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    张招亮, 陈海明, 黄庭培, 崔 莉. 无线网络的差异化比特错误率估计方法[J]. 计算机研究与发展, 2014, 51(1): 138-150.
    引用本文: 张招亮, 陈海明, 黄庭培, 崔 莉. 无线网络的差异化比特错误率估计方法[J]. 计算机研究与发展, 2014, 51(1): 138-150.
    Zhang Zhaoliang, Chen Haiming, Huang Tingpei, Cui Li. Differentiated Bit Error Rate Estimation for Wireless Networks[J]. Journal of Computer Research and Development, 2014, 51(1): 138-150.
    Citation: Zhang Zhaoliang, Chen Haiming, Huang Tingpei, Cui Li. Differentiated Bit Error Rate Estimation for Wireless Networks[J]. Journal of Computer Research and Development, 2014, 51(1): 138-150.

    无线网络的差异化比特错误率估计方法

    Differentiated Bit Error Rate Estimation for Wireless Networks

    • 摘要: 在无线网络中,比特错误率(bit error rate, BER)的估计是许多上层协议的基础,对数据传输的性能具有重要的影响,目前已成为一个重要的研究课题.但是现有BER估计编码未考虑实际网络的BER分布特征,估计误差较大.在实测分析802.11无线网络的BER分布特征的基础上,提出了一种采用差异化思想来提高BER估计准确度的方法差异化估错码(differentiated error estimation, DEE),其主要思想是在数据包中插入具有不同估错能力的多级估错位,并随机均匀地分布各估错位.然后,借助BER与奇偶校验错误概率的理论关系来估计BER.此外,DEE利用BER非均匀分布特征来优化各级估错位的能力,提高出现概率较高的BER的估计准确度,以降低平均估计误差.在7个节点组成的测试床上评价了DEE的性能.实验结果表明,与最近的研究成果估错码(error estimation code, EEC)相比,DEE可将估计误差平均减少约44%.当估错冗余较低时DEE可将估计误差减少约68%.此外,DEE具有比EEC更小的估计偏差.

       

      Abstract: In wireless networks, bit error rate (BER) estimation provides a critical foundation for many upper-layer protocols, greatly influencing the performance of data communications. Thus, it has become a significant research topic currently. However, the existing error estimating codes ignore the BER distribution in real networks. Hence, the estimation accuracy of these codes is low. Based on the analysis of BER distribution in practical 802.11 wireless networks, we propose to exploit the principle of differentiation to improve the accuracy of BER estimation. The main idea of our design, called DEE (differentiated error estimation), is to insert into each data packet multiple levels of error estimating codes which have different estimation capability. All these error estimating codes are randomly and evenly distributed in each packet. Then, BER is derived by the theoretical relationship between BER and the parity check failure probability. Furthermore, DEE utilizes the characteristics of uneven distribution of BER to optimize the estimation capability of each level of codes. It improves the estimation accuracy of BER which occurs more frequently, thus reducing the average estimation error. We implement and evaluate DEE over a 7-node testbed. The experimental result shows that DEE reduces the estimation error by about 44% on average compared with the recent scheme EEC (error estimating code). When the estimation redundancy is low, DEE can decrease the estimation error by about 68%. Furthermore, DEE has smaller estimation bias than EEC.

       

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