An Online and Distributed Approach for Modulation Classification Using Wireless Sensor Networks

Based on an online and distributed implementation of the expectation-maximization (EM) algorithm, a hybrid likelihood-based modulation classifier is proposed for a sensor network subject to unknown nonidentical flat fading. The proposed algorithm compares favorably in terms of computational complexity with respect to other maximum likelihood classifiers that rely on the batch (offline) EM algorithm for parameter estimation. Upon the reception of a new sample, each sensor computes the a posteriori probability of the corresponding symbol based on the average consensus algorithm, which relies on local communications among nearby sensors and promotes scalability and power efficiency. Then, each sensor updates its statistics with the innovation obtained from the samples received during the last symbol interval and new estimates for local sensor parameters are computed. Simulation results demonstrate that the proposed online and distributed EM-based classifier can achieve performance close that of a clairvoyant classifier equipped with perfect channel state information.