Convolutional Neural Network Based Symbol Detector for Two-Dimensional Magnetic Recording

Data detection in magnetic recording (MR) channels can be viewed as an image processing problem, proceeding from the 2-D image of readback bits, to higher level abstractions of features using convolutional layers that finally allow classification of individual bits. In this work, convolutional neural networks (ConvNets) are employed in place of the typical partial response equalizer and maximum-likelihood detector with noise prediction to directly process the un- equalized readback signals and output soft estimates. Several variations of ConvNets are compared in terms of network complexity and performance. The best performing ConvNet detector with two convolutional layers provides a data storage density of up to 3.7489 Terabits/in(2) on a low track pitch two-dimensional MR channel simulated with a grain-flipping-probability (GFP) model. An alternate ConvNet architecture reduces the network complexity by about 74%, yet results in only a 2.09% decrease in density compared to the best performing detector.

Automated summary

Data detection in magnetic recording channels is approached as an image processing problem using Convolutional Neural Networks. The best performing ConvNet detector achieves high data storage density on a low track pitch two-dimensional MR channel, while an alternate ConvNet architecture reduces network complexity with minimal decrease in performance.