2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
| Paper ID | E.3.3 | ||
| Paper Title | On the Randomized Babai Point | ||
| Authors | Xiao-Wen Chang, Zhilong Chen, Yingzi Xu, McGill University, Canada | ||
| Session | E.3: Estimation Theory | ||
| Presentation | Lecture | ||
| Track | Detection and Estimation | ||
| Manuscript | Click here to download the manuscript | ||
| Virtual Presentation | Click here to watch in the Virtual Symposium | ||
| Abstract | Estimating the integer parameter vector in a linear model with additive Gaussian noise arises from many applications, including communications. The optimal approach is to solve an integer least squares (ILS) problem, which is unfortunately NP-hard. Recently Klein's randomized algorithm, which finds a sub-optimal solution to the ILS problem, to be referred to as the randomized Babai point, has attracted much attention. This paper presents a formula of the success probability of the randomized Babai point and some interesting properties, and compares it with the deterministic Babai point. | ||
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
