New Angles on the Multi-Dimensional Intersymbol Interference Problem

多维码间干扰问题的新视角

• 批准号: 0635390
• 负责人: Benjamin Belzer
• 金额: --
• 依托单位: Washington State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-15 至 2010-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0635390&HistoricalAwards=false
• 关键词: New; Angles; Multi; Dimensional; Intersymbol

Traditional single-track magnetic and optical disk storage technologies have reached their density limits. To continue the historical trend of exponentially increasing storage density, two-dimensional (2D) storage techniques, wherein bits are written and/or read in 2D blocks, are being developed by industry. These 2D storage systems suffer from 2D intersymbol interference (ISI) due to the low-pass nature of the read/write system. The 2D-ISI can be modeled as two-dimensional convolution of the input block with a finite-extent 2D blurring function, or "mask", followed by additive noise.The well-known Viterbi algorithm (VA) provides the maximum likelihood sequence estimate (MLSE) for detection of 1D sequences on 1D ISI channels. The problem in two (or higher) dimensions is considerably more difficult, due in part to the lack of a natural order in 2D as opposed to 1D. Relatively speaking, the 2D problem is not as well understood, and the performance of known methods is less than satisfactory. In recent publications, we describe 2D ISI equalization algorithms, based on zig-zag scanning of the corrupted 2D data, that substantially outperform all previously published work, and that come very close to the 2D-MLSE bound, the theoretically best attainable performance in terms of minimal bit error rate for a given signal-to-noise (SNR) ratio. Also, we have demonstrated that the 2D correlation present in manydata files can be exploited by the 2D equalizer to achieve even further performance gains. To build on these promising preliminary results, we propose to develop a theoretical framework for efficient design and optimization of two and higher-dimensional ISI equalization algorithms, for both independent and correlated data, with both binary and non-binary ("M-ary") symbols.The proposed research employs a unified approach to MLSE for independent and correlated binary and M-ary multi-dimensional data; problems are addressed by designing, analyzing and demonstrating iterative algorithms based on the turbo principle, a concept borrowed from the iterative turbo decoding algorithm that has revolutionized the field of channel coding. The PIs have a number of promising preliminary results that serve to point out additional promising areas of investigation; these preliminary results include:An iterative row-column soft-decision feedback algorithm for 2D-ISI reduction in 2D binary data,which outperforms the best previously published result by about 0.4 dB at high SNR.A zig-zag 2D ISI equalization algorithm, which, when concatenated with the row-column algorithm, outperforms the best previously published result by about 0.7 dB at high SNR, and comes within 0.2 dB of the 2D MLSE performance bound.An algorithm for joint Markov random field estimation and 2D ISI equalization, which achieves up to 2 dB SNR gains over 2D ISI equalization alone, when the original 2D binary source is correlated.The proposed iterative algorithms use both row-column and zig-zag maximum-a-posteriori (MAP) detectors which exchange soft estimates, resulting in significantly improved data estimates compared to previously proposed row-column iterative algorithms. The benefits of adding additional scan orders to the iterative algorithm will be explored, for both 2D and 3D ISI, and for both correlated and non-correlated source data. Iterative detection will be combined with iterative decoding of low-density parity-check (LDPC) codes to perform joint decoding and detection in 2D and 3D ISI. New complexity reduction techniques will be investigatedto handle multi-dimensional ISI for sources with M-ary symbols.Broader impacts of the proposal:The proposed project addresses the problem of decoding and detection in multi-dimensional ISI. As such, it combines techniques from the two related yet distinct fields of expertise of the project's co-PIs: image processing (Sivakumar) and communications (Belzer). This yields a good synergy between problem formulations and known solution techniques between the two fields.The proposed research will produce a class of iterative algorithms for ML solutions to the multidimensional ISI equalization problem, thereby significantly improving storage densities and data rates for magnetic and optical storage. The project will also benefit the emerging technology of holographic storage, wherein lasers are used to store bits in stacks of 2D pages, leading to intra-page 2D ISI, and, at higher densities, 3D ISI due to inter-page interference. The project will result in advances in error control coding for 2D and 3D storage channels, thereby enabling further increases in storage density. Finally, we expect that the novel complexity reduction techniques we propose to develop for M-ary multi-dimensional ISI channels will also be of use on 1D ISI channels, which occur in a wide variety of telecommunication applications.The educational impact of this project will be the recruitment and training of undergraduate and graduate students. The project will support two full-time graduate students and about two undergraduate students per year, for three years. In addition, new knowledge created during this project will be integrated into the PIs' graduate courses in Estimation Theory, Channel Coding and Digital Communications.

传统的单轨磁性磁盘存储技术已达到其密度极限。为了延长二维（2D）存储技术的指数增强存储密度的历史趋势，其中正在编写和/或在2D块中编写和/或读取，这是由行业开发的。由于读/写入系统的低通信性质，这些2D存储系统遭受了2D隔膜间干扰（ISI）。 2D-ISI可以建模为输入块的二维卷积，具有有限的扩展2D模糊函数或“掩码”，其次是添加噪声。众所周知的Viterbi算法（VA）提供了最大的可能性序列序列（MLSE），以检测1D序列在1D ISI通道上的1D序列。两个（或更高）维度的问题要困难得多，部分原因是2D中缺乏自然顺序，而不是1D。相对而言，二维问题的理解不高，并且已知方法的性能并不令人满意。 在最近的出版物中，我们根据对损坏的2D数据的锯齿形扫描的2D ISI均衡算法描述，这些算法的表现大大优于所有先前发表的工作，并且非常接近2D-MLSE BOND，理论上最佳可获得的可获得性能的最小误差率是给定的信号到noise（noise noise noise（SNR）的最小位错误率）。此外，我们已经证明，ManyData文件中存在的2D相关性可以由2D均衡器利用，以实现进一步的性能提高。为了建立这些令人鼓舞的初步结果，我们建议为独立和相关的数据提供有效设计和优化两个和更高维的ISI均衡算法的理论框架，并具有二进制和非二元和非二进制（“ M- ARY”）符号。拟议的符号均采用独立和相关的BIROLED BIROLED BIROTER MOLEDICENIFIAL BIRORDIEN和MLSE的方法；通过基于涡轮原理设计，分析和演示迭代算法来解决问题，这是从迭代涡轮解码算法借用的概念，该算法彻底改变了通道编码领域。 PI有许多有希望的初步结果，可以指出其他有希望的调查领域；这些初步结果包括：2D二进制数据中的2D-ISI降低的迭代行列软性反馈算法，在高Snr.a Zig-Zag 2d ISI ISI均衡算法上，与以前的算法相交的是，该算法的最佳算法优于先前发表的最佳先前发表的结果。 high SNR, and comes within 0.2 dB of the 2D MLSE performance bound.An algorithm for joint Markov random field estimation and 2D ISI equalization, which achieves up to 2 dB SNR gains over 2D ISI equalization alone, when the original 2D binary source is correlated.The proposed iterative algorithms use both row-column and zig-zag maximum-a-posteriori (MAP) detectors which exchange soft与先前提出的行柱迭代算法相比，估计值可显着改善数据估计。对于2D ISI和3D ISI，以及相关和非相关的源数据，将探索在迭代算法中添加其他扫描订单的好处。迭代检测将与低密度平均检查（LDPC）代码的迭代解码结合使用，以在2D和3D ISI中执行关节解码和检测。将研究新的复杂性技术以处理具有M-ARY符号的来源的多维ISI。该提案的影响力影响：拟议的项目解决了多维ISI中解码和检测的问题。因此，它结合了该项目共同研究的两个相关但独特的专业知识领域的技术：图像处理（Sivakumar）和Communications（Belzer）。这在两个字段之间产生了问题配方和已知溶液技术之间的良好协同作用。拟议的研究将为多维ISI均衡均衡问题生成一类ML解决方案的迭代算法，从而显着提高存储密度和数据速率，以实现磁性和光学存储。该项目还将受益于全息储存的新兴技术，其中激光器用于将钻头存储在2D页的堆栈中，从而导致页面内2D ISI，并且在较高密度的3D ISI上，由于页间干扰。该项目将导致2D和3D存储通道的错误控制编码的进步，从而进一步增加存储密度。最后，我们预计，我们建议为M-ARY多维ISI渠道开发的新型复杂性技术也将用于1D ISI渠道，这些渠道发生在各种电信应用中。该项目的教育影响是该项目的教育影响是招聘和培训不足的学生和研究生和研究生。该项目将支持两名全日制研究生，每年约有两名本科生三年。此外，该项目期间创建的新知识将在估计理论，渠道编码和数字通信中纳入PIS的研究生课程。