基于软信息的信道编码方式自动识别方法研究

发布时间：2018-06-22 05:12

本文选题：软信息 + 信道编码　；参考：《中国科学技术大学》2016年硕士论文

【摘要】：在移动通信中,随着用户数目的增多,信道资源变得越来越紧张。为了缓解这一问题,引入了自适应调制编码技术,该技术根据信道质量自适应地选择调制和信道编码方式,并将相应的信息通过控制信道进行传输。但是由于噪声、干扰、延时等时变因素的存在,可能会使得控制信息不能及时、准确地到达接收端,同时频繁的控制信息传输会增大控制信道的开销。于是信道编码方式的自动识别被提出,它在已知候选信道编码方式集合的情况下,接收端仅根据接收序列的软信息识别出发送端所使用的信道编码方式。此外,在非协同的信息截获领域,侦察方并不能准确知道被侦察方所使用的信道编码方式,但可以根据一些先验信息获得对方可能使用的信道编码方式的集合。此时就可以利用信道编码方式的自动识别技术,根据截获信号解调后的软信息来进一步识别信道编码方试,从而为后续译码等工作提供支撑。由此可见,信道编码方式的自动识别对于协同工作的移动通信和非协同工作的信息截获都至关重要。本文在此应用背景下开展基于软信息的信道编码方式的自动识别方法研究,主要工作有以下两方面：1.卷积码、线性分组码和LDPC码的自动识别方法提出一种改进算法,用于卷积码、线性分组码和LDPC码的自动识别。改进算法根据接收序列的软信息,计算得到校验关系的后验概率对数似然比,并将它的加权均值作为识别特征量,通过识别特征量的最大值来确定发送端使用的信道编码方式。理论分析可知,与现有算法相比,改进算法的算法复杂度更低,并且更加易于工程实现。仿真实验表明,改进算法能在较短的检测长度下达到较高的检测概率,可以满足实际系统的应用需求。2. Turbo码的自动识别方法提出一种适用于Turbo码的自动识别算法。通过对Turbo码编码器结构的分析,该算法首先对接收序列的软信息进行解复接和解删除,获得两个分量编码器的输出序列软信息,然后对删除比特的软信息进行修补,最后利用卷积码的自动识别算法对分量编码器、删除结构和交织器进行识别,进而完成Turbo码的自动识别。仿真实验表明,该算法可以有效地实现对于Turbo码的自动识别。
[Abstract]:In mobile communications, as the number of users increases, channel resources become more and more tense. In order to alleviate this problem, adaptive modulation coding is introduced. The technology adaptively selects modulation and channel coding based on channel quality, and transfers the corresponding information through the control channel. But because of noise, interference, and delay, the technology is used to transmit the corresponding information through the control channel. The existence of time-varying factors may make the control information not timely and accurately arrive at the receiver, while frequent control of information transmission increases the overhead of the control channel. In addition, in the field of non cooperative information interception, the reconnaissance party can not accurately know the channel coding method used by the reconnaissance party, but can obtain the set of channel coding methods that the other party may use according to some prior information. Automatic recognition technology is based on the soft information after the interception of the signal to further identify the channel coding test, thus providing support for the subsequent decoding work. Thus, the automatic recognition of the channel coding mode is crucial to the information interception of the mobile communication and non cooperative work in cooperative work. This paper is carried out under this application background. Research on automatic recognition of channel coding based on soft information, the main work has two aspects as follows: 1. convolutional code, linear block code and LDPC code automatic recognition method, an improved algorithm is proposed for automatic recognition of convolutional code, linear block code and LDPC code. The improved algorithm is calculated and verified on the basis of the soft information of the receiving sequence. The posteriori probability logarithm likelihood ratio of the relation is used and its weighted mean is used as the recognition feature. The channel coding mode used by the transmitter is determined by identifying the maximum value of the characteristic quantity. The theoretical analysis shows that the improved algorithm is more complex and easier to implement than the existing algorithm. The simulation experiment shows that the improved algorithm is improved. The method can reach a higher detection probability under the short detection length, and can satisfy the application requirement of the actual system. The automatic recognition method of.2. Turbo code is proposed. By analyzing the structure of the Turbo code encoder, the algorithm first solves the soft information of the received sequence and removes and deletes the Turbo code. The soft information of the output sequence of the two component encoders is obtained, then the soft information of the deleted bits is mended. Finally, the automatic recognition algorithm of the convolutional code is used to recognize the component encoder, the deletion structure and the interleaver, and then the Turbo code is automatically recognized. The simulation experiment shows that the algorithm can effectively implement the Turbo code self. Dynamic identification.
【学位授予单位】：中国科学技术大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TN911.22

【相似文献】