Joint Self-Iterating Equalization for 2-D ISI in Disk Drives
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Solution Overview
Problem
Two-dimensional (2-D) intersymbol-interference (ISI) channels in data recording systems, such as magnetic disk drives, face challenges in accurate detection and equalization due to the complexity of 2-D MAP detection, which is NP-complete and difficult to realize for signal dimensions greater than a few bits, leading to suboptimal performance and error propagation in existing 1-D detection methods.
Innovation Solution
The implementation of a joint self-iterating soft equalization and iterative turbo 2-D maximum a-posteriori (MAP)-based detection method using a 2-D minimum mean-square error (MMSE) self-iterating equalizer and a turbo detector framework, which iteratively updates filter coefficients and passes soft extrinsic information to achieve near-optimal performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 2-D MAP detection is implemented to achieve optimal detection performance, then detection accuracy is improved, but computational complexity becomes NP-complete and difficult to realize
Solution Approach 1:
The patent segments the complex 2-D detection problem into separate 1-D detection steps along different directions (e.g., horizontal and vertical directions in TDMR systems). By dividing the 2-D ISI channel into multiple 1-D channels, the NP-complete 2-D MAP detection is transformed into a sequence of computationally feasible 1-D MAP detections, maintaining detection accuracy while reducing complexity to a practical level.
Solution Approach 2:
The patent transforms the 2-D detection problem by introducing a temporal dimension through iterative processing. Multiple iterative detection passes are performed, where each pass refines the detection results by incorporating information from previous iterations. This converts the intractable 2-D single-pass problem into a sequence of 1-D problems solved iteratively, achieving near-optimal 2-D detection performance with manageable computational complexity.
2Device complexity
If 1-D detection methods are used to reduce complexity, then computational complexity is reduced, but error propagation occurs and performance becomes suboptimal
Solution Approach 1:
The patent implements feedback mechanisms where detection results from one direction (e.g., horizontal detection) are fed back as prior information to improve detection in the orthogonal direction (e.g., vertical detection), and vice versa. This iterative feedback process allows errors to be corrected across multiple passes, reducing error propagation while maintaining low computational complexity per iteration. The feedback loop enables the system to converge toward optimal detection performance without requiring complex 2-D MAP detection.
Data Source
AI summary
A disk drive is configured to decode data written over a plurality of data tracks using joint self-iterating soft equalization and an iterative turbo 2-D MAP-based detection, by performing soft self-iterating linear 2-D MMSE equalization on received input samples; computing MMSE estimates for coefficients of a 2-D equalization filter based on a-priori values, mean of the a-priori values, the 2-D filter and the received input samples; updating the coefficients of the 2-D filter using a variance based on the a-priori values when carrying out a first iteration and based on an extrinsic LLR when carrying out subsequent iterations; determining values of the individual bits in the input samples using the updated coefficients; computing an output LLR of each individual received sample based on the computed MMSE estimate, the mean and the variance, and computing the extrinsic LLR by subtracting a priori LLR from the output LLR.