Turbo EP-based Equalization: a Filter-Type Implementation

This manuscript has been submitted to Transactions on Communications on September 7, 2017; revised on January 10, 2018 and March 27, 2018; and accepted on April 25, 2018 We propose a novel filter-type equalizer to improve the solution of the linear minimum-mean squared-error (LMMSE) turbo equalizer, with computational complexity constrained to be quadratic in the filter length. When high-order modulations and/or large memory channels are used the optimal BCJR equalizer is unavailable, due to its computational complexity. In this scenario, the filter-type LMMSE turbo equalization exhibits a good performance compared to other approximations. In this paper, we show that this solution can be significantly improved by using expectation propagation (EP) in the estimation of the a posteriori probabilities. First, it yields a more accurate estimation of the extrinsic distribution to be sent to the channel decoder. Second, compared to other solutions based on EP the computational complexity of the proposed solution is constrained to be quadratic in the length of the finite impulse response (FIR). In addition, we review previous EP-based turbo equalization implementations. Instead of considering default uniform priors we exploit the outputs of the decoder. Some simulation results are included to show that this new EP-based filter remarkably outperforms the turbo approach of previous versions of the EP algorithm and also improves the LMMSE solution, with and without turbo equalization

Iterative PDF Estimation-Based Multiuser Diversity Detection and Channel Estimation with Unknown Interference

The equivalent diversity order of multiuser detector employing multiple receive antennas and minimum mean squared error (MMSE) processing for frequency-selective channels is decreased if it aims at suppressing unknown cochannel interference (UCCI) while detecting multiple users' signals. This is an unavoidable consequence of linear processing at the receiver. In this paper, we propose a new multiuser signal detection scheme with the aim to preserve the detector's diversity order by taking into account the structure of the UCCI. We use the fact that the structure of the UCCI appears in the probability density function (PDF) of the UCCI plus noise, which can be characterized as multimodal Gaussian. A kernel smoothing PDF estimation based receiver is derived. The PDF estimation can be based on training symbols only (noniterative PDF estimation) or on training symbols as well as feedback from the decoder (iterative PDF estimation). It is verified through simulations that the proposed receiver significantly outperforms the conventional covariance estimation in channels with low frequency selectivity. The iterative PDF estimation significantly outperforms the noniterative PDF estimation-based receiver with minor training overhead