Suboptimal Algorithm of MLD Using Gradient Signal Search in Direction of Noise Enhancement for MIMO Channels

• KHINE Thet Htun

  Tokyo Institute of Technology

• FUKAWA Kazuhiko

  Tokyo Institute of Technology

• SUZUKI Hiroshi

  Tokyo Institute of Technology

Abstract

This paper proposes a suboptimal algorithm for the maximum likelihood detection (MLD) in multiple-input multiple-output (MIMO) communications. The proposed algorithm regards transmitted signals as continuous variables in the same way as a common method for the discrete optimization problem, and then searches for candidates of the transmitted signals in the direction of a modified gradient vector of the metric. The vector is almost proportional to the direction of the noise enhancement, from which zero-forcing (ZF) or minimum mean square error (MMSE) algorithms suffer. This method sets the initial guess to the solution by ZF or MMSE algorithms, which can be recursively calculated. Also, the proposed algorithm has the same complexity order as that of conventional suboptimal algorithms. Computer simulations demonstrate that it is much superior in BER performance to the conventional ones.

Journal

• IEICE Trans. Commun., B

  IEICE Trans. Commun., B 90 (6), 1424-1432, 2007-06-01

  The Institute of Electronics, Information and Communication Engineers

Keywords

• MIMO
• MLD
• discrete optimization problem
• gradient-based algorithm
• low-complexity
• ZF
• MMSE
• noise enhancement

Details

• CRID: 1573105977345882752
• NII Article ID: 110007519408
• NII Book ID: AA10826261
• ISSN: 09168516
• Text Lang: en
• Data Source:
  • CiNii Articles