Model-based compressed sensing algorithms for MIMO- OFDM channel estimation

  • Authors

    • P N. Jayanthi
    • S Ravishankar
    2018-03-10
    https://doi.org/10.14419/ijet.v7i2.4.10030
  • Sparsity, Compressedsensing, Channelestimation, Compressed Sampling Matching Pursuit, Model-Based Cosamp.

  • High data rates on the wireless channel can be achieved by combining orthogonal frequency division multiplexing (OFDM) with multiple input multiple output (MIMO) communication modulation scheme. MIMO-OFDM system impulse response of the channel is approximately sparse. Sparse channelestimation can be done using Compressive Sensing (CS) techniques. In this paper, a low complexity model based CoSaMp Compressive Sensing (CS) algorithm with conventional tools namely Least Square (LS) and Least Mean Square (LMS) are used for MIMO-OFDM channel estimation. Simulation results show amodel based CoSaMP for MIMO-OFDM channel estimation with LMS tool the Normalized Mean Square Error(NMSE)reduced by 34%with very reduced complexity.

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  • How to Cite

    N. Jayanthi, P., & Ravishankar, S. (2018). Model-based compressed sensing algorithms for MIMO- OFDM channel estimation. International Journal of Engineering & Technology, 7(2.4), 5-9. https://doi.org/10.14419/ijet.v7i2.4.10030