Simple, accurate and computationally efficient wireless channel modeling algorithm Dmitri Moltchanov, Yevgeni Koucheryavy, Jarmo Harju Institute of Communication Engineering, Tampere University of Technology, P.O.Box 553, Tampere, Finland {moltchan,yk,harju}@cs.tut.fi Abstract. We propose simple and computationally efficient wireless channel modeling algorithm. For this purpose we adopt the special case of the algorithm initially proposed in [1] and show that its complexity significantly decreases when the time-series is covariance stationary bi- nary in nature. We show that for such time-series the solution of the inverse eigenvalue problem returns unique transition probability matrix of the modulating Markov chain that is capable to match statistical properties of empirical frame error processes. Our model explicitly takes into account autocorrelational and distributional properties of empiri- cal data. We validate our model against empirical frame error traces of IEEE 802.11b wireless access technology operating in DCF mode over spread spectrum at 2Mbps and 5.5 Mbps bit rates. We also made avail- able the C code of the model as well as pre-compiled binaries for Linux and Windows operating systems at http://www.cs.tut.fi/˜moltchan. 1 Introduction The grow of the Internet and increase in the number of users that wish to access Internet services ’anytime and anywhere’ stimulate development of wireless ac- cess technologies. Consequently, air interface is expected to be an integral part of next-generation telecommunications networks. Due to movement of a mobile user, the propagation path between the trans- mitter and a receiver may vary from simple line-of-sight (LOS) to very complex ones. To estimate performance of wireless channels, propagation models are often used. Basically, we distinguish between the large-scale and small-scale propaga- tion models (see [2] for review). The former models focus on predicting the received local average signal strength over large separation distances between the transmitter and a receiver and do not take into account rapid changes of the received signal strength. As a result, they cannot be effectively used in perfor- mance evaluation studies. Propagation models characterizing rapid fluctuations of the received signal strength over short time duration are called small-scale propagation models. Due to implicit incorporation of small-scale mobility, these models provide better characterization of the received signal strength.