2 research outputs found
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
Bayesian equalization for LDPC channel decoding
We describe the channel equalization problem, and its prior estimate of the channel state information (CSI), as a joint Bayesian estimation problem to improve each symbol posterior estimates at the input of the channel decoder. Our approach takes into consideration not only the uncertainty due to the noise in the channel, but also the uncertainty in the CSI estimate. However, this solution cannot be computed in linear time, because it depends on all the transmitted symbols. Hence, we also put forward an approximation for each symbol's posterior, using the expectation propagation algorithm, which is optimal from the Kullback-Leibler divergence viewpoint and yields an equalization with a complexity identical to the BCJR algorithm. We also use a graphical model representation of the full posterior, in which the proposed approximation can be readily understood. The proposed posterior estimates are more accurate than those computed using the ML estimate for the CSI. In order to illustrate this point, we measure the error rate at the output of a low-density parity-check decoder, which needs the exact posterior for each symbol to detect the incoming word and it is sensitive to a mismatch in those posterior estimates. For example, for QPSK modulation and a channel with three taps, we can expect gains over 0.5 dB with same computational complexity as the ML receiver