Advanced decoding of constrained sequence coded signals

受限序列编码信号的高级解码

• 批准号: RGPIN-2020-04399
• 负责人: Fair, Ivan
• 金额: $ 2.04万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717014
• 关键词: Advanced; decoding; constrained; sequence; coded

Introduction: Coding is crucial in digital transmission and recording systems in order to ensure accurate communication as throughput, transmission speed, and storage densities increase. Source coding removes redundancy from the data, error control coding enables correction of errors, and constrained sequence (CS) coding helps prevent errors from occurring by avoiding the use of sequences that are prone to distortion on a particular channel. All three forms of coding are ubiquitous in today's high-performance digital communication systems. Objectives: By constraining the form of sequences that can be communicated over the channel, CS codes such as balanced codes, runlength limited codes, and codes that preclude the use of sequences most susceptible to intersymbol interference introduce properties into encoded sequences that improve the performance of the overall system. However, few of the CS decoders developed to date take advantage of the code properties to also intelligently assess the accuracy of the received sequence, instead deferring all error control measures to the error control code. Our initial work investigating advanced decoding techniques for CS codes has shown that it is possible to exploit known characteristics of the CS code in order to increase the accuracy of decoding. The research proposed in this application will extend our initial work to integrate improved sequence detection techniques into CS decoding and therefore provide more accurate data to the error control decoder, resulting in overall increased system performance. Novelty: Our initial investigations have explored two very different approaches for two very different applications: trellis-based decoding of balanced block codes based on their running digital sum characteristics, and deep learning-based decoding of block codes for visible light communications. In this research we will continue to examine the redundancy inherent in constrained sequences and develop novel approaches that will allow us to exploit this information to increase the accuracy of decoding. This research will also involve the investigation of new CS codes with properties that allow for increasing accuracy of sequence detection, and the use of iterative detection techniques in conjunction with error control decoding. Significance: As transmission speeds and storage densities skyrocket, Canada continues to play a significant role in the development of new communications technologies. This research will enable Canada to remain at the forefront of these technologies so that our economy will continue to benefit from the participation of many researchers and companies involved in this industry. Key outputs of this research will also be the creation of intellectual property to be exploited here in Canada and the education of highly qualified personnel with the skills necessary to step into this high tech industry.

简介：随着吞吐量、传输速度和存储密度的增加，为了确保准确的通信，编码在数字传输和记录系统中至关重要。 信源编码从数据中去除冗余，差错控制编码能够纠正差错，而约束序列（CS）编码通过避免使用在特定信道上易于失真的序列来帮助防止差错的发生。 所有这三种编码形式在当今的高性能数字通信系统中无处不在。 目的：通过约束可在信道上传送的序列的形式，CS码（诸如平衡码、游程长度受限码和排除使用最易受符号间干扰影响的序列的码）将改进整个系统的性能的特性引入编码序列。然而，迄今为止开发的CS解码器中很少有利用码属性来智能地评估接收序列的准确性，而不是将所有差错控制措施推迟到差错控制码。 我们的初步工作调查先进的CS码解码技术表明，它是可能的，以提高解码的准确性，利用已知的CS码的特性。 本申请中提出的研究将扩展我们的初步工作，将改进的序列检测技术集成到CS解码中，从而为错误控制解码器提供更准确的数据，从而提高系统的整体性能。 新奇：我们的初步研究针对两种非常不同的应用探索了两种非常不同的方法：基于运行数字和特性的平衡分组码的基于网格的解码，以及基于深度学习的可见光通信分组码解码。 在这项研究中，我们将继续研究约束序列中固有的冗余，并开发新的方法，使我们能够利用这些信息来提高解码的准确性。 这项研究还将涉及新的CS码的性能，允许增加序列检测的准确性的调查，并使用迭代检测技术结合错误控制解码。 重要性：随着传输速度和存储密度的飙升，加拿大继续在新通信技术的发展中发挥重要作用。 这项研究将使加拿大保持在这些技术的最前沿，使我们的经济将继续受益于许多研究人员和参与这一行业的公司的参与。 这项研究的主要成果还将是创造在加拿大利用的知识产权，以及培养具有进入这一高科技行业所需技能的高素质人才。