Abstract
Multi Stage Kalman Filter (MSKF) Based Time-Varying Sparse Channel Estimation With Fast Convergence
Highlights
Massive MIMO (MMIMO) systems are considered for high data rate communications in sparse channels, e. g. digital television (DTV) [1]- [2], echo cancellation, underwater [3], millimeter-wave 5G communications [4]
Like the popular sparsity based compressed sensing (CS) and Bayesian methods [13] - [21], yield superior performance in MMIMO mmwave spatially sparse channels, by exploiting the low rank angular structure induced by the multi-ray channel model with narrow angular spread (AS)
Motivation There exists a close connection between the ideas of compressed sensing (CS) and matrix rank minimization/reduced rank filters, in beam formed sparse channel estimation. [21], [20], [18]. [21] investigates single path mmwave channel sparsity in angular/space (Direction of Arrival (DOA)) domain, where the low-rank algebraic structure of the channel matrix is exploited by employing a reduced rank method, followed by a CS sparse method. [20] solves the same problem using CS methods only
Summary
Massive MIMO (MMIMO) systems are considered for high data rate communications in sparse channels, e. g. digital television (DTV) [1]- [2], echo cancellation, underwater [3], millimeter-wave (mmwave) 5G communications [4]. Like the popular sparsity based compressed sensing (CS) and Bayesian methods [13] - [21], yield superior performance in MMIMO mmwave spatially sparse channels, by exploiting the low rank angular structure induced by the multi-ray channel model with narrow angular spread (AS). These methods have been derived for single path (not multipath) channels, and do not utilize the temporal sparsity (in multipath lag) domain. The novel MSKF is able to reduce channel tracking errors of a standard Kalman filter, which occurs in a high-mobility, TV channel (as seen in Fig. 8 [23] and text below it)
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