基于模糊单类支持向量机的丢包区分算法

针对现有丢包区分算法难以获取先验知识和不具有推广性的问题,通过对模糊单类支持向量机的改进提出一种新的丢包区分算法。该算法根据无线误码丢包与按序到达包的时延特征分布一致的特点,由按序到达包的时延特征构成训练集,从而将区分误码丢包和拥塞丢包的二分类问题转化为判断丢包是否为误码丢包的单分类问题。由于无需采集两类丢包样本,解决了难以获取先验知识的问题,使新算法能实现在线的模型训练和丢包区分,具有很好的推广能力。仿真结果显示,新算法区分效果良好,提高了无线网络的传输效率。

Loss Differentiation Algorithm Based on Fuzzy One-class Support Vector Machine

The use of the existing Loss Differentiation Algorithms (LDA) cannot get prior knowledge from actual networks, so the algorithms can hardly be generalized to various environments. To solve this problem, a new LDA is proposed by enhancing the Fuzzy One-class Support Vector Machine (FOCSVM). The new LDA converts the differentiation packet loss between congestion and bit error on wireless links into judging that whether the loss is because of bit error. The training set consists of only the in-sequence packets received by the receiver, so the classification model can be got and updated during the session, without the prior knowledge about the two types of loss, which makes the LDA have fine generalization. Simulation results show that the proposed LDA has good differentiation accuracy and can enhance the utilization of resources in wireless networks.