Adaptive filter theory
Multiuser Detection
Principles and Applications of GSM
Principles and Applications of GSM
Microwave Mobile Communications
Microwave Mobile Communications
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Joint estimation and decoding of space-time Trellis codes
EURASIP Journal on Applied Signal Processing - Space-time coding and its applications - part I
Signal Processing - Signal processing in communications
EURASIP Journal on Applied Signal Processing
Channel tracking using particle filtering in unresolvable multipath environments
EURASIP Journal on Applied Signal Processing
Blind equalization of frequency-selective channels by sequential importance sampling
IEEE Transactions on Signal Processing - Part I
A survey of convergence results on particle filtering methods forpractitioners
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
A new class of soft MIMO demodulation algorithms
IEEE Transactions on Signal Processing
Multi-input multi-output fading channel tracking and equalizationusing Kalman estimation
IEEE Transactions on Signal Processing
A maximum likelihood approach to blind multiuser interferencecancellation
IEEE Transactions on Signal Processing
Resampling algorithms and architectures for distributed particle filters
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
Adaptive joint detection and decoding in flat-fading channels via mixture Kalman filtering
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
IEEE Journal on Selected Areas in Communications
Blind detection in MIMO systems via sequential Monte Carlo
IEEE Journal on Selected Areas in Communications
A fixed-lag particle smoother for blind SISO equalization of time-varying channels
IEEE Transactions on Wireless Communications
IEEE Transactions on Communications
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The ability to perform nearly optimal equalization of multiple input multiple output (MIMO) wireless channels using sequential Monte Carlo (SMC) techniques has recently been demonstrated. SMC methods allow to recursively approximate the a posteriori probabilities of the transmitted symbols, as observations are sequentially collected, using samples from adequate probability distributions. Hence, they are a class of online (adaptive) algorithms, suitable to handle the time-varying channels typical of high speed mobile communication applications. The main drawback of the SMC-based MIMO-channel equalizers so far proposed is that their computational complexity grows exponentially with the number of input data streams and the length of the channel impulse response, rendering these methods impractical. In this paper, we introduce novel SMC schemes that overcome this limitation by the adequate design of proposal probability distribution functions that can be sampled with a lesser computational burden, yet provide a close-to-optimal performance in terms of the resulting equalizer bit error rate and channel estimation error. We show that the complexity of the resulting receivers grows polynomially with the number of input data streams and the length of the channel response, and present computer simulation results that illustrate their performance in some typical scenarios.