Technical Program

Paper Detail

Paper Title Slice Sampling for Lattice Gaussian Distribution
Paper IdentifierFR1.R7.3
Authors Zheng Wang, Nanjing University of Aeronautics and Astronautics, China; Cong Ling, Imperial College London, United Kingdom
Session Post-Quantum Cryptography II
Location Bièvre, Level 5
Session Time Friday, 12 July, 09:50 - 11:10
Presentation Time Friday, 12 July, 10:30 - 10:50
Manuscript  Click here to download the manuscript
Abstract Sampling from the lattice Gaussian distribution has emerged as a key problem in coding and cryptography. In this paper, the slice sampling from Markov chain Monte Carlo (MCMC) is adopted to lattice Gaussian sampling. Firstly, we demonstrate that the Markov chain arising from the proposed slice sampling is uniformly ergodic, namely, it converges exponentially fast to the stationary distribution. Secondly, the convergence rate of the underlying Markov chain is investigated, and we show the proposed slice sampling algorithm entails a better convergence performance than the independent Metropolis-Hastings-Klein (IMHK) sampling algorithm. Finally, simulation results based on MIMO detection are presented to confirm the performance gain by convergence enhancement.