Maximum likelihood channel estimation algorithm combines logarithm likelihood ratio decoding in the coal mine application

The environment of the wireless communication system in the coal mine has unique characteristics： great noise, strong multiple path interference, and the wireless communication of orthogonal frequency division multiplexing （OFDM） in underground coal mine is sensitive to the frequency selection of multiple path fading channel, whose decoding is separated from the traditional channel estimation algorithm. In order to increase its accuracy and reliability, a new iterating channel estimation algorithm combining the logarithm likelihood ratio （LLR） decode iterate based on the maximum likelihood estimation （ML） is proposed in this paper, which estimates iteration channel in combination with LLR decode. Without estimating the channel noise power, it exchanges the information between the ML channel estimation and the LLR decode using the feedback information of LLR decode. The decoding speed is very quick, and the satisfied result will be obtained by iterating in some time. The simulation results of the shortwave broadband channel in the coal mine show that the error rate of the system is basically convergent after the iteration in two times.

Some Strong Deviation Theorems for Dependent Continuous Random Sequence

Let {Xn,n ≥ 1} be a sequence of arbitrary continuous random variables,we introduce the notion of limit asymptotic logarithm likelihood ratio r（ω）,as a measure of dissimilarity between probability measure P and reference measure Q.We get some strong deviation theorems for the partial sums of arbitrary continuous random variables under Chung-Teicher＇s type conditions[6-7].

A Class of Random Approximation Theorems for Random Sums of Arbitrary Stochastic Sequence on Nonhomogeneous Markov Chains

In this paper, the notion of limit random logarithmic likelihood ratio of stochastic sequence, as a measure of dissimilarity between the joint distribution on measure P and the Markov distribution on measure Q, is introduced. A class of random approximation theorems for arbitrary stochastic dominated sequence are obtained by using the tools of generating functions and the tailed-probability generating functions.

Performance analysis of complexity reduction BP decoding of rateless codes by deleting low reliable symbols

This paper investigates the performance of the method used to reduce the decoding complexity of rateless codes through the deletion of the received symbols with low reliability. In the decoder, the received symbols whose absolute value of logarithm likelihood ratio （LLR） is lower than the threshold are removed, together with their corresponding edges, and thus not involved in the decoding process. The relationship between the deletion probability and the likelihood ratio deletion threshold is derived. The average mutual information per received symbol is analyzed in the case of deletion. The required number of symbols for the decoder to keep the same performance as regular decoding decreases since the average mutual information per symbol increases with the deletion, thus reducing the decoding complexity. This paper analyzes the reduction of decoding computations and the consequent transmission efficiency loss from the perspective of mutual information. The simulation results of decoding performance are consistent with those of the theoretical analysis, which show that the method can effectively reduce the decoding complexity at the cost of a slight loss of transmission efficiency.