物联网通信中的无线信道抗干扰算法

谈玲; 庄勇

doi:10.11959/j.issn.1000-0801.2017283

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物联网通信中的无线信道抗干扰算法

研究与开发 | 更新时间：2024-06-05

- 物联网通信中的无线信道抗干扰算法
- Anti-interference algorithm of wireless channel for IoT communication
- 电信科学 2017年33卷第10期页码：58-64
- 作者机构：
- 作者简介：
  
  [ "谈玲（1979-），女，博士，南京信息工程大学副教授，主要研究方向为物联网大数据。" ]
  [ "庄勇（1987-），男，南京信息工程大学硕士生，主要研究方向为无线宽带通信。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(41505017);江苏省自然科学基金资助项目;The National Natural Science Foundation of Jiangsu Province of China(BK20160951)
- DOI：10.11959/j.issn.1000-0801.2017283
  中图分类号： TN919
- 网络出版日期：2017-10，
  
  纸质出版日期：2017-10-20
- 稿件说明：
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谈玲, 庄勇. 物联网通信中的无线信道抗干扰算法[J]. 电信科学, 2017,33(10):58-64.

Ling TAN, Yong ZHUANG. Anti-interference algorithm of wireless channel for IoT communication[J]. Telecommunications science, 2017, 33(10): 58-64.
谈玲, 庄勇. 物联网通信中的无线信道抗干扰算法[J]. 电信科学, 2017,33(10):58-64. DOI： 10.11959/j.issn.1000-0801.2017283.

Ling TAN, Yong ZHUANG. Anti-interference algorithm of wireless channel for IoT communication[J]. Telecommunications science, 2017, 33(10): 58-64. DOI： 10.11959/j.issn.1000-0801.2017283.

摘要

在物联网通信中，无线信道的抗干扰性依然受到各种因素影响，多天线系统对信道质量改善有较好的效果，但其信号检测的复杂度和性能还需要进一步提升。基于 QR 分解的检测算法具有较低的计算复杂度，但检测性能有待改善。为了改善QR分解检测算法的性能，提出一种基于ML准则结合判决候选机制的QR分解算法，并对其性能进行了分析。该算法采用 ML 准则对初始层进行精确估计检测，而后对其他检测层进行可靠判决，不可靠则引入候选点并从中选择最优候选点进行反馈。该算法可以显著改善系统干扰，并且在判决回馈中大大减少错误传播。实验仿真表明，在增加一定复杂度的情况下，该算法能够有效改善物联网系统性能。

Abstract

In IoT communication

the anti-interference of wireless channel is influenced by various factors.Multi-antenna system has significant effect for promoting anti-interference of channel

whose complexity and performance in signal detection need further improvement.The QR decomposition detection algorithm in signal detection of multi-antenna system has low computational complexity

but the algorithm performance is poor.In order to improve the performance of QR decomposition detection algorithm

a QR decomposition algorithm based on ML criterion and decision candidate mechanism was proposed

and the performance of the algorithm was analyzed.The ML criterion was used to estimate the initial level of the detection

and a reliable decision was adopted in the other detection layer.Candidate points were introduced in unreliable case and the optimal candidate were selected from the feedback.The algorithm could significantly improve the system interference

and propagation error were greatly reduced in the decision feedback.The experimental results show that the proposed algorithm can improve the performance of the IoT system effectively with certain complexity.

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references

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