A Signal-Level Maximum Likelihood Detection Based on Partial Candidates for MIMO FBMC-QAM System With Two Prototype Filters

Dongkyu Sim, Kyeongyeon Kim, Chanhong Kim, Chungyong Lee

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

In this paper, we consider a signal-level maximum likelihood detection for multiple-input multiple-output filter-bank multicarrier-quadrature amplitude modulation (MIMO FBMC-QAM) system with two prototype filters. Under high-frequency selective channels, the conventional symbol-level maximum likelihood detection suffers from degraded bit error rate performances because it cannot utilize the oversampling characteristics of the signal-level. However, due to intrinsic interference and high computational complexity, a maximum likelihood detection cannot be applied directly to the signal-level. To solve this problem, we propose a signal-level maximum likelihood detection based on partial candidates. Utilizing the diagonal dominant characteristics of effective channels, the proposed signal-level maximum likelihood detection can reduce the effect of intrinsic interference with practical computational complexity. Moreover, a region ordering algorithm based on Euclidean distance can improve the efficiency of the proposed signal-level maximum likelihood detection. Simulation results show that the proposed signal-level maximum likelihood detection can outperform the bit error rate performance of the conventional symbol-level maximum likelihood detection.

Original languageEnglish
Article number8624275
Pages (from-to)2598-2608
Number of pages11
JournalIEEE Transactions on Vehicular Technology
Volume68
Issue number3
DOIs
Publication statusPublished - 2019 Mar

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Aerospace Engineering
  • Applied Mathematics
  • Electrical and Electronic Engineering

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