Radio environment map construction using Hidden Markov Model in multiple primary user environment

In this paper, we discuss a method to construct a radio environment map (REM) in an environment with multiple primary users (PUs). The REM provides statistical information about the PU activity at each location. It enables the secondary user to access the licensed band dynamically. We derive the measurement architecture based on the “crowd-sourcing” scheme to gather large-scale measurement data with inexpensive sensor nodes. The PU detection or identification scheme is key part of the REM construction then there are multiple PUs in the environment. However, it is difficult to identify multiple PUs in an individual user terminal. Therefore, the PU detection or identification problem is solved at the REM servers using the Hidden Markov Model (HMM), which is a time-series-based machine learning technique. The proposed HMM method classifies the measurement data depending on the combined state of each transmitter, which can be either active or idle. The results show that the proposed method exhibits better performance than the existing unsupervised clustering method.