By Topic

Decoding by Embedding: Correct Decoding Radius and DMT Optimality

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Luzzi, L. ; Dept. of Electr. & Electron. Eng., Imperial Coll. London, London, UK ; Stehle, D. ; Cong Ling

The closest vector problem (CVP) and shortest (nonzero) vector problem (SVP) are the core algorithmic problems on Euclidean lattices. They are central to the applications of lattices in many problems of communications and cryptography. Kannan's embedding technique is a powerful technique for solving the approximate CVP; yet, its remarkable practical performance is not well understood. In this paper, the embedding technique is analyzed from a bounded distance decoding (BDD) viewpoint. We present two complementary analyses of the embedding technique: we establish a reduction from BDD to Hermite SVP (via unique SVP), which can be used along with any Hermite SVP solver (including, among others, the Lenstra, Lenstra and Lovász (LLL) algorithm), and show that, in the special case of LLL, it performs at least as well as Babai's nearest plane algorithm (LLL-aided successive interference cancellation). The former analysis helps us to explain the folklore practical observation that unique SVP is easier than standard approximate SVP. It is proven that when the LLL algorithm is employed, the embedding technique can solve the CVP provided that the noise norm is smaller than a decoding radius λ1/(2γ) , where λ1 is the minimum distance of the lattice, and γ ≈ O(2n/4). This substantially improves the previously best known correct decoding bound γ ≈ O(2n) . Focusing on the applications of BDD to decoding of multiple-input multiple-output systems, we also prove that BDD of the regularized lattice is optimal in terms of the diversity-multiplexing gain tradeoff, and propose practical variants of embedding decoding which require no knowledge of the minimum distance of the lattice and/or further improve the error performance.

Published in:

Information Theory, IEEE Transactions on  (Volume:59 ,  Issue: 5 )