Performance of a signal-dependent autoregressive channel model

Recently, work has been published on a channel model that employs signal-dependent autoregressive filters to accurately and efficiently model signal nonlinearities and media noise characteristics, such as signal-dependence and correlation, which are common in magnetic recording signals. This paper evaluates the model's performance using 42 sets of spin-stand data that cover a large range of head/media manufacturers and operating points.