IEEE 802.11n中速率、模式及信道的联合自适应算法
Joint Adaptation Algorithm of Rate, Mode and Channel for IEEE 802.11n

DOI：10.13328/j.cnki.jos.004583

 作者 单位 E-mail 陈剑 解放军理工大学 指挥信息系统学院, 江苏 南京 210007 李贺武 清华大学 信息网络工程研究中心, 北京 100084 张晓岩 Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede 7500, Netherland南京师范大学 数学科学学院 数学研究所, 江苏 南京 200023 royxyzhang@gmail.com 周俊 解放军理工大学 指挥信息系统学院, 江苏 南京 210007

针对IEEE 802.11n无线网络中的速率、MIMO模式与信道宽度的联合自适应问题,提出了一种基于非静态Multi-Armed Bandit学习方法的联合自适应算法,并设计了一种新颖的报酬函数.为解决该算法收敛时间较慢的问题,基于分类回归树设计了MCS、MIMO模式以及信道宽度预测算法,其能够有效利用无线网卡驱动程序采集的相关统计数据预测不同MCS、MIMO模式以及信道宽度组合的报酬函数,大幅度缩小联合自适应算法的搜索空间.该算法具有易实现、近似最优及计算复杂度低的特点.真实实验结果表明:在无干扰和不同干扰环境下,联合自适应算法都能够有效地提高UDP吞吐量.

To address the issue of joint adaptation of rate, MIMO (multiple input multiple output) mode and channel width in IEEE 802.11n wireless networks, a joint adaptation algorithm based on non-stationary multi-armed bandit learning approach is proposed, and a novel reward function is also presented. To reduce the convergence time of the algorithm mentioned above, the prediction algorithms of MCS (modulation and coding scheme), MIMO mode and channel width based on classification and regression trees are developd to effectively utilize the statistical data collected by the wireless network interface driver to predict the reward values of different combination of MCS, MIMO mode and channel width, and shrink the search space of the joint adaptation algorithm. The proposed algorithm is easy to implement, approximately optimal, and has low computation complexity. The real experiment results show that the UDP throughput is improved significantly by the proposed algorithm under the interference-free environment and the environment with different interference conditions.
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