Multiple Delay Estimation Using Genetic Algorithm-Based MCMC in Non-Orthogonal Random Access

被引：5

作者：

Han, Rui ^{[1
]}

Bai, Lin ^{[1
,2
]}

Yi, Jie ^{[3
]}

Liu, Jianwei ^{[1
]}

Choi, Jinho ^{[4
]}

机构：

[1] Beihang Univ, Sch Cyber Sci & Technol, Beijing 100191, Peoples R China

[2] Beihang Univ, Beijing Lab Gen Aviat Technol, Beijing 100191, Peoples R China

[3] Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China

[4] Deakin Univ, Sch Informat Technol, Geelong, Vic 3220, Australia

来源：

IEEE WIRELESS COMMUNICATIONS LETTERS | 2020年 / 9卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Estimation; Complexity theory; Genetic algorithms; Delays; Silicon carbide; Markov processes; Monte Carlo methods; Markov chain Monte Carlo; genetic algorithm; multiple delay estimation; machine-type communications; non-orthogonal random access;

D O I：

10.1109/LWC.2019.2957492

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In machine-type communication (MTC), since multiple active devices are located randomly in a cell, the arrival time of multiple signals can be different. A priori information on the arrival time of multiple signals is helpful to estimate more specific parameters such as the number of signals, which is essential to employ the successive interference cancellation (SIC) to improve throughput of a non-orthogonal random access (NORA) system. In this letter, we propose an approach to estimate the round-trip delay (RTD) of multiple signals based on Markov chain Monte Carlo (MCMC) and Genetic Algorithm (GA). Simulation results show our proposed GA-based MCMC method can achieve high performance with low complexity.

引用

页码：398 / 401

页数：4

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