Analogy matrix completion to MIMO channel recovery

As compresed sensing (CS) theory was developed by Candes and Tao, Donoho, sparse signal or approximate sparse signal recovery can be reconstruction exactly. On the heels of CS, a remarkable new field has very recently emerged. This field addresses a broad range of a data matrix from what appears to be incomplete, and perhaps even corrupted, information. This problem can be called as Matrix completion (MC).

In general, recovering a data matrix by a part of its entries is impossiable. However, if the unknown matrix is known to have llow rank or approximately low rank, then accurate recovery is possible. This recovery problem can be analogy to MIMO channel matrix modeling.

Channel estimation is a key problem in MIMO communication systems. In general, least square (LS) algorithm is a good candidata for channel estimation or modeling. However, this method will waste a lot of spectrum resource. If we employ the channel matrix completion, will improve greatly the spectrum resource. Recently, we will research this method on MIMO channel estiamtion. If you have some good suggestions about the MIMO channel matrix recovery, please do not hesitate to tell me. My email box is ：gui@uestc.edu.cn

One of my reseach topics: MIMO Channel Estimation

The importance of wireless channel estimation and understanding channel knowledge for successful design of communication systems can never be overstated. In eariler of channel estimation, the wireless medium was viewed as an obstacle or challenge in designing reliable communication links. However, decades of research and subsequent insights have change this paradigm. Modern day communcation systems rather tend to exploit the channel knowledge for increasing system reliablity and thoughtput by employing techniques such as MIMO, which is the key for extending the limits of existing communication systems. Thus, MIMO channel estimation become a most important challenge because it must be to overcome the highly frequency selective of the fading channel.