CAREER: Iterative Decoding Schemes For Channels With Memory: Application To Fading Channels

职业：具有记忆的信道的迭代解码方案：在衰落信道中的应用

• 批准号: 0093215
• 负责人: Javier Garcia-Frias
• 金额: --
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-02-15 至 2008-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0093215&HistoricalAwards=false
• 关键词: CAREER; Iterative; Decoding; Schemes; Channels

Error correcting codes (channel codes) are on e of the key elements to optimize digital communications systems. Traditionally, the design of good error correcting codes assumes memoryless channels. In this context, turbo codes and the rediscovery of low-density parity check (LDPC) codes represent two of the most significant advances in channel coding in recent years: Iterative decoding of these codes makes it possible to achieve performance close to the theoretical limits for memoryless channels. However, in most of the applications, the channel is not so simple. This research focuses on the study of iterative decoding for more realistic channels, such as wireless communications channels, characterized by having memory. The objective is to achieve reliable communications, close to theoretical limits, for these types of channels. This will have a direct application in the design of realistic communications systems (including wireless communications), allowing a reduction in the transmitter power requirements for a given quality of service, and a better use of the available bandwidth.In order to achieve the best possible performance when the channel has memory, the statistical properties of the channel must be exploited in the decoding process. This will be accomplished in a two-fold process: First, statistical models, such as hidden Markov models and stochastic grammars, the iterative decoding schemes will be modified to incorporate the statistical models in the decoding of turbo codes, LDPC codes, and concatenated space-time codes. Both steps are completely interwined. The idea is to jointly design the statistical models and the decoding modifications, taking the decoding performance for the real channel as the real channel as the optimization criterion. Moreover, when possible, this process should work adaptively, with no a priori knowledge of the channel required: when the communications system is used in an unknown channel, a convenient statistical model of the channel should be obtained jointly with decoding (in either a completely blind fashion if possible or by using pilots). In every iteration such a model should be used for the decoding and be conveniently refined.

纠错码（信道码）是优化数字通信系统的关键要素之一。 传统上，良好的纠错码的设计假设无记忆通道。 在这种情况下，Turbo 码和低密度奇偶校验 (LDPC) 码的重新发现代表了近年来信道编码领域的两个最重要的进步：这些代码的迭代解码使得实现接近理论极限的性能成为可能。无记忆频道。 然而，在大多数应用中，通道并不是那么简单。 本研究重点研究更现实的信道的迭代解码，例如具有记忆性的无线通信信道。 目标是为这些类型的通道实现接近理论极限的可靠通信。 这将直接应用于现实通信系统（包括无线通信）的设计，允许降低给定服务质量的发射机功率要求，并更好地利用可用带宽。当通道具有内存时，必须在解码过程中利用通道的统计特性。 这将通过两个过程来完成：首先，统计模型，例如隐马尔可夫模型和随机语法，将修改迭代解码方案以将统计模型合并到turbo码、LDPC码和级联空间的解码中-时间代码。 这两个步骤是完全交织在一起的。 其思想是联合设计统计模型和解码修改，以真实通道的解码性能作为优化标准。 此外，在可能的情况下，该过程应该自适应地工作，无需先验了解所需的信道：当通信系统在未知信道中使用时，应该与解码联合获得信道的方便的统计模型（在完全解码的情况下）。如果可能的话，盲目时尚或通过使用飞行员）。 在每次迭代中，应该使用这样的模型进行解码并方便地进行改进。