On exact maximum-likelihood detection for non-coherent MIMO wireless systems: A branch-estimate-bound optimization framework
- Creators
- Xu, Weiyu
- Stojnic, Mihailo
- Hassibi, Babak
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.
Additional Information
© 2008 IEEE. This work was supported in part by the National Science Foundation under grant no. CCR-0729203, by the David and Lucille Packard Foundation, and by Caltech's Lee Center for Advanced Networking.Attached Files
Published - 04595343.pdf
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Additional details
- Eprint ID
- 93418
- Resolver ID
- CaltechAUTHORS:20190304-085002366
- NSF
- CCR-0729203
- David and Lucile Packard Foundation
- Caltech Lee Center for Advanced Networking
- Created
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2019-03-04Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field