DMT Optimality of LR-Aided Linear Decoders for a General Class of Channels, Lattice Designs, and System Models - Computer Science > Information TheoryReportar como inadecuado




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Abstract: The work identifies the first general, explicit, and non-random MIMOencoder-decoder structures that guarantee optimality with respect to thediversity-multiplexing tradeoff DMT, without employing a computationallyexpensive maximum-likelihood ML receiver. Specifically, the work establishesthe DMT optimality of a class of regularized lattice decoders, and moreimportantly the DMT optimality of their lattice-reduction LR-aided linearcounterparts. The results hold for all channel statistics, for all channeldimensions, and most interestingly, irrespective of the particular lattice-codeapplied. As a special case, it is established that the LLL-based LR-aidedlinear implementation of the MMSE-GDFE lattice decoder facilitates DMT optimaldecoding of any lattice code at a worst-case complexity that grows at mostlinearly in the data rate. This represents a fundamental reduction in thedecoding complexity when compared to ML decoding whose complexity is generallyexponential in rate.The results- generality lends them applicable to a plethora of pertinentcommunication scenarios such as quasi-static MIMO, MIMO-OFDM, ISI,cooperative-relaying, and MIMO-ARQ channels, in all of which the DMT optimalityof the LR-aided linear decoder is guaranteed. The adopted approach yieldsinsight, and motivates further study, into joint transceiver designs with animproved SNR gap to ML decoding.



Autor: Joakim Jalden, Petros Elia

Fuente: https://arxiv.org/







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