By Zining Wu
The creation of the web age has produced huge, immense call for for in creased garage ability and for the ensuing raises within the quantity of data that may be kept in a small area. whereas actual and media advancements have pushed nearly all of development in smooth garage structures, sign processing and coding equipment have expanding ly been used to reinforce these advancements. Run-length-limited codes and partial-response detection tools have become the norm in an that after rejected any sophistication within the learn or write professional cessing circuits. VLSI advances now let more and more refined sign processing equipment for negligible expense and complexity, a development certain to proceed at the same time disk entry speeds growth to billions of bits consistent with moment and terabits in line with sq. inch within the new millennium of the in formation age. This new ebook representing the Ph. D. dissertation paintings of Stanford's contemporary graduate Dr. Zining Wu is an updated and fo cused evaluate of the world that are supposed to be of worth to these simply beginning during this zone and besides people with significant services. using saturation recording, i. e. the mandated limit of two-level inputs, creates attention-grabbing twists at the use of communica tion/transmission tools in recording.
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Additional info for Coding and Iterative Detection for Magnetic Recording Channels
Magnetic recording channels are modeled as binary-input constraint lSI channels. , 1999] studied the implementation of turbo-equalization on the PR4 (1 - D2) and EPR4 (1 + D - D2 - D 3 ) channels and showed high coding gain on these equalized channels. The rest of the chapter is organized as follows. Section 2. discusses turbo encoder and coding gain for memory less channels. Section 3. summarizes the BCJR decoding algorithm. Section 4. introduces turbo equalization for intersymbol interference channels and reveals the performance of turbo codes on magnetic recording channels.
5 dB pure coding gain, respectively. As a result, it is very difficult to apply convolutional codes to the recording channels. Up until 1993, coding research on magnetic recording channels has focused on high rate codes so that the SNR penalty due to the rate loss is minimized. , 1998], the Modulo N code [Fredrickson, 1993], and the high-rate Matched Spectral Null (MSN) codes [Karabed and Siegel, 1991]. These codes usually provide a pure coding gain of less than 2 dB at high recording densities.
The module inputs are the a priori LLRs of the encoder input u and the encoder output x, represented as La(u) and La(x), respectively. The module outputs are the extrinsic information about u and x calculated by using La(u), La(x) and the code constraints. Please note that the a priori LLRs for x in this generalized SISO module is actually the "channel information" introduced above. 8: Soft-input Soft-output (SISO) module for symbol-by-symbol APP calculation. 4 COMPLEXITY OF THE BCJR ALGORITHM The BCJR algorithm is considerably more complicated than the harddecision Viterbi algorithm.