Channel Estimation For Fast Fading Mimo Ofdm Systems In High Mobility Communications

The fast fading channel estimation method is proposed for high-speed mobile MIMO-OFDM system. Firstly, the basic theory and commonly channel estimation methods are discussed. Secondly, the wireless communications fading channel characteristics are introduced. Fast fading channel characteristics were analyzed in time, frequency and spatial domain. The inter-symbol interference of the OFDM system is caused by the different multipath signal delay. The inter-carrier interference of OFDM system is caused by the Doppler frequency shift. The orthogonality between subcarriers of is destructed. In the MIMO system, the interference between antennas in the spatial domain is formed. The sparse transform coding measurement, reconstruction algorithms and distributed compressed sensing of the compressed sensing theory are analyzed. The feasibility of the compressed sensing-based channel estimation is analyzed. Finally, compressed sensing-based channel estimation algorithm is proposed for OFDM system, MIMO-OFDM system and distributed MIMO-OFDM. The work and innovation are completed in the following:The existing problem of sparse channel model is analyzed. The parameter fast fading channels model is derived without aliasing and leakage power, and has more sparse and low computational complexity. A delay Doppler sparse channel model is proposed for OFDM systems. An angle delay Doppler sparse channel model is proposed for MIMO-OFDM system. The channe is measured by a perceived random matrix of pilot. The fast fading channel is reconstructed with a high probability. The system spectrum efficiency is improved.The sparse time-frequency groups channel estimation algorithm based on compressed sensing is for OFDM systems. Using the time domain correlation, the energy is concentrated in a small number of sampling points. Using the frequency domain correlation, the energy is concentrated in only a few points on the Doppler frequency shift. Therefore, the complexity of proposed algorithm than the existing algorithms based on time-frequency two-dimensional is lower. The performance of the proposed algorithm is improved and the estimated delay is reduced. The sparse time-frequency groups channel estimation algorithm based on compressed sensing is for MIMO-OFDM systems. The correlation of different airspace antenna channel sparse coefficient and the same antenna channel sparse coefficient is analyzed and used. The complexity of proposed algorithm is lower. The performance of the proposed algorithm is improved and the estimated delay is reduced.The sparsity of fast fading channel impulse responses is unknown. A sparsity adaptive estimation of fast fading channels based on compressed sensing is given. The sparsity adaptive estimation enjoys a potentially higher sparsity level from transmit-receive antennas and multi-symbol processing. The multi-antenna structure and sparse time-frequency basis is constructed. The time-varying channel impulse responses within the multiple antennas and group OFDM symbols have a more sparse nature. The fast fading channels are estimated by a sparsity adaptive compressive sensing technique without prior information of the sparsity. The simulation results show that the new channel estimator can provide a considerable performance improvement in estimating fast fading channels.Joint sparse models between the antenna nodes can be exploited with spatial-delay-Doppler correlation. The sparse channel impulse response is compressed and reconstructed in an energy efficient manner based on distributed compressed sensing algorithm. The simulation results show that the new channel estimator can provide a considerable performance improvement in estimating fast fading channels, when the significant reduction is achieved in the required number of pilots and computational complexity.