{"title":"2D-Pdnp多道检测的最小误码率调整","authors":"Shanwei Shi, J. Barry","doi":"10.1109/TMRC49521.2020.9366714","DOIUrl":null,"url":null,"abstract":"A dominant impediment in magnetic recording is pattern-dependent media noise, and its impact will only grow more severe as areal densities increase. The pattern-dependent noise prediction (PDNP) algorithm [1] [2], widely used as an effective strategy for mitigating pattern-dependent media noise in single-track detection, has recently been extended to the multitrack scenario [3]; it uses 2D patterns (spanning multiple tracks) to mitigate both downtrack and crosstrack pattern-dependent noise, based on the architecture shown in Fig. 1. The sampled readback waveforms are filtered by a $2 \\times 2$ MIMO equalizer with coefficients $\\mathbf{C}$, whose output $\\mathbf{y}_{k}=\\left[y_{k}^{(1)}, y_{k}^{(2)}\\right]^{T}$ is passed to a 2D -PDNP multitrack detector. Associated with each 2D bit pattern is a signal level vector $\\mathbf{s}$, a standard deviation diagonal matrix $\\Lambda$, and a set of matrix-valued predictor coefficients $\\mathbf{P}_{0}, \\mathbf{P}_{1}, \\ldots, \\mathbf{P}_{N_{p}-1} $. The branch metric of edge e for the 2D -PDNP Viterbi detector is [3]:","PeriodicalId":131361,"journal":{"name":"2020 IEEE 31st Magnetic Recording Conference (TMRC)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Minimum-Bit-Error Rate Tuning for 2D-Pdnp Multitrack Detection\",\"authors\":\"Shanwei Shi, J. Barry\",\"doi\":\"10.1109/TMRC49521.2020.9366714\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A dominant impediment in magnetic recording is pattern-dependent media noise, and its impact will only grow more severe as areal densities increase. The pattern-dependent noise prediction (PDNP) algorithm [1] [2], widely used as an effective strategy for mitigating pattern-dependent media noise in single-track detection, has recently been extended to the multitrack scenario [3]; it uses 2D patterns (spanning multiple tracks) to mitigate both downtrack and crosstrack pattern-dependent noise, based on the architecture shown in Fig. 1. The sampled readback waveforms are filtered by a $2 \\\\times 2$ MIMO equalizer with coefficients $\\\\mathbf{C}$, whose output $\\\\mathbf{y}_{k}=\\\\left[y_{k}^{(1)}, y_{k}^{(2)}\\\\right]^{T}$ is passed to a 2D -PDNP multitrack detector. Associated with each 2D bit pattern is a signal level vector $\\\\mathbf{s}$, a standard deviation diagonal matrix $\\\\Lambda$, and a set of matrix-valued predictor coefficients $\\\\mathbf{P}_{0}, \\\\mathbf{P}_{1}, \\\\ldots, \\\\mathbf{P}_{N_{p}-1} $. The branch metric of edge e for the 2D -PDNP Viterbi detector is [3]:\",\"PeriodicalId\":131361,\"journal\":{\"name\":\"2020 IEEE 31st Magnetic Recording Conference (TMRC)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-08-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE 31st Magnetic Recording Conference (TMRC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TMRC49521.2020.9366714\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 31st Magnetic Recording Conference (TMRC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TMRC49521.2020.9366714","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Minimum-Bit-Error Rate Tuning for 2D-Pdnp Multitrack Detection
A dominant impediment in magnetic recording is pattern-dependent media noise, and its impact will only grow more severe as areal densities increase. The pattern-dependent noise prediction (PDNP) algorithm [1] [2], widely used as an effective strategy for mitigating pattern-dependent media noise in single-track detection, has recently been extended to the multitrack scenario [3]; it uses 2D patterns (spanning multiple tracks) to mitigate both downtrack and crosstrack pattern-dependent noise, based on the architecture shown in Fig. 1. The sampled readback waveforms are filtered by a $2 \times 2$ MIMO equalizer with coefficients $\mathbf{C}$, whose output $\mathbf{y}_{k}=\left[y_{k}^{(1)}, y_{k}^{(2)}\right]^{T}$ is passed to a 2D -PDNP multitrack detector. Associated with each 2D bit pattern is a signal level vector $\mathbf{s}$, a standard deviation diagonal matrix $\Lambda$, and a set of matrix-valued predictor coefficients $\mathbf{P}_{0}, \mathbf{P}_{1}, \ldots, \mathbf{P}_{N_{p}-1} $. The branch metric of edge e for the 2D -PDNP Viterbi detector is [3]: