Research On High Mobility Channel Estimation Algorithm Based On Basis Expansion Model

With the rapid development of high-speed railway,traveling by high-speed train has become the first choice for many people.However,the traditional global system for mobile communications-railway(GSM-R)cannot satisfy the requirements of passengers’broadband wireless communication.To solve this problem,the long-term evolution for railway(LTE-R)system is proposed by the international railway alliance.Because of its high reliability and high data rate in the high mobility environment,it will gradually replace the GSM-R system.In the wireless communication environment of high-speed railway,the movin g speed of users is usually greater than 300km/h.High mobility will bring about severe Doppler effect and fast time-varying characteristics.Fast and effective estimation of channel state information is very important for ensuring the communication quality of users.Although researchers have obtained some research achievements of high mobility channel estimation,further improvement and optimization are still needed.Therefore,based on the existing high mobility channel estimation results,the fast time-varying channel estimation based on the basis expansion model is deeply investigated in this thesis.The main contributions are summarized as follows:(1)A fast time-varying channel estimation algorithm based on basis expansion model is investigated.By employing the basis expansion model,the fast time-varying channel of LTR-R system is fitted into the sum of basis functions multiplied by coefficients.The advantages and disadvantages of the polynomial basis expansion model,the complex exponential basis expansion model,the generalized complex exponential basis expansion model,and the optimization generalized complex exponential basis expansion model are analyzed.The key of fitting the fast time-varying channel based on basis expansion model is the solution of the basis function coefficient.Therefore,the solving process of the basis function coefficient is deduced in detail in this thesis and the fast time-varying channel estimation is achieved.Simulation results show that the optimization generalized complex exponential basis expansion model has the lowest normalized mean squared error.(2)An improved basis expansion model channel estimation method is investigated.Aiming at the problems existing in fast time-varying channel fitting based on the basis expansion model,the modified scheme is studied to improve the accuracy of the basis expansion model fitting.On the basis of the traditional baseline tilting method,an improved baseline tilting approach is proposed in this thesis to reduce the influence of the discontinuity of the beginning and the end of fast time-varying channel fitted by the basis expansion model.Thus,the Gibbs effect is further reduced.Simulation results show that the improved baseline tilting method has lower normalized mean squared error than the traditional baseline tilting approach and the single basis expansion model method.(3)The channel estimation strategy of data points based on interpolation algorithm is investigated.The advantages and disadvantages of three pilot patterns(i.e.,block pattern,comb pattern,and star patternare analyzed and the conditional expression of pilot interval is given.Three interpolation algorithms(i.e.,linear interpolation,Gauss interpolation,and three-order Hermite interpolation)are employed and their advantages and disadvantages are analyzed.The channel estimation of the non-pilot location based on the interpolation algorithm is realized.Simulation results show that the three-order Hermite interpolation algorithm has lower normalized mean squared error than the linear interpolation algorithm and the Gauss interpolation algorithm.