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: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=739943
• 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.

前言：随着吞吐量、传输速度和存储密度的增加，编码在数字传输和记录系统中至关重要，以确保准确的通信。信源编码从数据中去除冗余，差错控制编码实现纠错，而约束序列(CS)编码通过避免使用在特定信道上容易失真的序列来帮助防止错误的发生。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：通过限制可在信道上传送的序列的形式，CS码，例如平衡码、游程长度限制码和禁止使用最容易受到符号间干扰的序列的码，将特性引入到编码的序列中，以改善整个系统的性能。然而，迄今为止开发的CS解码器中很少利用码的特性来智能地评估接收序列的准确性，而是将所有差错控制措施推迟到差错控制码。我们对CS码的高级译码技术的初步研究表明，可以利用CS码的已知特性来提高译码的精度。本申请中提出的研究将扩展我们最初的工作，将改进的序列检测技术整合到CS译码中，从而为差错控制译码提供更准确的数据，从而导致系统整体性能的提高。新颖性：我们的初步调查针对两个非常不同的应用探索了两种非常不同的方法：基于网格的平衡分组码的解码基于其运行数字和特性，以及用于可见光通信的基于深度学习的分组码解码。在这项研究中，我们将继续检查受限序列中固有的冗余，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。这项研究还将涉及具有允许增加序列检测精度的特性的新CS码的研究，以及结合差错控制译码使用迭代检测技术。意义：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前沿，使我们的经济将继续受益于许多参与这一行业的研究人员和公司。这项研究的主要成果还将是在加拿大创造可供利用的知识产权，以及培养具有进入这一高科技行业所需技能的高素质人员。