A Bayesian approach for PAPR and MUI reduction in OFDM-based massive MIMO systems

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Orthogonal frequency division multiplexing (OFDM) based massive multiple-input multiple-output (MIMO) downlink systems face the issues of high peak-to-average power ratio (PAPR) and multiuser interference (MUI) which significantly affect their performance. The solution lies in finding an OFDM-modulated signal that possesses a low PAPR and also enables MUI cancellation. In this paper, a com-parative analysis has been performed based on a Bayesian PAPR reduction algorithm. This method models the problem into a hierarchical truncated Gaussian mixture prior (TGM) model which makes use of the redundant degrees of freedom of the transmission array. This leads to a low PAPR signal as most of the samples are concentrated on the boundaries. A variational expectation-maximization (EM) tactic is incorporated to obtain an estimate of the hyperparameters. This is followed by the implementation of the generalized approximate message passing (GAMP) algorithm to reduce the complexity of computation. MATLAB simulations show a significant improvement in PAPR reduction and MUI cancellation with this Bayesian approach leading to better power efficiency and system performance.

     


  • Keywords


    Bayesian Learning; EM; GAMP; Massive MIMO; MUI; OFDM; PAPR; TGM.

  • References


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Article ID: 19427
 
DOI: 10.14419/ijet.v7i4.19427




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