Bio

Elnaz Banan Sadeghian received the B.S. degree in electrical engineering from Shahid Beheshti University, Tehran, Iran, in 2005, and the M.S. degree in Biomedical Engineering from Amirkabir University of Technology, Tehran, Iran, in 2008. She is currently pursuing her Ph.D. degree in electrical engineering at the Georgia Institute of Technology, Atlanta, Georgia, USA. Her current research interests are in the area of signal processing and communication theory, including synchronization, equalization, and coding as applied to magnetic recording channels.

Detector for Two-Dimensional Magnetic Recording

Detector for Two-Dimensional Magnetic Recording

The data industry such as Google, Facebook, Yahoo, and also many other organizations, rely heavily on data storage facilities to store their valuable data. Hard disk drives, due to their reliability and extremely cheap price, form a main part of these data storage facilities. The disk drive industry is currently pursuing a huge increase in the recorded data density up to 10 Terabits per square inch of the medium through two-dimensional magnetic recording (TDMR). I work toward realization of this technology, specifically, to design a detector which can recover the data from extremely dense hard drives. This is a challenge, in part because this novel technology shrinks the widths of the data tracks to such an extent that an attempt to read data from one track will inevitably lead to interference from neighboring tracks, and in part because of the challenging nature of the magnetic medium itself. The combination of interference between different tracks and along adjacent bits on each track is a key challenge for TDMR and motivates the development of two-dimensional signal processing strategies of manageable complexity to mitigate this two-dimensional interference. To address this issue, we have designed a novel detection strategy for TDMR recording channel with multiple read heads. Our method suppresses the intertrack interference and thereby reduces the detection problem to a traditional one-dimensional problem, so that we may leverage existing one-dimensional iterative detection strategies. Simulation results show that our proposed detector is able to reliably recover five tracks from an array of five read heads at an acceptable signal-to-noise ratio. Further, we are working on a detector which also performs the task of synchronizing the reader and the writer clock speeds so that the data can be extracted more accurately. Obtained results from this research can help greatly increase hard disk capacities through TDMR.