2- https://doi.org/10.1117/12.919302
Bayesian sparse channel estimation
- May 1, 2012
- Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE
- Chulong Chen +1 more
In Orthogonal Frequency Division Multiplexing (OFDM) systems, the technique used to estimate and track the time-varying multipath channel is critical to ensure reliable, high data rate communications. It is recognized that wireless channels often exhibit a sparse structure, especially for wideband and ultra-wideband systems. In order to exploit this sparse structure to reduce the number of pilot tones and increase the channel estimation quality, the application of compressed sensing to channel estimation is proposed. In this article, to make the compressed channel estimation more feasible for practical applications, it is investigated from a perspective of Bayesian learning. Under the Bayesian learning framework, the large-scale compressed sensing problem, as well as large time delay for the estimation of the doubly selective channel over multiple consecutive OFDM symbols, can be avoided. Simulation studies show a significant improvement in channel estimation MSE and less computing time compared to the conventional compressed channel estimation techniques.
- # Channel Estimation
- # Channel Estimation MSE
- # Compressed Channel Estimation
- # High Data Rate Communications
- # Bayesian Learning Framework
- # Multiple Orthogonal Frequency Division Multiplexing Symbols
- # Orthogonal Frequency Division Multiplexing
- # Multiple Orthogonal Frequency Division Multiplexing
- # Conventional Channel Estimation Techniques
- # Sparse Structure
