Construction of Time-Space Radio Environment Database using HMM for Cooperative Sensing

Abstract

In recent years, many researchers focus on a measurement-based radio environment database (RED) that utilizes the actual received signal power obtained by spectrum sensing as an enabler for an efficient frequency sharing. In this paper, in an environment where multiple transmitters are switched ON/OFF, each sensor is distributed in the environment by cooperative sensing and acquires time series data. Moreover, each sensor learns using the Hidden Markov Model (HMM) on the acquired time series data, thereby estimating the parameter of the receivable transmitter. After collecting estimation results by HMM in each sensor, we propose to use sensor selection for estimation of time parameters (channel occupancy rate, average ON/OFF interval) and multiple imputation for estimation of spatial parameters (pathloss exponent, transmit power). By the proposed method, it is possible to construct a highly accurate time–space RED. The simulation results confirm the effectiveness of the proposed method.