Compressive sensing based channel estimation for millimeter wave MIMO

Millimeter waves (MMW) are meant for high data rate short range indoor communications. The indoor environment is Doppler sparse, due to slow movement of objects and humans. Thus the Doppler spread becomes negligible. Owing to this fact channel estimation using compressive sensing is used. Conventionally, training symbol based linear channel estimation techniques least squares, minimum mean square error estimation is used in the multiantenna setup. The linear techniques work with the linear combination of multipath symbols and hence computationally complex. Hence in this work, compressive sensing and least square channel estimation techniques are compared with respect to the bit error rate. Comparative analysis indicate compressive sensing achieves the same performance as least square technique with respect to bit error rate however with reduced number of samples. In compressive sensing based channel estimation, Orthogonal Matching Pursuit (OMP) is used as the reconstruction algorithm due to its fast convergence rate and reduced computational complexity.