  A low complexity massive multiple-input multiple-output (MIMO) technique is
studied with a geometry-based stochastic channel model, called COST 2100 model.
We propose to exploit the discrete-time Fourier transform of the antenna
correlation function to perform user scheduling. The proposed algorithm relies
on a trade off between the number of occupied bins of the eigenvalue spectrum
of the channel covariance matrix for each user and spectral overlap among the
selected users. We next show that linear precoding design can be performed
based only on the channel correlation matrix. The proposed scheme exploits the
angular bins of the eigenvalue spectrum of the channel covariance matrix to
build up an "approximate eigenchannels" for the users. We investigate the
reduction of average system throughput with no channel state information at the
transmitter (CSIT). Analysis and numerical results show that while the
throughput slightly decreases due to the absence of CSIT, the complexity of the
system is reduced significantly.
