Fano Decoding Complexity And The Hidden Markov Model-based Channel Modeling And Prediction

The research on the model of wireless channel is one of the key technologies in the field of wireless communications. Compared to wave channels, discrete channel models usually adopt Markov model, which has higher computational efficiency. And because the Markov model divides the channel into different discrete states, we can choose different adaptive technology, which is also the trend in the wireless communication development. Another core problem of adaptive technology is the accurate prediction of channels.This thesis focuses on three aspects:Firstly, the relationship between the complexity of Fano sequential decoding, signal-to-noise ratio (SNR) in a mobile channel and Doppler spread in Rayleigh fading channels is disscussed. We found that when SNR is higher, the complexity of Fano sequential decoding is lower, and when Doppler spread is lower, the complexity of Fano sequential decoding is lower. So it is reasonable to use the complexity of Fano sequential decoding as a measure of the condition of channels.Secondly, Model-building of the discrete channel, using the complexity of Fano sequential decoding and HMM, is discussed. In this thesis, measured Fano decoding complexity is used as observation value, Baum-Welch algorithm is employed to obtain an optimal estimate of the HMM model parameters, and then the maximum likelihood hidden state sequence is found by using the Viterbi algorithm. After optimized Baum-Welch algorithm by Stochastic Relaxation algorithm, accuracy of the Discrete Channel Model estimate is improved and estimates accuracy rate of the channel state is over of 90 percent.Thirdly, research on the channel prediction with the state transition probability matrix of Markov model, based on discrete channel models by HMM is discussed. First, by using Sliding Window algorithm to obtain a sequence of measured values before performing Viterbi decoding, the instantaneous state of channel is achieved; Then using HMM's state transition probability matrix to predict one step and many steps of channel states. One step of prediction has achieved good results; while, because of accumulated error, the result of prediction of many steps fails.