On Compressed Sensing Based Iterative Channel Estimator for UWA OFDM Systems

Abstract

The ever-increasing demand for high-data-rate communication over a wireless multipath fading channel usually necessitates that at the receiver, prior knowledge about the channel is known. This is often achieved using known pilot signals that track the channel and produces at the receiver channel impulse response reconstruction obtained from the received signals. Recently, empirical studies have demonstrated that rich multipath channel assumption is violated in most physical systems and that the channel instead exhibits a sparse multipath behavior that is characterized by only a few dominant paths in propagation. In past decades, there has been a growing interest in the discussion and study of using underwater acoustic channel as the physical layer for communication systems. In this work, Compressed Sensing (CS)-based iterative channel estimators for Underwater Acoustic (UWA) OFDM systems are proposed where channel is assumed to be both sparse and time varying. The estimation of UWA channel is mainly based on Kalman filtered Compressed Sensing (KFCS) algorithms. CS with Kalman filter (KF) provides new idea about channel estimation for UWA OFDM communication systems, whose result outweigh traditional CS-UWA results.