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

An promising theory: Matrix completion

Matrix completion, which was proposed by Candes and Tao, is a new matrix recovery technqiue which is come from compressed sensing (CS) theory. In general, Matrix reconstruction from few entries is impossiple. However, there exist recovery possiblity, if we know the Matrix has low-rank perpety. As Candes said, matrix completion will become a hot topic which can be employed many areas such as GPS and MIMO channel estimation.

MIMO channel estimation is a challenge task, especially in frequency-selective fading channel. Thus, MIMO channel estimation employ matrix completion which based on CS theory is a new idea. But, how we can do it? This is my research at present. If you have some suggestion about it, don't forget tell me. Waiting for your commances. Many thanks.