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Sliced Lattice Gaussian Sampling: Convergence Improvement and Decoding Optimization
- Source :
- IEEE Transactions on Communications. 69:2599-2612
- Publication Year :
- 2021
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2021.
-
Abstract
- Sampling from the lattice Gaussian distribution has emerged as a key problem in coding and decoding while Markov chain Monte Carlo (MCMC) methods from statistics offer an effective way to solve it. In this paper, the sliced lattice Gaussian sampling algorithm is proposed to further improve the convergence performance of the Markov chain targeting at lattice Gaussian sampling. We demonstrate that the Markov chain arising from it is uniformly ergodic, namely, it converges exponentially fast to the stationary distribution. Meanwhile, the convergence rate of the underlying Markov chain is also investigated, and we show the proposed sliced sampling algorithm entails a better convergence performance than the independent Metropolis-Hastings-Klein (IMHK) sampling algorithm. On the other hand, the decoding performance based on the proposed sampling algorithm is analyzed, where the optimization with respect to the standard deviation $\sigma >0$ of the target lattice Gaussian distribution is given. After that, a judicious mechanism based on distance judgement and dynamic updating for choosing $\sigma $ is proposed for a better decoding performance. Finally, simulation results based on multiple-input multiple-output (MIMO) detection are presented to confirm the performance gain by the convergence enhancement and the parameter optimization.
- Subjects :
- Technology
decoding
Computer science
Gaussian
CHAIN MONTE-CARLO
Markov process
02 engineering and technology
Standard deviation
CAPACITY
MIMO detection
symbols.namesake
Engineering
0203 mechanical engineering
SYSTEMS
SEARCH
1005 Communications Technologies
0202 electrical engineering, electronic engineering, information engineering
ALGORITHM
Electrical and Electronic Engineering
CODES
Science & Technology
lattice Gaussian sampling
COMPLEXITY
CHANNELS
Stationary distribution
Markov chain
Coding
0804 Data Format
Engineering, Electrical & Electronic
020302 automobile design & engineering
020206 networking & telecommunications
Markov chain Monte Carlo
MCMC methods
REDUCTION
0906 Electrical and Electronic Engineering
Rate of convergence
Telecommunications
symbols
Algorithm
Decoding methods
Subjects
Details
- ISSN :
- 15580857 and 00906778
- Volume :
- 69
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Communications
- Accession number :
- edsair.doi.dedup.....455dd8a6318abc04f22e14bed5e194cd