Data detection with CFO uncertainty and nonlinearity for mmWave MIMO-OFDM Systems

Preety Priya, Debarati Sen

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)


The nonlinear distortions attributed by radio frequency power amplifiers (PAs) are inevitable in millimeter-wave (mmWave) systems due to the high frequency and large bandwidth. This nonlinearity induces multiplicative distortion and intercarrier interference in mmWave multiple-input-multiple-output (MIMO) orthogonal frequency-division multiplexing systems, which in collusion with a frequency-selective channel and a carrier frequency offset (CFO) brings great challenges to signal detection. Furthermore, the nonlinearity destroys orthogonality of the training pilots and degrades the estimation accuracy of the channel gains and the CFO. Also, the target posterior distribution for data detection becomes analytically intractable due to the nonlinearity. In this article, we present an importance-sampling-based framework for estimating the CFO in the presence of PA impairment, which utilizes few pilots and initializes the channel estimator. We propose a particle-filter-based iterative algorithm for data detection, where the intractable posterior distribution is approximated by weighted random measures. Furthermore, a sequential-maximum-likelihood-based semiblind channel estimator in the presence of nonlinearity is proposed. Performances of the estimator and the detector are evaluated by means of normalized mean square error, mean square error, and bit error rate. To validate the efficacy of the channel and the CFO estimator, the Cramer-Rao bound (CRB) and the weighted Bayesian CRB are derived, respectively.

Original languageEnglish
Pages (from-to)3734-3745
Number of pages12
JournalIEEE Systems Journal
Issue number3
Publication statusPublished - Sept 2022
Externally publishedYes


  • Millimeter-wave (mmWave) multiple-input-multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM)
  • nonlinear power amplifier
  • particle filter (PF)
  • sequential maximum likelihood (SQML) estimation

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