Wireless Transmission Mimo - Ofdm Channel Estimation Algorithm Of The Comparison And Analysis

MIMO-OFDM technology can greatly improve channel capacity, reliability, transmission rate in the wireless transmission environment, is resistant to multipath fading and restrain interference and noise, it will be a method that obtain a reliable and high-speed transmission capacity in wireless broadband communications. But, The time domain and frequency domain response of the channel is time-varying, in order to achieve data transmission of high rate and to ensure the performance of the system is not the effects of channel multipath and fading effects, it needs using multi-amplitude, multi-phase modulation of MIMO-OFDM systems to track the diversification of channel response using channel estimation method, estimate the time domain or frequency domain response of the channel, correction and recovery the received data to obtain coherent detection performance gain, simultaneously, the max multipath delay of channel, the user mobile speed, receiver noise and how to insert pilot the guide are important factors to affect the performance of channel estimation algorithm. Therefore, the accuracy of channel estimation significantly affect the performance of wireless communication systems, channel estimation technique is an important basis to obtain information, to conduct channel equalization and transfer options, is the wireless communications research focus in recent years, cause widespread attention in the communications sector. So, channel estimation algorithm has a very important meaning and value in the MIMO-OFDM system.This article first briefly describes the overview of the development of key technologies of wireless communication systems, describes the basic principles of MIMO-OFDM systems and its key technologies based on it and then do studying more in-depth focusing on this critical technology of channel estimation. First, it analyzes the BER performance of the LS algorithm, the MMSE algorithm and the LMMSE algorithm based on pilot channel estimation algorithm, and then analyzes how to improve these algorithms and compares their performance, simultaneously, it analyzes the MSE performance and SER performance of the LS. MMSE and LMMSE channel estimator in the different SNR and high-speed mobile environment. Secondly, it describes the blind channel estimation algorithm of linear preprocessing and the subspace blind channel estimation algorithm in the blind channel estimation algorithm and analyzes their advantages and disadvantages after the corresponding simulation. Finally, it describes semi-blind channel estimation algorithm by combining the advantages and disadvantages of the pilot channel estimation algorithm and blind channel estimation algorithm, and make certain improvements to the semi-blind algorithm of linear preprocessing and the semi-blind algorithm of subspace. After simulating, experimental data analyze the MSE performance and average deviation of the blind channel estimation, as well as compared and analyzed the traditional channel estimation MSE performance and semi-blind channel estimation MSE performance. It can be known that the performance of the improved algorithm has more clear improvement than the original algorithm, verifying its feasibility.