Improved QRD-QLD algorithm for low complexity MIMO decoding

Nimrod Peer; Yonathan Murin; Ron Dabora

doi:10.1109/LCOMM.2014.2350976

Improved QRD-QLD algorithm for low complexity MIMO decoding

Nimrod Peer, Yonathan Murin, Ron Dabora

Department of Electrical & Computer Engineering

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

In this paper we present a decoding algorithm for multiple-input multiple-output (MIMO) communications. The algorithm is based on the QRD-QLD algorithm (see, e.g., Radosavljevic et al. 2012), proposes an improved path metric computation, and achieves a low probability of error which is close to the single tree search sphere decoder, at low implementation complexity. The proposed improved path metric computation increases the reliability of combining the results of the partial searches into sequences representing the maximum likelihood (ML) and the counter ML hypotheses in soft input MIMO decoding. The excellent performance of the improved algorithm are demonstrated via numerical simulations.

Original language	English
Article number	2350976
Pages (from-to)	1703-1706
Number of pages	4
Journal	IEEE Communications Letters
Volume	18
Issue number	10
DOIs	https://doi.org/10.1109/LCOMM.2014.2350976
State	Published - 1 Oct 2014

Keywords

MIMO decoding
QR-QL decomposition

ASJC Scopus subject areas

Modeling and Simulation
Computer Science Applications
Electrical and Electronic Engineering

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10.1109/LCOMM.2014.2350976

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abstract = "In this paper we present a decoding algorithm for multiple-input multiple-output (MIMO) communications. The algorithm is based on the QRD-QLD algorithm (see, e.g., Radosavljevic et al. 2012), proposes an improved path metric computation, and achieves a low probability of error which is close to the single tree search sphere decoder, at low implementation complexity. The proposed improved path metric computation increases the reliability of combining the results of the partial searches into sequences representing the maximum likelihood (ML) and the counter ML hypotheses in soft input MIMO decoding. The excellent performance of the improved algorithm are demonstrated via numerical simulations.",

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AB - In this paper we present a decoding algorithm for multiple-input multiple-output (MIMO) communications. The algorithm is based on the QRD-QLD algorithm (see, e.g., Radosavljevic et al. 2012), proposes an improved path metric computation, and achieves a low probability of error which is close to the single tree search sphere decoder, at low implementation complexity. The proposed improved path metric computation increases the reliability of combining the results of the partial searches into sequences representing the maximum likelihood (ML) and the counter ML hypotheses in soft input MIMO decoding. The excellent performance of the improved algorithm are demonstrated via numerical simulations.

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Improved QRD-QLD algorithm for low complexity MIMO decoding

Abstract

Keywords

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