Efficient coordinated recovery of sparse channels in massive MIMO
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
KAUST Grant NumberEE002355
Preprint Posting Date2014-09-12
Permanent link to this recordhttp://hdl.handle.net/10754/563988
MetadataShow full item record
AbstractThis paper addresses the problem of estimating sparse channels in massive MIMO-OFDM systems. Most wireless channels are sparse in nature with large delay spread. In addition, these channels as observed by multiple antennas in a neighborhood have approximately common support. The sparsity and common support properties are attractive when it comes to the efficient estimation of large number of channels in massive MIMO systems. Moreover, to avoid pilot contamination and to achieve better spectral efficiency, it is important to use a small number of pilots. We present a novel channel estimation approach which utilizes the sparsity and common support properties to estimate sparse channels and requires a small number of pilots. Two algorithms based on this approach have been developed that perform Bayesian estimates of sparse channels even when the prior is non-Gaussian or unknown. Neighboring antennas share among each other their beliefs about the locations of active channel taps to perform estimation. The coordinated approach improves channel estimates and also reduces the required number of pilots. Further improvement is achieved by the data-aided version of the algorithm. Extensive simulation results are provided to demonstrate the performance of the proposed algorithms.
CitationMasood, M., Afify, L. H., & Al-Naffouri, T. Y. (2015). Efficient Coordinated Recovery of Sparse Channels in Massive MIMO. IEEE Transactions on Signal Processing, 63(1), 104–118. doi:10.1109/tsp.2014.2369005
SponsorsThis work was supported in part by King Abdullah University of Science and Technology (KAUST) and King Fahd University of Petroleum and Minerals (KFUPM) through research institute project number EE002355, and in part by a Deanship of Scientific Research (DSR) grant at KFUPM through project number RG-1314.