Peter Mcewen

Punctured recursive convolutional encoders and their applications in turbo codes

Puncturing is the predominant strategy to construct high code rate convolutional encoders, and infinite impulse response convolutional encoders are an essential building block in turbo codes. In this paper various properties of convolutional encoders with these characteristics are developed. In particular, the closed form representation of a punctured convolutional encoder and its generator matrix are constructed, necessary and sufficient conditions are given such that the punctured encoders retain the infinite impulse response property, and various lower bounds on distance properties, such as effective free distance, are developed. Finally, necessary and sufficient conditions are given on the inverse puncturing problem: representing a known convolutional encoder as a punctured encoder.

Event error control codes and their applications

Burst error detection codes and cyclic redundancy check (CRC) codes are generalized to event-error detection codes, which are useful in various noisy channels. A systematic linear block code is constructed that detects any event error from an arbitrary list of event errors. The result is generalized to detection and correction of multiple event errors. Bounds are found on the minimum number of redundant bits needed to construct such codes. It is shown that, under certain conditions, the linear code construction is optimal. Various applications are discussed, where there is a Markov source or a Markov channel. It is argued that the codes described herein can be employed either as error detection codes, or as distance-enhancing codes when complete decoders are applied. Specific examples covered in this correspondence include hybrid automatic repeat request (ARQ) systems, intersymbol interference (ISI) channels, and Gilbert (1958) channels.

Getting the information in and out: The channel (invited)

Magnetic recording channels perform several signal processing tasks in the process of storing and retrieving data. Among them are precompensation, equalization, timing recovery, detection, and coding. A range of algorithms is available for each of these tasks with different levels of complexity and performance. We describe how each of these tasks is done today and indicate how they are likely to change as requirements and implementation technologies evolve.

Monte Carlo study of inductive heads with PRML recording channel

We present a Monte Carlo simulation of inductive recording heads with a PRML recording channel. We show the effects of various head parameters such as gap length, throat height, track width, pole thickness, pulse undershoot and flying height on signal to noise (SNR) and bit error rates. The analysis shows a strong dependency of bit error rate on flying height, track width and pulse undershoot.