Near-ML MIMO detection algorithm with LR-aided fixed-complexity tree searching

Hyunsub Kim, Jangyong Park, Hyukyeon Lee, Jaeseok Kim

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


In this paper, we propose a low-complexity multiple-input multiple-output (MIMO) detection algorithm with lattice-reduction-aided fixed-complexity tree searching which is motivated by the fixed-complexity sphere decoder (FSD). As the proposed scheme generates a fixed tree whose size is much smaller than that of the full expansion in the FSD, the computational complexity is reduced considerably. Nevertheless, the proposed scheme achieves a near-maximum-likelihood (ML) performance with a large number of transmit antennas and a high-order modulation. The experimental results demonstrate that the performance degradation of the proposed scheme is less than 0.5 dB at the bit error rate (BER) of 10^{-5 for a 8 × 8 MIMO system with 256 QAM. Also, the proposed method reduces the complexity to about 1.23% of the corresponding FSD complexity.

Original languageEnglish
Article number6932446
Pages (from-to)2221-2224
Number of pages4
JournalIEEE Communications Letters
Issue number12
Publication statusPublished - 2014 Dec 1

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Computer Science Applications
  • Electrical and Electronic Engineering


Dive into the research topics of 'Near-ML MIMO detection algorithm with LR-aided fixed-complexity tree searching'. Together they form a unique fingerprint.

Cite this