Accurate sampling timing acquisition for baseband OFDM power-line communication in non-gaussian noise

Chen Chen, Yun Chen, Na Ding, Yizhi Wang, Jia Chin Lin, Xiaoyang Zeng, Defeng David Huang

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

In this paper, a novel technique is proposed to address the joint sampling timing acquisition for baseband and broadband power-line communication (BB-PLC) systems using Orthogonal-Frequency-Division-Multiplexing (OFDM), including the sampling phase offset (SPO) and the sampling clock offset (SCO). Under pairwise correlation and joint Gaussian assumption of received signals in frequency domain, an approximated form of the log-likelihood function is derived. Instead of a high complexity two-dimension grid-search on the likelihood function, a five-step method is employed for accurate estimations. Several variants are presented in the same framework with different complexities. Unlike conventional pilot-assisted schemes using the extra phase rotations within one OFDM block, the proposed technique turns to the phase rotations between adjacent OFDM blocks. Analytical expressions of the variances and biases are derived. Extensive simulation results indicate significant performance improvements over conventional schemes. Additionally, effects of several noise models including non-Gaussianity, cyclo-stationarity, and temporal correlation are analyzed and simulated. Robustness of the proposed technique against violation of the joint Gaussian assumption is also verified by simulations.

Original languageEnglish
Article number6466340
Pages (from-to)1608-1620
Number of pages13
JournalIEEE Transactions on Communications
Volume61
Issue number4
DOIs
StatePublished - 2013

Keywords

  • OFDM
  • baseband system
  • non-Gaussian noise
  • power-line communication
  • sampling clock offset
  • sampling phase offset

Fingerprint

Dive into the research topics of 'Accurate sampling timing acquisition for baseband OFDM power-line communication in non-gaussian noise'. Together they form a unique fingerprint.

Cite this