Characterizing Temporal SNR Variation in 802.11 Networks

The analysis and design of wireless medium access control (MAC) protocols, coding schemes, and transmission algorithms can significantly benefit from an understanding of the channel quality variation. We attempt to represent channel quality variation using a finite-state birth-death Markov model. We outline a method to compute the parameters of the model based on measured traces obtained using common wireless chipsets. Using this Markov chain, we statistically evaluate the performance based on the channel quality, long-term correlations, and burst length distributions. Such a model significantly performs better than a traditional two-state Markov chain in characterizing 802.11 networks while maintaining the simplicity of a birth-death model. We interpret the variation of the model parameters across different locations and different times. A finite-state stationary model is amenable to analysis and can substantially benefit the design of efficient algorithms and make simulations for wireless network protocols faster.

[1]  Hong Shen Wang,et al.  Finite-state Markov channel-a useful model for radio communication channels , 1995 .

[2]  E. O. Elliott.  Estimates of error rates for codes on burst-noise channels , 1963 .

[3]  Paramvir Bahl,et al.  Characterizing user behavior and network performance in a public wireless LAN , 2002, SIGMETRICS '02.

[4]  E. Gilbert.  Capacity of a burst-noise channel , 1960 .

[5]  P. Krishnamurthy,et al.  Markov modeling of 802.11 channels , 2003, 2003 IEEE 58th Vehicular Technology Conference. VTC 2003-Fall (IEEE Cat. No.03CH37484).

[6]  Wang-Chien Lee,et al.  Exploring spatial correlation for link quality estimation in wireless sensor networks , 2006, Fourth Annual IEEE International Conference on Pervasive Computing and Communications (PERCOM'06).

[7]  Randy H. Katz,et al.  A trace-based approach for modeling wireless channel behavior , 1996, Winter Simulation Conference.

[8]  Andreas Willig,et al.  Measurements of a wireless link in an industrial environment using an IEEE 802.11-compliant physical layer , 2002, IEEE Trans. Ind. Electron..

[9]  Tristan Henderson,et al.  The changing usage of a mature campus-wide wireless network , 2004, MobiCom '04.

[10]  Brahim Bensaou,et al.  Performance analysis of IEEE 802.11e contention-based channel access , 2004, IEEE Journal on Selected Areas in Communications.

[11]  Sebastian Thrun,et al.  A Learning Algorithm for Localizing People Based on Wireless Signal Strength that Uses Labeled and Unlabeled Data , 2003, IJCAI.

[12]  Saleem A. Kassam,et al.  Finite-state Markov model for Rayleigh fading channels , 1999, IEEE Trans. Commun..

[13]  Hayder Radha,et al.  Markov-based modeling of wireless local area networks , 2003, MSWIM '03.

[14]  Andreas Willig,et al.  A new class of packet- and bit-level models for wireless channels , 2002, The 13th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications.

[15]  Solomon Kullback,et al.  Information Theory and Statistics , 1960 .