A Study of Equalization for Magnetic Recording Channels

• Main Authors: Doong, Meng Chang, 董孟昌
• Other Authors: Lu Chung Chin
• Format: Others
• Language: zh-TW
• Published: 1994
• Online Access: http://ndltd.ncl.edu.tw/handle/85243887953463267614

碩士 === 國立清華大學 === 電機工程研究所 === 82 === Optical and magnetic recording systems are two main streams of data storage systems. For high data rate read/write applications, magentic recording seems to be a better choice up to now. A lot of techniques have been derived to increase the storage density and improve the quality. Peak detection is the most widely used detecting technique. Pulse sliming and write current shaping are used to "slim" the pulse in order to reduce the effect of ISI (intersymbol interference) at low and moderate density. As the linear density is increased, these techniques enhance too much noise . Partial response signaling is used to control ISI and avoid this situation. Viterbi detector has been used to improve the performance. Equalizer at the writing side, the write equalizer, is a high pass filter to compensate the frequency response of magnetic channels without noise enhancement. Beside equalization techniques, coding techniques are also investigated. Rll (run-length limited) codes are widely used to reduce the effect of ISI thus improve the performance. K.A.S Immink mentioned that RLL codes result in coding loss and degrade the performance of MLSE (maximum likelihood sequence estimation) detector over the Lorentzian linear channel with AWGN (additive white Gaussian noise). In this thesis, performance of RLL coddes with peak detection and MLSE detection has been evaluated by simulaion. The results confirm the coding loss phenomina and indicate the advantages of RLL codes in peak detection. This thesis is organized as follows. The magnetic recording channel models are described in Chapter 2. In Chapter3, some equalization techniques aiming directly at the magnetic recording channels are described. In Chapter 4, some properties of RLL sequences are narrated. In Chapter 5, performance of RLL codes with peak detection and MLSE detection is evaluated by simulation respectively. Finally, a conclusion is given in Chapter 6.