This project addresses methods for increasing the density and robustness of data storage systems. Specifically, it extends our work on combined maximum transition run (MTR) coding and signal space detection to recording channels at very high densities and to cope more efficiently with realistic noise and nonlinearity encountered in such channels. In particular, MTR coding and other distance-enhancing code constraints will be investigated for use in future high density recording channels. Generalized signal space detection methods which will be used in conjunction with the distance-enhancing codes will also be studied. The proposed research tasks are: 1) performance analysis of MTR codes in the presence of medium noise and nonlinearity; 2) distance analysis and search for other distance-enhancing code constraints incorporating the data-dependent nature of transition noise and nonlinearity; 3) derivation of optimal hyperplanes better suited to data-dependent noise and nonlinearity; and 4) finding generalized signal space partitioning methods in higher dimensions to develop efficient constrained-delay detectors.
