In lattice-coded multiple-input multiple-output (MIMO) systems, optimal decoding amounts to solving the closest vector problem (CVP). embedding is a powerful technique for the approximate CVP, yet its remarkable perfo...
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ISBN:
(纸本)9781457705953
In lattice-coded multiple-input multiple-output (MIMO) systems, optimal decoding amounts to solving the closest vector problem (CVP). embedding is a powerful technique for the approximate CVP, yet its remarkable performance is not well understood. In this paper, we analyze the embedding technique from a bounded distance decoding (BDD) viewpoint. 1/(2 gamma)-BDD is referred to as a decoder that finds the closest vector when the noise norm is smaller than lambda(1)/(2 gamma), where lambda(1) is the minimum distance of the lattice. We prove that the Lenstra, Lenstra and Lovasz (LLL) algorithm can achieve 1/(2 gamma)-BDD for gamma approximate to O(2(n/4)). This substantially improves the existing result gamma = O(2(n)) for embeddingdecoding. We also prove that BDD of the regularized lattice is optimal in terms of the diversity-multiplexing gain tradeoff (DMT).
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