3 research outputs found
A Robust Cooperative Modulation Classification Scheme with Intra sensor Fusion for the Time correlated Flat Fading Channels
Networks with distributed sensors, e.g. cognitive radio networks or wireless sensor networks enable large-scale deployments of cooperative automatic modulation classification (AMC). Existing cooperative AMC schemes with centralised fusion offer considerable performance increase in comparison to single sensor reception. Previous studies were generally focused on AMC scenarios in which multipath channel is assumed to be static during a signal reception. However, in practical mobile environments, time-correlated multipath channels occur, which induce large negative influence on the existing cooperative AMC solutions. In this paper, we propose two novel cooperative AMC schemes with the additional intra-sensor fusion, and show that these offer significant performance improvements over the existing ones under given conditions
Automatic modulation classification using interacting multiple model - Kalman filter for channel estimation
YesA rigorous model for automatic modulation
classification (AMC) in cognitive radio (CR) systems is proposed
in this paper. This is achieved by exploiting the Kalman filter
(KF) integrated with an adaptive interacting multiple model
(IMM) for resilient estimation of the channel state information
(CSI). A novel approach is proposed, in adding up the squareroot singular values (SRSV) of the decomposed channel using the
singular value decompositions (SVD) algorithm. This new
scheme, termed Frobenius eigenmode transmission (FET), is
chiefly intended to maintain the total power of all individual
effective eigenmodes, as opposed to keeping only the dominant
one. The analysis is applied over multiple-input multiple-output
(MIMO) antennas in combination with a Rayleigh fading channel
using a quasi likelihood ratio test (QLRT) algorithm for AMC.
The expectation-maximization (EM) is employed for recursive
computation of the underlying estimation and classification
algorithms. Novel simulations demonstrate the advantages of the
combined IMM-KF structure when compared to the perfectly
known channel and maximum likelihood estimate (MLE), in
terms of achieving the targeted optimal performance with the
desirable benefit of less computational complexity loads