Conference proceeding
ON exact maximum-likelihood detection for non-coherent MIMO wireless systems: A branch-estimate-bound optimization framework
2008 IEEE International Symposium on Information Theory, pp.2017-2021
07/2008
DOI: 10.1109/ISIT.2008.4595343
Abstract
Fast fading wireless environments pose a great challenge for achieving high spectral efficiency in next generation wireless systems. Joint maximum-likelihood (ML) channel estimation and signal detection is of great theoretical and practical interest, especially for multiple-input multiple-output(MIMO) systems where the multiple channel coefficients need to be estimated. However, this is a hard combinatorial optimization problem, for which obtaining efficient exact algorithms has been elusive for the general MIMO systems. In this paper, we propose an efficient branch-estimate-bound non-coherent optimization framework which provably achieves the exact ML joint channel estimation and data detection for general MIMO systems. Numerical results indicate that the exact joint ML method can achieve substantial performance improvements over suboptimal methods including iterative channel estimation and signal detection. We also derive analytical bounds on the computational complexity of the new exact joint ML method and show that its average complexity approaches a constant times the length of the coherence time, as the SNR approaches infinity.
Details
- Title: Subtitle
- ON exact maximum-likelihood detection for non-coherent MIMO wireless systems: A branch-estimate-bound optimization framework
- Creators
- Weiyu Xu - California Institute of TechnologyMihailo Stojnic - California Institute of TechnologyBabak Hassibi - California Institute of Technology
- Resource Type
- Conference proceeding
- Publication Details
- 2008 IEEE International Symposium on Information Theory, pp.2017-2021
- DOI
- 10.1109/ISIT.2008.4595343
- ISSN
- 2157-8095
- eISSN
- 2157-8117
- Publisher
- IEEE
- Language
- English
- Date published
- 07/2008
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984197522502771
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