SemiBlind channel estimation in cell-free massive MIMO

We consider cell-free (CF) massive MIMO (MaMIMO) systems, which comprise a very large number of geographically distributed access points (APs) serving a smaller number of users. We exploit channel sparsity to tackle pilot contamination, which originates from the reuse of pilot sequences. Specifically, we consider semi-blind methods for channel estimation in the presence of unknown data to resolve the pilot contamination. This task is further aided by exploiting prior channel information in a Bayesian formulation. We develop Bayesian Maximum a Posteriori (MAP) and Minimum Mean Squared-Error (MMSE) channel estimators and we also provide various Cramer-Rao Bounds to characterize performance limits. One contribution is the derivation of an original type of Bayesian CRB for the semiblind problem at hand, in which a certain expectation operation isWe consider cell-free (CF) massive MIMO (MaMIMO) systems, which comprise a very large number of geographically distributed access points (APs) serving a smaller number of users. We exploit channel sparsity to tackle pilot contamination, which originates from the reuse of pilot sequences. Specifically, we consider semi-blind methods for channel estimation in the presence of unknown data to resolve the pilot contamination. This task is further aided by exploiting prior channel information in a Bayesian formulation. We develop Bayesian Maximum a Posteriori (MAP) and Minimum Mean Squared-Error (MMSE) channel estimators and we also provide various Cramer-Rao Bounds to characterize performance limits. One contribution is the derivation of an original type of Bayesian CRB for the semiblind problem at hand, in which a certain expectation operation is facilitated by the asymptotics of the large system dimensions considered here. Whereas Bayesian CRBs lead to fairly useless lose bounds, corresponding to unrealistic genie-aided scenarios, the proposed variation turns out to be quite tight as illustrated by performance comparisons with various estimation algorithms. In particular we develop various message passing type approximate MMSE estimation algorithms based on Expectation Propagation. In particular various complexity variations are considered and their effect on convergence speed and estimation performance, as also distributed implementations. facilitated by the asymptotics of the large system dimensions considered here. Whereas Bayesian CRBs lead to fairly useless lose bounds, corresponding to unrealistic genie-aided scenarios, the proposed variation turns out to be quite tight as illustrated by performance comparisons with various estimation algorithms. In particular we develop various message passing type approximate MMSE estimation algorithms based on Expectation Propagation. In particular various complexity variations are considered and their effect on convergence speed and estimation performance, as also distributed implementations.