無線網路中以機器學習方法為基礎的新穎速率適應演算法

Abstract

In recent years, IEEE 802.11 wireless networks have become the most popular wireless technology. IEEE 802.11 supports multiple transmission rates. How to determine the appropriate transmission rate is challenging. In this paper, we propose a novel rate adaptation algorithm to tackle this problem. We utilize the maximum likelihood estimator to robustly predict the transmission statistics for each transmission rate. Then we exploit the cross-layer correlation between PHY and MAC to determine the transmission cost for each transmission rate. The goal of our design is to achieve the maximum spectral efficiency. Based on extensive simulation experiments, the proposed algorithm outperforms existing well-known algorithms. Wireless mesh networks (WMNs) have experienced an enormous growth over the past few years. The performance of WMNs depends on the joint effect of both routing algorithms and rate adaptive algorithms. The performance of various routing algorithms has been studied extensively in the literature.However, little work has been done to evaluate the cross-layer impact of rate adaptive algorithms inWMN environments. In this paper, we compare the performance of several rate adaptive algorithms to exploit the multi-hop performance in WMN environments. In addition, a novel rate adaptive algorithm is proposed via the machine learning approach to robustly reflect the channel information. The goal of our design is to maximize the spectral efficiency. Through extensive computer simulations under different channel and topology environments, experimental results demonstrate the proposed algorithm outperforms other existing algorithms in WMN environments.