WSN assisted modulation detection with maximum likelihood approach, suitable for non-identical Rayleigh channels

Adaptive modulation plays an important role inoptimum spectrum utilization. In adaptive modulation, the transmitter dynamically changes the modulation scheme based on the channel state information CSI). The receiver must know the type of modulation used in order to demodulate the received signal. In this paper, the likelihood-based modulation detection using power efficient single hop wireless sensor network (WSN) is presented. The local estimation of the modulation scheme used and channel state information is performed at each sensor. Eachsensor is assumed to experience non-identical Rayleigh fading channel. Each sensor performs two operations, its local maximum-likelihood (ML) based channel estimation and the evaluation of the local likelihood functions of the received noisy signal under all possible modulation hypothesis, adaptively used at the transmitter. These local likelihood functions from all the sensors are fed into the master node for global classification. The power-efficient single-hop fusion technique is employed to fuse the local data into the master node. The performance of the proposed approach is evaluated using the probability of correct classification, Pcc, and the time complexity.