Codes with provable guarantees for storage applications
Creators: name: University Of California Office Of The President
Publisher: name: University of California Office of the President
publisherIdentifier: https://ror.org/00dmfq477
publisherIdentifierScheme: ROR
Year: 2024

description: The future of magnetic recording technologies is premised on the assumption that areal densities exceeding 10Tb/in2 can be achieved. To meet these aggressive goals -- and to achieve them imminently -- it is necessary to develop error correction schemes to ensure that the data is very reliably written to and retrieved from the noisy storage medium. Error correction schemes built out of graph-based codes show excellent potential in meeting such stringent requirements. However, the performance of such codes is most often verified only through simulations. The problem is, as the requirements on the reliability become even more severe, the simulation time needed to evaluate code performance grows in proportion. Evaluation of codes for magnetic recording applications is already being expressed in months of simulation time. What makes this situation even worse is that the popular Monte Carlo simulations are utterly oblivious to the properties of the codes and their iterative decoding algorithms. Thus, when a code parameter is changed, the simulations start from scratch. In this project, we will develop an approach that tackles the question of performance evaluation from the code-centric perspective. This approach will use a theoretical framework recently pioneered by PI. Our research results will obviate the need for lengthy and uninformative Monte Carlo simulations. The proposed mathematical set-up unifies the analysis of both the decoding errors due to non-codewords and the decoding errors due to codewords. This framework enables a systematic and quick design methodology premised on combinatorial and graph-theoretic properties of good codes. The expected code performance will be computed using sophisticated analytical and statistical methods based on the theory of large deviations and importance sampling. This will be done in a fraction of time required by nominal Monte Carlo simulations. In the seed part of this proposal we will focus on the specific but extremely important class of approximate inference algorithms that describe message passing decoding algorithms. While making significant contributions in the specific domain of error control coding systems, the proposed research will also be laying the groundwork for much broader impact. Our long-term objective is to provide efficient system characterization based on core but simple structure, and to provide efficient system evaluation based on this core structures. Such results will be of direct benefit to a whole range and a diverse set of complex engineering systems that deal with simulation-based performance evaluation. These include increasingly important statistical nano-level circuit evaluation, wireless sensor applications, and the inferences in smart-grid networks. The innovations enabled by this proposal will thus pave the way for a systematic and quick evaluation of the increasingly complex technological infrastructure that is of central importance to California and its people.