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

纠错码（信道码）是优化数字通信系统的关键因素之一。传统上，良好的纠错码设计假定无记忆信道。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的两个最重要的进步：这些代码的迭代解码使得实现接近无内存信道的理论极限的性能成为可能。然而，在大多数应用程序中，通道并不是那么简单。本研究主要针对具有记忆性的无线通信信道等较为现实的信道进行迭代解码的研究。目标是为这些类型的信道实现接近理论极限的可靠通信。这将直接应用于实际通信系统（包括无线通信）的设计，从而减少对给定服务质量的发射机功率要求，并更好地利用可用带宽。为了在信道有内存的情况下获得尽可能好的性能，必须在解码过程中充分利用信道的统计特性。这将通过两个过程来完成：首先，统计模型，如隐马尔可夫模型和随机语法，迭代解码方案将被修改，以将统计模型纳入turbo码，LDPC码和连接空时码的解码中。这两个步骤完全交织在一起。其思想是以真实信道的译码性能为优化标准，联合设计统计模型和译码修改。此外，在可能的情况下，这个过程应该自适应地工作，而不需要先验的信道知识：当通信系统在未知信道中使用时，应该与解码一起获得信道的方便统计模型（如果可能的话，以完全盲的方式或使用导频）。在每次迭代中，应该使用这样的模型进行解码，并方便地进行细化。

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.