Kalman filtering: theory and practice
Kalman filtering: theory and practice
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Joint Time-Domain Tracking of Channel and Frequency Offsets for MIMO OFDM Systems
Wireless Personal Communications: An International Journal
EURASIP Journal on Applied Signal Processing
Efficient particle filtering for jump Markov systems. Application to time-varying autoregressions
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing - Part II
Optimal training design for MIMO OFDM systems in mobile wireless channels
IEEE Transactions on Signal Processing
Multi-input multi-output fading channel tracking and equalizationusing Kalman estimation
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Particle filters for state estimation of jump Markov linear systems
IEEE Transactions on Signal Processing
A QRD-M/Kalman filter-based detection and channel estimation algorithm for MIMO-OFDM systems
IEEE Transactions on Wireless Communications
Iterative detection and frequency synchronization for OFDMA uplink transmissions
IEEE Transactions on Wireless Communications
IEEE Transactions on Wireless Communications
Synchronization algorithms for MIMO OFDMA systems
IEEE Transactions on Wireless Communications
Multiuser OFDM with adaptive subcarrier, bit, and power allocation
IEEE Journal on Selected Areas in Communications
Adaptive control of surviving symbol replica candidates in QRM-MLD for OFDM MIMO multiplexing
IEEE Journal on Selected Areas in Communications
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Joint carrier frequency offset (CFO) and channel estimation for uplink MIMO-OFDMA systems over time-varying channels is investigated. To cope with the prohibitive computational complexity involved in estimating multiple CFOs and channels, pilot-assisted and semi-blind schemes comprised of parallel Schmidt Extended Kalman filters (SEKFs) and Schmidt-Kalman Approximate Particle Filters (SK-APF) are proposed. In the SK-APF, a Rao-Blackwellized particle filter (RBPF) is developed to first estimate the nonlinear state variable, i.e. the desired user's CFO, through the sampling-importance-resampling (SIRS) technique. The individual user channel responses are then updated via a bank of Kalman filters conditioned on the CFO sample trajectories. Simulation results indicate that the proposed schemes can achieve highly accurate CFO/channel estimates, and that the particle filtering approach in the SK-APF outperforms the more conventional Schmidt Extended Kalman Filter.