other · eScholarship (California Digital Library) · 2023
Monte Carlo Markov Chain Methods for Detection and Decoding
Huang, Jiun-Ting
Cited by 0Open access
Abstract
Optimal detection is one of the most fundamental problems in communication and statistical signal processing. Nevertheless, in many applications, the optimal detection problem often reduces to an NP-complete, or even NP-hard, discrete optimization problem. This research project aims to develop randomized algorithms using Monte Carlo Markov chain methods to achieve near-optimal decision-making. In particular, this project focuses on two specific applications---signal detection in multiple-in-multiple-out systems and decoding of error correction codes. For the scenarios considered, we successfully proposed fast-converging Monte Carlo Markov chain algorithms that achieve near-optimality in reasonable iterations and outperform the previously best-known methods. These results open a new approach to optimal detection.
Cite this paper
Huang, J.-T. (2023). Monte Carlo Markov Chain Methods for Detection and Decoding. eScholarship (California Digital Library).
Huang, Jiun-Ting. “Monte Carlo Markov Chain Methods for Detection and Decoding.” eScholarship (California Digital Library), 2023.
Huang, Jiun-Ting. 2023. “Monte Carlo Markov Chain Methods for Detection and Decoding.” eScholarship (California Digital Library).
Huang, J.-T. (2023) ‘Monte Carlo Markov Chain Methods for Detection and Decoding’, eScholarship (California Digital Library).
@misc{huang2023monte,
title = {{Monte Carlo Markov Chain Methods for Detection and Decoding}},
author = {Huang, Jiun-Ting},
journal = {eScholarship (California Digital Library)},
year = {2023}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.