The error-pattern correcting code (EPCC) is a code designed to correct frequently observed error cluster patterns of the intersymbol interference (ISI) channel. This paper focuses on developing theoretical understanding of the performance of serial concatenation of the EPCC with an outer recursive systematic convolutional code (RSCC) in ISI channel environments. To analyze the performance of this EPCC-RSCC concatenation, an upper union bound on the maximum-likelihood (ML) bit-error rate (BER), averaged over all possible interleavers, is derived which offers crucial insights into the error floor behavior of the matching turbo decoder. The ML bound is also used to compare the performance of EPCC-RSCC to that of a stand-alone RSCC in serial concatenation to precoded and nonprecoded ISI channels. This comparison shows that by targeting the low Hamming-weight interleaved errors of the RSCC, which result in low Euclidean distance error events in the channel detector, EPCC-RSCC exhibits a much lower BER floor compared to conventional schemes, especially for high rate applications and short interleaver lengths. The error rate performance of an iterative suboptimal turbo equalizer (TE), called TE-EPCC, is also demonstrated to converge close to the ML bound at high SNR.

The error pattern correcting code (EPCC) can be constructed to provide a syndrome decoding table targeting the dominant error events of an inter-symbol interference channel at the output of the Viterbi detector. For the size of the syndrome table to be manageable and the list of possible error events to be reasonable in size, the codeword length of EPCC needs to be short enough. However, the rate of such a short length code will be too low for hard drive applications. To accommodate the required large redundancy, it is possible to record only a highly compressed function of the parity bits of EPCC's tensor product with a symbol correcting code. In this paper, we show that the proposed tensor error-pattern correcting code (T-EPCC) is linear time encodable and also devise a low-complexity soft iterative decoding algorithm for EPCC's tensor product with q-ary LDPC (T-EPCC-qLDPC). Simulation results show that T-EPCC-qLDPC achieves almost similar performance to single-level qLDPC with a 1/2 KB sector at 50% reduction in decoding complexity. Moreover, 1 KB T-EPCC-qLDPC surpasses the performance of 1/2 KB single-level qLDPC at the same decoder complexity.