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MIMO Detection by Lagrangian Dual Maximum-Likelihood Relaxation: Reinterpreting Regularized Lattice Decoding
The Chinese University of Hong Kong.
The Chinese University of Hong Kong.
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0001-6630-243X
2014 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 62, no 2, 511-524 p.Article in journal (Refereed) Published
Abstract [en]

This paper considers lattice decoding for multi-input multi-output (MIMO) detection under PAM constellations. A key aspect of lattice decoding is that it relaxes the symbol bound constraints in the optimal maximum-likelihood (ML) detector for faster implementations. It is known that such a symbol bound relaxation may lead to a damaging effect on the system performance. For this reason, regularization was proposed to mitigate the out-of-bound symbol effects in lattice decoding. However, minimum mean square error (MMSE) regularization is the only method of choice for regularization in the present literature. We propose a systematic regularization optimization approach considering a Lagrangian dual relaxation (LDR) of the ML detection problem. As it turns out, the proposed LDR formulation is to find the best diagonally regularized lattice decoder to approximate the ML detector, and all diagonal regularizations, including the MMSE regularization, can be subsumed under the LDR formalism. We show that for the 2-PAM case, strong duality holds between the LDR and ML problems. Also, for general PAM, we prove that the LDR problem yields a duality gap no worse than that of the well-known semidefinite relaxation method. To physically realize the proposed LDR, the projected subgradient method is employed to handle the LDR problem so that the best regularization can be found. The resultant method can physically be viewed as an adaptive symbol bound control wherein regularized lattice decoding is recursively performed to correct the decision. Simulation results show that the proposed LDR approach can outperform the conventional MMSE-based lattice decoding approach.

Place, publisher, year, edition, pages
2014. Vol. 62, no 2, 511-524 p.
Keyword [en]
lattice decoding, lattice reduction, MIMO detection, Lagrangian duality, regularization
National Category
Signal Processing
Research subject
URN: urn:nbn:se:kth:diva-136959DOI: 10.1109/TSP.2013.2292040ISI: 000330291700020ScopusID: 2-s2.0-84892391939OAI: diva2:677584
EU, European Research Council, 228044

QC 20140227. Updated from accepted to published.

Available from: 2013-12-10 Created: 2013-12-10 Last updated: 2014-02-27Bibliographically approved

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