Superimposed training-based compressed sensing of sparse multipath channels

In a number of wireless communication applications, the impulse response of multipath communication channels has sparse nature. In this study, physical model for various propagation environments exhibiting sparse channel structure is considered. A superimposed (SI) training-based compressed channel sensing (SI-CCS) technique is proposed for such sparse multipath channels. A non-random periodic pilot sequence is SI over the information sequence at the transmitter, which avoids the use of dedicated time slots for training sequence. At the receiver, first-order statistics and the theory of compressed sensing is applied to estimate the wireless communication channels with sparse impulse response. A simulation analysis is presented to demonstrate the effectiveness of the proposed-channel estimation technique, where mean-square error and bit-error rate are used as the performance measures. Exploiting the proposed SI-CCS technique, the simulation results along with the observations are presented, which illustrate the effect of various channel parameters on the performance of the proposed technique. Furthermore, obtained simulation results for the proposed SI-CCS technique along with its comparison with other techniques in literature are also presented. It is established that for the cases of sparse multipath channels, the proposed SI-CCS technique can potentially achieve significant improvement in the performance of channel estimator over the existing estimation techniques of such sparse channels.