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要素之一。 传统上，好的纠错码的设计假设无记忆信道。 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展：这些码的迭代解码使得有可能实现接近无记忆信道的理论极限的性能。 然而，在大多数应用中，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更现实的信道，进行迭代译码的研究。 目标是实现可靠的通信，接近理论极限，这些类型的渠道。 这将有一个直接的应用在现实的通信系统（包括无线通信）的设计，允许在给定的服务质量的发射机功率要求的减少，并更好地利用可用的bandwidth.In为了实现最佳可能的性能时，信道有记忆，信道的统计特性必须在解码过程中被利用。 这将在一个双重的过程中完成：第一，统计模型，如隐马尔可夫模型和随机文法，迭代解码方案将被修改，以将统计模型的Turbo码，LDPC码，级联空时码的解码。 这两个步骤完全交织在一起。 其思想是联合设计的统计模型和解码修改，以解码性能为真实的信道作为真实的信道作为优化标准。 此外，在可能的情况下，该过程应该自适应地工作，而不需要信道的先验知识：当在未知信道中使用通信系统时，应该与解码（如果可能的话，以完全盲的方式或通过使用导频）联合地获得信道的方便的统计模型。 在每次迭代中，这样的模型应该用于解码并且被方便地细化。

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.