Data-Driven Channel Modeling Using Spectrum Measurement

Dynamic spectrum access has been a subject of extensive study in recent years. The increasing volume of literature calls for better understanding of the characteristics of current spectrum utilization as well as better tools for analysis. A number of measurement studies have been conducted recently, revealing previously unknown features. On the other hand, analytical studies largely continues to rely on standard models like the two-state Markov (Gilbert-Elliot) model. In this paper, we present an alternative, stochastic differential equation (SDE) based spectrum utilization model that captures dynamic changes in channel conditions induced by primary users' activities. The SDE model is in closed form, can generate spectrum dynamics as a temporal process, and is shown to provides very good fit for real spectrum measurement data. We show how synthetic spectrum data can be generated in a straightforward manner using this model to enable realistic simulation studies. Moreover, we show that the SDE model can be viewed as a more general modeling framework (continuous in time and continuous in value) than commonly used discrete Markovian models: it is defined by only a few parameters but can be used to obtain the transition matrix of any N-state Markov model. This is verified by comparing the two-state GE model generated by the SDE model and that trained directly from the data. We show that the GE model is a good fit for the (quantized) data, thereby a fine choice when binary descriptions of the channel condition is sufficient. However, when highly resolution (in channel condition) is needed, the SDE model is much more accurate than an N-state model, and is much easier to train and store.