As the throughput and density requirements increase for perpendicular magnetic recording channels, the presence of strong media noise degrades performance. \ud \ud Detection algorithms have been developed that increase performance in channels with strong media noise through the use of data dependent detectors. However optimal data dependent detectors are exponentially more complex than data independent detectors, and therefore cannot be fully exploited. In this thesis we shall discuss the existing detection algorithms, comparing the performance against the complexity.\ud \ud We then introduce a new sub-optimal detection algorithm, which employs a simple pre-detector that supplies estimates to a main detector. Numerical simulations are performed which show near optimal performance, but without the exponential increase in complexity.\ud \ud We will also show how detector implementations can exploit structure in the trellis to further reduce complexity, through loops and path invariants. \ud \ud An analytical means of measuring bit error rate from only the statistics of noise is presented, and this is utilised to optimally determine the equaliser and ISI target coefficients for a white noise Viterbi detector. \ud \ud Finally, we introduce a new class of VLSI binary addition algorithms which can be utilised to increase the throughput of a Viterbi detector, but which also has a wider application in hardware design
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