Modeling of a simple writing process considering intergranular exchange fields and magnetostatic interaction fields between grains is studied for two-dimensional magnetic recording (TDMR). A new designing method of a two-dimensional neural network equalizer with a mis-equalization suppression function (2D-NNEMS) for TDMR is also proposed. The bit-error rate (BER) performance of a low-density parity-check coding and iterative decoding system with the designed 2D-NNEMS is obtained via computer simulation using a read/write channel model employing the proposed writing process under TDMR specifications of 4 Tb/in$ 2, and it is compared with those for one- and two-dimensional finite impulse response equalizers (FIREs). It is clarified that the BER performance for the designed 2D-NNEMS is far superior to those for the FIREs.
- Channel modeling
- genetic algorithms
- magnetic cluster
- two-dimensional magnetic recording (TDMR)
- two-dimensional neural network equalizer (2D-NNE)