A particle filter and joint likelihood ratio based error source diagnosing method for IEEE 802.11 networks. Issue 4 (6th May 2013)

• Record Type: Journal Article
• Authors: Hao, Wang; Hao, Jiang
• Is Part Of: Wireless communications and mobile computing. Volume 15:Issue 4(2015)
• Page Start: 726
• Page End: 741
• Publication Date: 2013-05-06
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1002/wcm.2378
• Languages: English
• ISSNs: 1530-8669

Abstract

<abstract abstract-type="main" id="wcm2378-abs-0001"> <title>Abstract</title> <p id="wcm2378-para-0003">As a result of the fast growing scale of IEEE 802.11 networks, problems such as low signal‐to‐noise ratio, collision, and small‐scale fading have seriously impacted the performance of IEEE 802.11 networks. In this work, we describe a novel cross‐layer analysis method, using the combination of received channel power sampling at the physical (PHY) layer and information at the medium access control (MAC) layer. The proposed method analyzes the causes of error frames by recording samples of received channel power at the physical layer on a small time scale (5 μs) and employs the particle filter‐based joint likelihood ratio method in order to detect changes in the received channel power and to isolate models of the changes within the time domain. At the same time, it determines the source and the destination addresses of the error frames by decoding packet physical addresses at the MAC layer and then locates the error source. On the basis of the proposed method, optimizations are possible both at the MAC layer and the PHY layer. The simulation and the experimental validation were both carried out for the proposed method. The simulation validation was carried out in order to validate the accuracy of the particle filter‐based joint likelihood ratio method for fault detection and for model isolation using the proposed method. We compared the performance of the extended Kalman filter and the particle filter‐based likelihood ratio method using the non‐Gaussian situation for the proposed method. We then performed several experiments in order to validate the accuracy of the proposed method for error source diagnosis. We also show the applications of the proposed method. The experiments under actual scene showed that different optimizations can be made to optimize the actual wireless local area network by determining the three different causes of the errors. Copyright © 2013 John Wiley & Sons, Ltd.</p> </abstract>