Self-Directed Equalization for Magnetic Recording Channels With Multi-Sensor Read Head

Today, adaptive equalization is generally used to capture the run-time read channel dynamics in the presence of a random read-head offset. An adaptive equalizer updates its coefficients using a certain adaptive signal-processing algorithm, e.g., least-mean-square algorithm. This paper proposes a self-directed equalization strategy for the emerging very high-density magnetic recording drives with a multi-sensor read head. The key idea is to exploit the spatial diversity of readback signals from different read sensors to accurately estimate run-time read-head offsets, based upon which the equalizer coefficients are accordingly self-configured in order to improve the equalization performance. This paper presents specific methods to practically implement this design strategy, and its effectiveness has been well demonstrated through the simulations with a Voronoi-based channel model.