Joint Channel Estimation and Nonlinear Distortion Recovery Based on Compressed Sensing for OFDM Systems

摘要

In order to solve the problems of high PAPR and channel estimation in OFDM systems, a new algorithm of joint channel estimation and Nonlinear Distortion (NLD) recovery based on compressed sensing is proposed for nonlinearly distorted OFDM systems, using the dual-sparsity of channel and NLD. In quasi-static channel, the channel is estimated by adopting Golay complementary sequences to against NLD, and the NLD is estimated by using compressed sensing based on pectinate pilots. In time-varying channel, a scheme of pilot grouping and cascaded clipping is proposed. The pilots are divided into two groups. The first group, which is protected from NLD influence, is adopted to estimate the channel by compressed sensing, and the second group is used to estimate the NLD by compressed sensing as well, based on the estimated channel information. Simulation results show a good performance of the proposed algorithm without any priori information. And also advantages are brought for the system without any PAPR reduction algorithms or iterative operations.