
    • 2
  • Syed Junaid Nawaz
    • 2
    Email author
  • Shree K. Sharma
    • 3
  • Mohmammad N. Patwary
    • 4
  1. 1.Department of Electrical EngineeringCOMSATS Institute of Information Technology (CIIT)LahorePakistan
  2. 2.Department of Electrical EngineeringCOMSATS Institute of Information Technology (CIIT)IslamabadPakistan
  3. 3.SnT - securityandtrust.luUniversity of LuxembourgKirchberg, Luxembourg-CityLuxembourg
  4. 4.Faculty of Computing Engineering and SciencesStaffordshire UniversityStoke-on-TrentUK

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