TF-Capacity-Approaching Coding and Detection for Page-Oriented Digital Recording Channels.

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The availability of inexpensive, reliable, high-capacity data storage devices in compact form factors has revolutionized personal computing and information management during the past decade. More recently, with the introduction of high-density disk drives into such products as digital cameras, audio players, and set-top video recording systems, storage devices are transforming the world of consumer electronics. These popular applications, as well as the use of mass storage systems in science and commerce, will motivate continued advances in magnetic recording technology. These advances, such as perpendicular recording, are expected to yield areal densities as high as 500 billion bits of information per square inch. Beyond that, however, magnetic recording technology will likely encounter fundamental physical limits on achievable density and data transfer rate. Therefore, it is vital to pursue new approaches to high capacity digital information storage. Many of these approaches use page-oriented reading and writing, in contrast to today's track-oriented methods. Examples include recording on nano-scale patterned media, multi-beam two-dimensional optical recording, and optical holographic recording. This project studies theoretical limits on the storage capacity of these page-oriented technologies, as well as the signal processing and coding algorithms needed to achieve those limits in practice. Specifically, the project addresses theoretical and algorithmic aspects of coding and detection for noisy two-dimensional channels with intersymbol interference channels. The investigators study novel iterative, soft-input, soft-output detection methods for these channels, as well as capacity-approaching algebraic and graph-based error-control codes and their decoding algorithms. The project also develops information-theoretic foundations and construction techniques for two-dimensional constrained modulation codes. These signal processing and coding methods are combined in novel system architectures, whose performance is evaluated using analytical and computational methods.