Research On Pilot Design And Channel Estimation Based On Compressive Sensing In OFDM Systems

The reasonable pilot design can improve the spectrum efficiency in OFDM systems.Channel estimation is an indispensable part of the receiver.Applying the theory of compressive sensing to channel estimation in OFDM systems can not only reduce the number of the using pilots,but also improve the performance of channel estimation.This thesis mainly focuses on pilot design and channel estimation in OFDM systems with the theory of compressive sensing.Firstly,a pilot design scheme based on the binary particle swarm optimization algorithm is proposed to solve the problem of too much pilot overhead and too low spectrum efficiency.According to the mutual incoherence property,the smaller the cross-correlation value of the measurement matrix,the more the sparse signal can be reconstructed with high probability.With the objective to minimize the cross correlation of the measurement matrix,the chaos initialization mechanism is introduced to ensure the homogeneous dispersion of the initial particles in the solution space,and the rapid convergence of the population can also be guaranteed by the particle mutation mechanism.Simulation results show that,compared with the random pilot design scheme,the outer and inner loop pilot design scheme and the least square channel estimation with equidistant pilot design scheme,the proposed scheme can effectively save the pilot overhead,improve the spectrum efficiency and obtain a better channel estimation performance under the same number of pilots.Secondly,an improved generalized orthogonal matching pursuit algorithm is proposed for channel estimation due to the long time of channel estimation in the orthogonal matching pursuit algorithm,the low accuracy of channel estimation in the generalized orthogonal matching pursuit algorithm,and all need a priori information with channel sparseness.The proposed algorithm bring in a regularization mechanism to make the energy of the currently selected atoms the largest,and according to the relationship between the estimated sparseness and the support set to change the step size to ensure that the signal can converge quickly and accurately.In additional,the proposed algorithm use noise power parameters to ensure the iteration stop threshold for OFDM channel estimation.Simulation results show that the proposed algorithm has a good channel estimation performance without prior information of the sparseness witha short computation time,which compared with the least square algorithm,the orthogonal matching pursuit algorithm and the generalized orthogonal matching pursuit algorithm.