Statistical Pruning for Near Maximum Likelihood Detection of MIMO Systems
- Creators
- Cui, Tao
- Ho, Tracey
- Tellambura, Chintha
Abstract
We show a statistical pruning approach for maximum likelihood (ML) detection of multiple-input multiple-output (MIMO) systems. We present a general pruning strategy for sphere decoder (SD), which can also be applied to any tree search algorithms. Our pruning rules are effective especially for the case when SD has high complexity. Three specific pruning rules are given and discussed. From analyzing the union bound on the symbol error probability, we show that the diversity order of the deterministic pruning is only one by fixing the pruning probability. By choosing different pruning probability distribution functions, the statistical pruning can achieve arbitrary diversity orders and SNR gains. Our statistical pruning strategy thus achieves a flexible trade-off between complexity and performance.
Additional Information
© 2007 IEEE. This work has been supported in part by the Natural Sciences and Engineering Research Council of Canada, Informatics Circle of Research Excellence, the National Science Foundation under Grant CCF-0220039, and Caltech's Lee Center for Advanced Networking.Attached Files
Published - Cui2007p86852007_Ieee_International_Conference_On_Communications_Vols_1-14.pdf
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Additional details
- Eprint ID
- 19849
- Resolver ID
- CaltechAUTHORS:20100909-133322426
- Natural Sciences and Engineering Research Council of Canada (NSERC)
- Informatics Circle of Research Excellence
- CCF-0220039
- NSF
- Caltech Lee Center for Advanced Networking
- Created
-
2010-09-15Created from EPrint's datestamp field
- Updated
-
2021-11-08Created from EPrint's last_modified field
- Other Numbering System Name
- INSPEC Accession Number
- Other Numbering System Identifier
- 9875594