An independent but not identically distributed bit error model for heavy-tailed wireless channels

被引:0
|
作者
Jia LU [1 ]
Wei YANG [2 ]
Jun-hui WANG [1 ]
Bao-liang LI [1 ]
Wen-hua DOU [1 ]
机构
[1] School of Computer,National University of Defense Technology
[2] Navy Academy of Armament
关键词
Trace; Heavy-tailed; Independent but not identically distributed(inid); Bit error model; Bursty;
D O I
暂无
中图分类号
TN911.5 [信道均衡];
学科分类号
081002 ;
摘要
The error patterns of a wireless channel can be represented by a binary sequence of ones(burst) and zeros(run),which is referred to as a trace.Recent surveys have shown that the run length distribution of a wireless channel is an intrinsically heavy-tailed distribution.Analytical models to characterize such features have to deal with the trade-off between complexity and accuracy.In this paper,we use an independent but not identically distributed(inid) stochastic process to characterize such channel behavior and show how to parameterize the inid bit error model on the basis of a trace.The proposed model has merely two parameters both having intuitive meanings and can be easily figured out from a trace.Compared with chaotic maps,the inid bit error model is simple for practical use but can still be deprived from heavy-tailed distribution in theory.Simulation results demonstrate that the inid model can match the trace,but with fewer parameters.We then propose an improvement on the inid model to capture the ’bursty’ nature of channel errors,described by burst length distribution.Our theoretical analysis is supported by an experimental evaluation.
引用
收藏
页码:42 / 49
页数:8
相关论文
共 50 条
  • [31] Heavy-tailed distributions in a stochastic gene autoregulation model
    Bokes, Pavol
    JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT, 2021, 2021 (11):
  • [32] On estimation of measurement error models with replication under heavy-tailed distributions
    Lin, Jin-Guan
    Cao, Chun-Zheng
    COMPUTATIONAL STATISTICS, 2013, 28 (02) : 809 - 829
  • [33] On a stochastic process with a heavy-tailed distributed component describing inventory model type of (s, S)
    Aliyev, Rovshan
    COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 2017, 46 (05) : 2571 - 2579
  • [34] On estimation of measurement error models with replication under heavy-tailed distributions
    Jin-Guan Lin
    Chun-Zheng Cao
    Computational Statistics, 2013, 28 : 809 - 829
  • [35] Stochastic daily precipitation model with a heavy-tailed component
    Neykov, N. M.
    Neytchev, P. N.
    Zucchini, W.
    NATURAL HAZARDS AND EARTH SYSTEM SCIENCES, 2014, 14 (09) : 2321 - 2335
  • [36] The Heavy-Tailed Gleser Model: Properties, Estimation, and Applications
    Olmos, Neveka M.
    Gomez-Deniz, Emilio
    Venegas, Osvaldo
    MATHEMATICS, 2022, 10 (23)
  • [37] On a heavy-tailed distribution and the stability of an equilibrium in a distributed delay symmetric network
    Ncube, Israel
    CHAOS SOLITONS & FRACTALS, 2021, 152
  • [38] A distributed quantile estimation algorithm of heavy-tailed distribution with massive datasets
    Xie, Xiaoyue
    Shi, Jian
    MATHEMATICAL BIOSCIENCES AND ENGINEERING, 2020, 18 (01) : 214 - 230
  • [39] Distributed Stochastic Strongly Convex Optimization under Heavy-Tailed Noises
    Sun, Chao
    Chen, Bo
    2024 IEEE INTERNATIONAL CONFERENCE ON CYBERNETICS AND INTELLIGENT SYSTEMS, CIS AND IEEE INTERNATIONAL CONFERENCE ON ROBOTICS, AUTOMATION AND MECHATRONICS, RAM, CIS-RAM 2024, 2024, : 150 - 155
  • [40] A distributed consensus filter for sensor networks with heavy-tailed measurement noise
    Dong, Peng
    Jing, Zhongliang
    Shen, Kai
    Li, Minzhe
    SCIENCE CHINA-INFORMATION SCIENCES, 2018, 61 (11)