Accurate channel estimation based on Bayesian Compressive Sensing for next-generation wireless broadcasting systems

Digital television terrestrial broadcasting (DTTB) industry has achieved rapid development in recent years. It is now facing new development opportunities for next-generation wireless broadcasting systems. As one of the DTTB standards, digital terrestrial multimedia/television broadcasting (DTMB) uses the time domain synchronous OFDM (TDS-OFDM) as its basic core technology. TDS-OFDM has higher spectral efficiency and faster synchronization but it cannot support high-order modulation and high-definition television (HDTV) delivery in fast fading channels. Recently, compressive sensing (CS) methods have been used for the accurate channel estimation. However, the classical CS algorithms require the channel sparsity in prior and the signal recovery accuracy is unacceptable when the signal-to-noise ratio (SNR) is low. To solve this problem, in this paper we exploit the Bayesian Compressive Sensing (BCS) based on statistical learning theory (SLT) and relevance vector machines (RVM) to improve the sparse channel estimation accuracy. Besides, we propose to reduce the correlation of the measurement matrix columns for a further performance improvement. Simulation results demonstrate that the proposed BCS-based channel estimation has better performance than conventional solutions.