【作者】 蔡细明；

【导师】 宋正勋； 胡贞；

【作者基本信息】 长春理工大学 ， 通信与信息系统， 2006， 硕士

【摘要】 为了研究能适应环境变化的无线传输系统，提高无线通信系统的性能，就需要用可编程数字电路来取代传统电路，采用通常所说的软件无线电(SDR)的概念。本论文采用隐马尔可夫模型(HMM)来减小解码与解调选择错误。为了获得更好的系统性能，本系统采用极大似然编码与调制转换方案，通过CSI获得的编码与调制转换概率以及基于解码与解调选择错误概率来选择基于HMM的调制方案。论文提出了两种通过增加编码间最小距离来减小DSE的方法，即利用网格编码估计补偿和基于LDPC编码与和-积解码算法的盲调制识别技术。并着重利用对调制方案识别很重要的相位误差补偿技术。更多还原

【Abstract】 In order to achieve high performance in wireless communication systems, it is necessary to consider a radio transceiver system able to adapt to changes in the environment. This calls for the replacement of the conventional circuit by a programmable digital circuit. This is known as Software Defined Radio (SDR). Decreases decoder and demodulator selection error using a Hidden-Markov Model (HMM) in this paper. In order to estimate the switching patterns of the encoder and modulator, our proposed system selects the maximum likelihood encoder and modulator transition patterns. The selection uses both encoder and modulator transition probability based on the HMM obtained by CSI as well as Decoder and Demodulator Selection Error probabilities .We propose two methods for reducing DSE by increasing the minimum distance between two encoders. One proposes a trellis encoder estimation scheme that compensates for this problem. The other method consists of a blind modulation identification technique for an adaptive coded modulation scheme based on LDPC codes and the Sum-Product decoding algorithm. Attention is paid to the phase error-correcting technique using the decoding result, which is critical for the modulation scheme identification.更多还原