Abstract: Channel errors and collisions are two major factors that cause packet loss and errors in wireless networks. The reason for effectively identifying packet loss and errors is the basis for implementing the high performance IEEE 802.11n protocol. This paper focuses on how to improve the accuracy of discriminating the causes of packet losses and errors with low overhead. Based on the supervised learning theory, this paper proposes a light-weighted discriminator, named MPLEC, to differentiate the root causes of packet losses and errors with high accuracy and timeliness. MPLEC analyzes the packet reception situation through a large number of field scene statistical experiments, extracts the feature vector composed of RSSI and CSI as the input of the supervised learning model, and performs offline training and inspection on the multi-class MPLEC classification model through supervised learning method. The result indicates that the accuracy rate of MPLEC is as high as 87%. Finally, this paper applies the MPLEC to the CSMA/CA protocol to evaluate its performance. The experimental results show that compared with the original backoff algorithm, this method can increase the probability of successful retransmission by 25% and increase the time utilization by 7.8%.

Key words: wireless local area network, collisions, channel fading, supervised learning