Decision-Aided Extended Kalman Filter Based Adaptive Timing Recovery for Wavelet Packet Modulated Signals

Abstract

One of the major issues with multi-carrier systems is their vulnerability to timing synchronization errors resulting in the loss of time synchronization which causes loss of orientation of incoming data at the receiver. This paper presents an acquisition algorithm to timing recovery using the decision-aided extended Kalman filtering (EKF) technique for nonlinear disturbance channels in a wavelet packet transform-based multicarrier modulation communication system. This timing recovery algorithm gives faster convergence, smaller root mean square (RMS) errors, and better bit error rate (BER) performance than traditional timing recovery methods, such as the phase-locked loop (PLL), maximum likelihood (ML), and Kalman filter (KF) methods. Thus, the algorithm is able to handle larger timing errors more reliably and to provide better timing recovery, since the scheme takes into account the nonlinear relationship between the signal samples and timing errors. Simulations for various time-varying channels show that the timing recovery algorithm works well for wavelet packet transform-based multicarrier modulation communication systems.