Multi Stage Kalman Filter (MSKF) Based Time-Varying Sparse Channel Estimation With Fast Convergence

The paper develops novel algorithms for time-varying (TV) sparse channel estimation in Massive multiple-input, multiple-output (MMIMO) systems. This is achieved by employing a reduced (non-uniformly spaced tap) delay-line equalizer, which can be related to low/reduced rank filters. low filter implemented deriving an innovative TV (Krylov-space based) Multi-Stage Kalman Filter (MSKF), appropriate state techniques. MSKF converges very quickly, within few stages/iterations (at each symbol). possible because uses those signal spaces, maximally correlated with the desired signal, rather than standard principal component (PCA) spaces. also able reduce tracking errors, encountered high-mobility channel. In addition, well suited large-scale MMIMO unlike most existing methods, including recent Bayesian-Belief Propagation, Krylov, fast iterative re-weighted compressed sensing (RCS) and minimum minimization requires more iterations converge, as scale of system increases. A Bayesian Cramer Rao lower bound (BCRLB) noisy CS (in channel) derived, provides benchamrk performance other estimators.