Research On Channel Estimation Algorithm Of OFDM System In High-speed Mobile Scenario

Orthogonal Frequency Division Multiplexing(OFDM)is one of multi-carrier modulation technology,which has strong resistance to noise,anti-multipath effects,and can improve the spectrum efficiency of the system.However,with the continuous evolution of the Internet of vehicles,unmanned aerial vehicle and high-speed railway,people's demand for communication in high-speed mobile scenarios is gradually increasing,and at the same time,there are higher requirements for communication quality.The continuous increase in the operating of highspeed transportation vehicles such as high-speed railway and aviation system leads to the increase of Doppler frequency shift,which will lead to the reduction of the accuracy of channel estimation algorithm,thus directly affecting the overall performance of OFDM system and bringing severe challenges to the existing wireless mobile communication system.Therefore,this thesis studies the channel estimation algorithm for OFDM systems under high-speed mobile scenarios.The main contents are as follows:(1)In high-speed mobile scenarios,the double-selective fading and non-stationary characteristics of wireless channels in time/frequency domain make it difficult for traditional channel estimation algorithms to accurately track channel changes.Therefore,this thesis combines Basis Expansion Models(BEM)and Extended Kalman Filter(EKF)technology,and proposes a channel estimation algorithm based on Iterative Extended Kalman Filter(IEKF).In this algorithm,EKF is used to estimate BEM basis coefficients,and state transition equations are constructed to track non-stationary channel changes;In order to reduce the impact of errors caused by approximate linearization of EKF,the state estimation value in the update phase is iterated,and the error threshold is set to determine the conditions for stop iteration.Meanwhile,Levenberg-Marquardt algorithm is used to optimize the iteration process,which can effectively reduce the linearization error and improve the accuracy of channel estimation.The simulation results confirm that compared with the existing EKF channel estimation and EKF-RTSS channel estimation methods,the proposed algorithm has better normalized mean square error performance and system robustness under the same signal noise ratio.(2)The non-Gaussian noise caused by high-speed movement will deteriorate the performance of the receiver,which in turn causes the performance of the channel estimation algorithm to drop significantly.Although the traditional Particle Filter(PF)technique can deal with the non-Gaussian noise matter,it has the phenomenon of Particle dilution.Therefore,an improved PF channel estimation algorithm based on particle swarm optimization is proposed.Firstly,the particle swarm optimization(PSO)algorithm is used to alternative the resampling step to move the particles toward the high-likelihood area and solve the particle depletion problem.Secondly,an adaptive factor is added to the particle swarm inertia weight updating,and the particle position updating is adjusted adaptively to enhance the global search ability.Finally,local randomization was used to update the position of particles to maintain the diversity of particle swarm samples,effectively reduce the PF tracking error and improve the accuracy of channel estimation.The simulation results show that in non-Gaussian noise environment and Gaussian noise environment,the proposed algorithm can obtain better normalized mean square error performance than the existing PF channel estimation and EKF channel estimation algorithms.In summary,this thesis proposes two channel estimation algorithms for the time-varying characteristics of channels in high-speed mobile scenarios and the problem that the nonGaussian noise environment faces the degradation of channel estimation algorithm performance.Two channel estimation algorithms are proposed,which can effectively track time-varying conditions in high-speed mobile scenarios.Channel changes,improve the accuracy of channel estimation,have good robustness to environmental noise,and guarantee the link performance of the OFDM system in high-speed mobile scenarios,which has certain research and application value.