Iterative decoding for magnetic recording channels.

The success of turbo codes indicates that performance close to the Shannon limit may be achieved by iterative decoding. This has in turn stimulated interest in the performance of iterative detection for partial-response channels, which has been an active research area since 1999. In this dissertation, the performance of serially concatenated recording systems is investigated by computer simulations as well as experimentally. The experimental results show that the iterative detection algorithm is not sensitive to channel nonlinearities and the turbo coded partial-response channel is substantially better than partial-response maximum-likelihood channels. The classical iterative decoding algorithm was originally designed for additive white Gaussian noise channels. This dissertation shows that the performance of iterative detection can be significantly improved by considering the noise correlation of the magnetic recording channel. The idea is to iteratively estimate the correlated noise sequence at each iteration. To take advantage of the noise estimate, two prediction techniques were proposed, and the corresponding systems were named noise predictive turbo systems. These noise predictive turbo systems can be generalized to other detector architectures for magnetic recording channels straightforwardly

An Iterative Joint Linear-Programming Decoding of LDPC Codes and Finite-State Channels

In this paper, we introduce an efficient iterative solver for the joint linear-programming (LP) decoding of low-density parity-check (LDPC) codes and finite-state channels (FSCs). In particular, we extend the approach of iterative approximate LP decoding, proposed by Vontobel and Koetter and explored by Burshtein, to this problem. By taking advantage of the dual-domain structure of the joint decoding LP, we obtain a convergent iterative algorithm for joint LP decoding whose structure is similar to BCJR-based turbo equalization (TE). The result is a joint iterative decoder whose complexity is similar to TE but whose performance is similar to joint LP decoding. The main advantage of this decoder is that it appears to provide the predictability of joint LP decoding and superior performance with the computational complexity of TE.Comment: To appear in Proc. IEEE ICC 2011, Kyoto, Japan, June 5-9, 201

Turbo-Equalization Using Partial Gaussian Approximation

This paper deals with turbo-equalization for coded data transmission over intersymbol interference (ISI) channels. We propose a message-passing algorithm that uses the expectation-propagation rule to convert messages passed from the demodulator-decoder to the equalizer and computes messages returned by the equalizer by using a partial Gaussian approximation (PGA). Results from Monte Carlo simulations show that this approach leads to a significant performance improvement compared to state-of-the-art turbo-equalizers and allows for trading performance with complexity. We exploit the specific structure of the ISI channel model to significantly reduce the complexity of the PGA compared to that considered in the initial paper proposing the method.Comment: 5 pages, 2 figures, submitted to IEEE Signal Processing Letters on 8 March, 201

The Error-Pattern-Correcting Turbo Equalizer

The error-pattern correcting code (EPCC) is incorporated in the design of a turbo equalizer (TE) with aim to correct dominant error events of the inter-symbol interference (ISI) channel at the output of its matching Viterbi detector. By targeting the low Hamming-weight interleaved errors of the outer convolutional code, which are responsible for low Euclidean-weight errors in the Viterbi trellis, the turbo equalizer with an error-pattern correcting code (TE-EPCC) exhibits a much lower bit-error rate (BER) floor compared to the conventional non-precoded TE, especially for high rate applications. A maximum-likelihood upper bound is developed on the BER floor of the TE-EPCC for a generalized two-tap ISI channel, in order to study TE-EPCC's signal-to-noise ratio (SNR) gain for various channel conditions and design parameters. In addition, the SNR gain of the TE-EPCC relative to an existing precoded TE is compared to demonstrate the present TE's superiority for short interleaver lengths and high coding rates.Comment: This work has been submitted to the special issue of the IEEE Transactions on Information Theory titled: "Facets of Coding Theory: from Algorithms to Networks". This work was supported in part by the NSF Theoretical Foundation Grant 0728676

Turbo Detection of Space-time Trellis-Coded Constant Bit Rate Vector-Quantised Videophone System using Reversible Variable-Length Codes, Convolutional Codes and Turbo Codes

In this treatise we characterise the achievable performance of a proprietary video transmission system, which employs a Constant Bit Rate (CBR) video codec that is concatenated with one of three error correction codecs, namely a Reversible Variable-Length Code (RVLC), a Convolutional Code (CC) or a convolutional-based Turbo Code (TC). In our investigations, the CBR video codec was invoked in conjunction with Space-Time Trellis Coding (STTC) designed for transmission over a dispersive Rayleigh fading channel. At the receiver, the channel equaliser, the STTC decoder and the RVLC, CC or TC decoder, as appropriate, employ the Max-Log Maximum A-Posteriori (MAP) algorithm and their operations are performed in an iterative 'turbo-detection' fashion. The systems were designed for maintaining similar error-free video reconstruction qualities, which were found to be subjectively pleasing at a Peak Signal to Noise Ratio (PSNR) of 30.6~dB, at a similar decoding complexity per decoding iteration. These design criteria were achieved by employing differing transmission rates, with the CC- and TC-based systems having a 22% higher bandwidth requirement. The results demonstrated that the TC-, RVLC- and CC-based systems achieve acceptable subjective reconstructed video quality associated with an average PSNR in excess of 30~dB for $E_b/N_0$ values above 4.6~dB, 6.4~dB and 7.7~dB, respectively. The design choice between the TC- and RVLC-based systems constitutes a trade-off between the increased error resilience of the TC-based scheme and the reduced bandwidth requirement of the RVLC-based scheme

An Iteratively Decodable Tensor Product Code with Application to Data Storage

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.Comment: Hakim Alhussien, Jaekyun Moon, "An Iteratively Decodable Tensor Product Code with Application to Data Storage