基于贝叶斯序贯推理的自适应调制识别算法

提出了一种时变衰落信道下的自适应调制识别算法,设计出一种新的动态状态空间模型,来刻画信号调制方式与时变信道增益的时变特性,并引入一阶有限状态马尔可夫(finite state Markov channel,FSMC)模型来描述衰落信道;基于上述,新算法采用贝叶斯序贯推理法,充分发掘利用了其中所隐含的信道动态相关特性,实现对调制方式和时变信道增益的联合估计。仿真结果表明,新算法性能相比于传统ALRT算法有极大提升,且增加采样点数或者降低多普勒频移值都会使算法性能得到改善。

Sequential Bayesian inference based adaptive modulation recognition algorithm

Under the time-varying fading channel, an adaptive modulation recognition algorithm is presented. A new dynamic state space model is designed to describe time varying characteristics of modulation schemes and channel gain. A first-order finite state Markov channel （FSMC） model is introduced for the fading channel. On this basis, a new algorithm, which adopts the sequential Bayesian inference method and is proposed to fully exploit the dynamic transfer characteristics of the hidden channel state, achieves joint estimation of modulation and time-varying channel gain. The simulation results prove that performance of the algorithm compared to traditional ALRT algorithms greatly improves, and increasing the number of sampling points or reducing the Doppler shift value can make the performance better.