8,145 research outputs found
A Modified KZ Reduction Algorithm
The Korkine-Zolotareff (KZ) reduction has been used in communications and
cryptography. In this paper, we modify a very recent KZ reduction algorithm
proposed by Zhang et al., resulting in a new algorithm, which can be much
faster and more numerically reliable, especially when the basis matrix is ill
conditioned.Comment: has been accepted by IEEE ISIT 201
A Linearithmic Time Algorithm for a Shortest Vector Problem in Compute-and-Forward Design
We propose an algorithm with expected complexity of \bigO(n\log n)
arithmetic operations to solve a special shortest vector problem arising in
computer-and-forward design, where is the dimension of the channel vector.
This algorithm is more efficient than the best known algorithms with proved
complexity.Comment: It has been submitted to ISIT 201
On the Success Probability of the Box-Constrained Rounding and Babai Detectors
In communications, one frequently needs to detect a parameter vector \hbx
in a box from a linear model. The box-constrained rounding detector \x^\sBR
and Babai detector \x^\sBB are often used to detect \hbx due to their high
probability of correct detection, which is referred to as success probability,
and their high efficiency of implimentation. It is generally believed that the
success probability P^\sBR of \x^\sBR is not larger than the success
probability P^\sBB of \x^\sBB. In this paper, we first present formulas for
P^\sBR and P^\sBB for two different situations: \hbx is deterministic and
\hbx is uniformly distributed over the constraint box. Then, we give a simple
example to show that P^\sBR may be strictly larger than P^\sBB if \hbx is
deterministic, while we rigorously show that P^\sBR\leq P^\sBB always holds
if \hbx is uniformly distributed over the constraint box.Comment: to appear in ISIT 201
Distributed Cooperative Localization in Wireless Sensor Networks without NLOS Identification
In this paper, a 2-stage robust distributed algorithm is proposed for
cooperative sensor network localization using time of arrival (TOA) data
without identification of non-line of sight (NLOS) links. In the first stage,
to overcome the effect of outliers, a convex relaxation of the Huber loss
function is applied so that by using iterative optimization techniques, good
estimates of the true sensor locations can be obtained. In the second stage,
the original (non-relaxed) Huber cost function is further optimized to obtain
refined location estimates based on those obtained in the first stage. In both
stages, a simple gradient descent technique is used to carry out the
optimization. Through simulations and real data analysis, it is shown that the
proposed convex relaxation generally achieves a lower root mean squared error
(RMSE) compared to other convex relaxation techniques in the literature. Also
by doing the second stage, the position estimates are improved and we can
achieve an RMSE close to that of the other distributed algorithms which know
\textit{a priori} which links are in NLOS.Comment: Accepted in WPNC 201
On the sensitivity of the SR decomposition
AbstractFirst-order componentwise and normwise perturbation bounds for the SR decomposition are presented. The new normwise bounds are at least as good as previously known results. In particular, for the R factor, the normwise bound can be significantly tighter than the previous result
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