Partition-Based Hybrid Decoding (PHD): A Class of ML Decoding Schemes for MIMO Signals Based on Tree Partitioning and Combined Depth- and Breadth-First Search

 

J. I. Park¹, Y. Lee², S. Yoon*³

 

¹ College of Information and Communication Engineering Sungkyunwan University Suwon, Korea. *syoon@skku.edu.

² College of Information and Communication Engineering Sungkyunwan University Suwon, Korea.

³ College of Information and Communication Engineering Sungkyunwan University Suwon, Korea.

 

ABSTRACT

In this paper, we propose a hybrid maximum likelihood (ML) decoding scheme for multiple-input multiple-output (MIMO) systems. After partitioning the searching tree into several stages, the proposed scheme adopts the combination of depth- and breadth-first search methods in an organized way. Taking the number of stages, the size of signal constellation, and the number of antennas as the parameter of the scheme, we provide extensive simulation results for various MIMO communication conditions. Numerical results indicate that, when the depth- and breadth-first search methods are employed appropriately, the proposed scheme exhibits substantially lower computational complexity than conventional ML decoders while maintaining the ML bit error performance.

Keywords: Hybrid decoding, multiple input multiple output (MIMO), maximum likelihood detection, tree partitioning.

 

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Acknowledgements

This research was supported by the National Research Foundation (NRF) of Korea under Grants 2012-0005066 and 2012R1A2A2A01045887 with funding from the Ministry of Education, Science and Technology (MEST), Korea, by the Information Technology Research Center (ITRC) program of the National IT Industry Promotion Agency under Grants NIPA-2012-H0301-12-1005 and NIPA-2012-H0301-12-2005 with funding from the Ministry of Knowledge Economy (MKE), Korea, and by National GNSS Research Center program of Defense Acquisition Program Administration and Agency for Defense Development.

 

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