Compressed Sensing Based Channel Estimation And Adaptive Channel Prediction

In order to meet the demand for high data rate explodes nowadays, accurate channel state information (CSI) is needed in wireless communication systems. Numerous experimental stu-dies undertaken by various researchers in the recent past have shown that wireless channel associated with a number of scattering environment tends to exhibit sparse structures when operating at large bandwidths and symbol durations and with large plurality of antennas. Compressive sensing (CS) has gained much attention in communication and has been ac-cepted for sparse channel estimation. CS promises to estimate the channel with much less training sequences or at higher accuracy with a constant number of training sequences.The time-selective fading channel coefficient of Single-Input Single-output (SISO) sys-tem is a superposition of a few scatterers so it is considered sparse in Doppler domain. In this paper, we study the CS channel estimation for SISO system in time-selective fading channel. A revised CS channel estimation is used for time-selective fading channel to obtain the am-plitudes, Doppler shifts associated with individual scatterers. The simulation results demon-strate that amplitudes and Doppler shifts can be obtained precisely by the revised CS channel estimation. Meanwhile, channel changes rapidly caused by multipath fading in mobile wire-less communication systems. The estimated CSI is usually outdated and not optimized for current channel conditions thus reducing the system efficiency. In order to maintain the sys-tem performance, channel prediction is exploited to compensate the multipath fading. With the Doppler shifts corresponding to individual scatterers obtained by CS channel estimation, we use a Kalman channel prediction algorithm to track the channel variation. The simulation results demonstrate that the performance of channel prediction is enhanced with the more ac-curate Doppler shifts obtained by the revised CS channel estimation.Multi-input Multi-output (MIMO) technology attracts much attention by virtue of multi-ply increasing system capacity without increasing the system bandwidth. The channel tends to be sparse in angle domain when a large number of antennas are adopted. The virtual repre-sentation channel model provides a discretized approximation of the time-frequency response of the physical multipath channel. This representation exposes the relationship between the distribution of physical paths within the angle-delay-Doppler space. It sets the stage for the application of compressed sensing theory to sparse channel estimation of MIMO systems. In this paper, we explore the specifics of channel estimation for sparse time-selective channels of MIMO systems based on the virtual representation model. The iterative reweighted basic pursuit de-noising (BPDN) algorithm is introduced to reduce the punishment of large coeffi-cients and to increase the punishment of noise thus reduce the reconstruction error. Simulations demonstrate that it gives a better performance compared with the BPDN algorithm, orthogonal matching pursuit (OMP) algorithm and the conventional LS method.