Decision feedback equalization with convolutional coding and constrained delay

Jung-Tao Liu, Purdue University

Abstract

Error propagation is a significant problem with the decision feedback equalizer (DFE) at low to moderate SNR where channel coding is employed. The main objective of this work is to optimize the DFE for a coded system by introducing stationary error models to compensate for the error propagation. The modified DFE (MDFE) differs from conventional DFE only in its tap values, but must be obtained as the iterative solution of nonlinear equations. Bit error rate bounds taking into account error propagation in the convolutionally coded system are derived for both the DFE and MDFE. Simulations studies confirm that the MDFE can yield 4 dB gain over the DFE in a typical convolutionally coded system without interleaving, and large reduction in decoding delay over the conventional DFE with interleaving and the same performance. An adaptive MDFE solution is derived which incorporates the error propagation model into the training. The adaptive MDFE is compared with offline and adaptive DFE's. The adaptive MDFE has the best overall performance in a convolutionally coded system. A first and second order stability and performance analysis show a close match between the theory and experiments. The general design and performance of the conventional DFE with constrained decision delay is also considered (without error propagation). This is an unsolved problem in the literature. We derive the optimal infinite impulse response (IIR) fixed delay DFE, which is shown to require a two-sided linear filter, in addition to the feedback filter. A study of the corresponding finite impulse response (FIR) DFE shows that whenever the number of feedback taps is less than the channel memory and/or the noise is colored (as is the case with matched filtering, or oversampling), a two-sided linear filter should also be adopted. The two-sided linear filter improves the noise whitening and phase of the equivalent channel, resulting in improved performance over the FIR DFE with feedforward linear filter and the same delay.

Degree

Ph.D.

Advisors

Gelfand, Purdue University.

Subject Area

Electrical engineering

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