Frequency Hopping Systems In The Channel Quality Assessment Algorithm

In recent years, with the rapid development of wireless communication technology, adaptive frequency hopping(AFH) technology which is built on the basis of conventional hopping has become one of military communication hot spots. It is based on automatic link quality evaluation connects the adaptive frequency control with adaptive power control perfectly , which greatly enhances the capability of anti-fading, anti-jamming and anti-intercept. Since updating the frequency hopping set of sending and receiving sides in adaptive frequency control needs to judge the frequency hopping first, and increasing or decreasing the power in adaptive power control needs to judge the frequency hopping firstly, so we must have real-time channel quality evaluation by the received data in the receiver.The second chapter briefly introduces the basic principle and the mathematical model of frequency hopping communication systems. Next, we explain a number of parameters involved by the algorithm. Finally, we describe the basic principle and the mathematical model of adaptive frequency hopping communication system.The third chapter introduces main theoretical basis involved by the signal-to-noise ratio(SNR) estimators in the channel quality evaluation algorithm. Then we mainly study on three common SNR estimators in detail: high-order cumulants estimator, data fitting estimator and eigenvalue decomposition of signal auto-correlation matrix estimator. The results of simulation illustrate that the performance of the eigenvalue decomposition of signal auto-correlation matrix estimator is the best and most accurate in the above three algorithms.The fourth chapter intensively study eigenvalue decomposition of receiving signal auto-correlation matrix estimator and the details of realization firstly, such as how to calculate the auto-correlation matrix, how to do the eigenvalue decomposition and how to determine the dimension of signal subspace. Following by, we propose the possible problems in the algorithm, make corresponding improvements on these deficiencies. We put forward to the AFH model based on algorithm and simulate. Next, we work out the the eigenvalue decomposition of receiving signal improved auto-correlation matrix estimator ovcoming the deficiencies of the eigenvalue decomposition of receiving signal auto-correlation matrix estimator and the simulation results show that the algorithm is indeed better than the auto-correlation.The fourth chapter and the fifth chapter study on adaptative frequency hopping channel quality evaluation algorithm based on receiving signal-to-noise ratio prediction which is divided into three parts: SNR estimation, SNR prediction and the comparison with threshold. The fifth chapter studies on the system model, assessment principles and implementation on kalman prediction and simulate the algorithm. The results indicate that the kalman prediction error is relatively small in certain range of SNR. Besides, we elaborate the determination of threshold based on the performance analysis of BER and draw the overall flow chart about channel quality evaluation algorithm.Finally, the paper summarizes the whole work and gives the direction for the further research.