Compressive Sensing Based Channel Estimation for OFDM Systems Under Long Delay Channels

Time-domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) has advantages in spectral efficiency and synchronization. However, its iterative interference cancellation algorithm will suffer from performance loss especially under severely fading channels with long delays and has difficulty supporting high-order modulations like 256 QAM, which may not accommodate the emerging ultra-high definition television service. To solve this problem, a channel estimation method for OFDM under the framework of compressive sensing (CS) is proposed in this paper. Firstly, by exploiting the signal structure of recently proposed time-frequency training OFDM scheme, the auxiliary channel information is obtained. Secondly, we propose the auxiliary information based subspace pursuit (A-SP) algorithm to utilize a very small amount of frequency-domain pilots embedded in the OFDM block for the exact channel estimation. Moreover, the obtained auxiliary channel information is adopted to reduce the complexity of the classical SP algorithm. Simulation results demonstrate that the CS-based OFDM outperforms the conventional dual pseudo noise padded OFDM and CS-based TDS-OFDM schemes in both static and mobile environments, especially when the channel length is close to or even larger than the guard interval length, where the conventional schemes fail to work completely.