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.

传统的单磁道磁盘和光盘存储技术已达到其密度极限。为了继续指数地增加存储密度的历史趋势，工业界正在开发二维（2D）存储技术，其中在2D块中写入和/或读取位。由于读/写系统的低通特性，这些2D存储系统遭受2D符号间干扰（ISI）。2D-ISI可以被建模为输入块与有限范围的2D模糊函数或“掩模”的二维卷积，随后是加性噪声。二维（或更高）的问题要困难得多，部分原因是2D与1D相比缺乏自然顺序。相对而言，2D问题没有被很好地理解，并且已知方法的性能不太令人满意。 在最近的出版物中，我们描述了2D ISI均衡算法，基于损坏的2D数据的Z字形扫描，其性能大大优于所有先前发表的工作，并且非常接近2D-MLSE界限，对于给定的信噪比（SNR），在最小比特错误率方面理论上可达到的最佳性能。此外，我们已经证明，2D相关性存在于manydata文件可以利用的2D均衡器，以实现更进一步的性能增益。为了建立在这些有希望的初步结果，我们提出了一个有效的设计和优化的二维和高维ISI均衡算法的理论框架，为独立和相关的数据，与二进制和非二进制（“M元”）symbols.The拟议的研究采用了统一的方法MLSE为独立和相关的二进制和M元多维数据;通过设计、分析和演示基于turbo原理的迭代算法来解决问题，turbo原理是从迭代turbo解码算法中借用的概念，该迭代turbo解码算法已经彻底改变了信道编码领域。PI有一些有希望的初步结果，这些结果有助于指出其他有希望的研究领域;这些初步结果包括：一种用于2D二进制数据中的2D-ISI降低的迭代行-列软判决反馈算法，其在高SNR下比先前公布的最佳结果好约0.4dB;一种锯齿形2D ISI均衡算法，当与行-列算法级联时，其在高SNR下比先前公布的最佳结果好约0.7dB，并且在2D MLSE性能界限的0.2dB以内;一种用于联合马尔可夫随机场估计和2D ISI均衡的算法，提出的迭代算法同时使用行列和Z字形最大后验概率（MAP）检测器交换软估计，与以前提出的行列迭代算法相比，数据估计得到了显著改善。将探讨在迭代算法中添加额外扫描顺序的好处，包括2D和3D ISI以及相关和非相关源数据。迭代检测将与低密度奇偶校验码（LDPC）的迭代解码相结合，以在2D和3D ISI中执行联合解码和检测。新的复杂性降低技术将被investigatedto处理多维ISI的源与M-ary symbols.Broader影响的建议：建议的项目解决的问题，在多维ISI的解码和检测。因此，它结合了项目共同PI的两个相关但不同的专业领域的技术：图像处理（Sivakumar）和通信（Belzer）。这产生了一个很好的协同作用之间的问题配方和已知的解决方案技术之间的两个field.The建议的研究将产生一类迭代算法的ML解决方案的多维ISI均衡问题，从而显着提高存储密度和数据速率的磁和光存储。该项目还将有利于全息存储的新兴技术，其中激光用于在2D页面的堆栈中存储比特，导致页内2D ISI，并且在更高的密度下，由于页间干扰而导致3D ISI。该项目将推动2D和3D存储通道的错误控制编码，从而进一步提高存储密度。最后，我们预计，我们提出的新的复杂性降低技术开发的M元多维ISI信道也将使用1D ISI信道，这发生在各种各样的电信application.The教育的影响，这个项目将是本科生和研究生的招聘和培训。该项目将支持两名全日制研究生和大约两名本科生，每年三年。此外，在这个项目中创造的新知识将被整合到PI的估计理论，信道编码和数字通信的研究生课程中。