Blind Estimation Based On Higher Order Statistics For MIMO System

MIMO (multiple-input multiple-output) technology means using multiple antennaes at both the transmitter and receiver side in communication systems. As early as 1995, Foschini and Telatar coming from Bell Labs had proposed a multi-antenna channel capacity theory, that theory: if the antennas are independent of each other, the channel capacity of MIMO system will linearly growth with the increasing number of transceiver and receiver antennas.The theory breaked the traditional single antenna Shannon's channel capacity constraints.This theoretical breakthrough has demonstrated the tremendous theoretical channel capacity of MIMO system. MIMO technology can increase the capacity and utilization of the spectrum without increasing bandwidth of the communication system.Under the condition that spectrum resources becomes increasingly tense, MIMO technology is regarded as one of the key technologies of the next fourth generation system and becomes a hotspot in the field of wireless communications. the present study of MIMO technology was focused in the following directions: space-time coding, how to obtain and use channel status information (CSI), MIMO channel model, MIMO channel capacity, channel estimation.In wireless MIMO systems, receiver need to introduce an equalizer to eliminate the ISI (inter-symbol interference) because of the existence of multipath, and equalization must be carried out before learnning channel characteristics through channel estimation. In addition, it is impossible to achieve great theoretical channel capacity of MIMO system, practical capacity that system can achieve depends on the understanding and tracking of the channel information. Therefore, accurate channel estimation is crucial to ensure the quality of MIMO system.This study involved channel model and channel capacity of MIMO system, the focus task is reaserching on blind channel estimation techniques of MIMO systems, the principal work are as follows:1.Some characteristics of the wireless fading channel is studied. Mainly focuses on three groups of wireless multipath channel dispersion parameter :the time dispersion (delay proliferation, the relevant bandwidth), frequency dispersion (Doppler expansion ,the relevant time), the dispersion angle (angle expansion Related distance) and their effects on wireless multipath channel. Established a correlation MIMO channel model,the corresponding computer simulation results show that if the distance between antennas is fixed, the correlation coefficient decreases with the increase in angle expansion ; for a certain angle expansion , the correlation is becoming stronger with the decrease in distance between antennas.2.Discuss channel capacity of MIMO system. The computer simulation results show that multi-input multi-output system can largely increace channel capacity. In addition, analysis the impact on MIMO system capacity in view of the number of antennas, SNR, channel state information and the correlation between antennas. The simulation results show that, MIMO system capacity growth with the increase in SNR and the number of antennas . If the the transmitter side knows the CSI, an adaptive transmission power distribution caccording to situation of each antenna can be used to improve system capacity; if the correlation coefficient is smaller (such as 0.2), the channel capacity will not caused much damage, but for large correlation coefficient (0.9), channel capacity decreased significantly.So when designing MIMO systems, it is necessary and important to take measures to reduce the impact of all correlations so as to enhance the MIMO system capacity.3.Compared to the traditional technology based on training sequence,blind estimation technology has tremendous advantages.In this paper,various of MIMO systems blind estimation algorithm has been summarized and compared for further study. Research on relevant knowledge of higher-order statistics, theoretically proved the advantage of using higher-order statistics in MIMO systems blind estimation algorithm. Using second-order statistics can only get the magnitude of system, but the presence of higher-order statistics can also get the phase information of system, and can identify minimum phase systems and non-minimum phase systems, and also can inhibit the Gaussian noise.4. Study the pre-whittening and bispectrum estimation methods, Research blind MIMO system estimation algorithm based on EVD (eigenvalue decomposition) of second-order statistics, and computer simulation results are given. This algorithm is a relatively simple and need less calculation, but highly restrictive conditions restricted and doesn't contain phase information of the system, In addition,it can not distinguish between non-minimum phase systems.5.Study the bispectrum estimation methods, theoretically proved Optimal window has the smallest estimated MSE (mean square error) compared to all the other window functions. Then, combined a certain 2D window function constructed using Optimal window with a frequency domain blind channel estimation algorithm based on higher-order statistics to estimate channel.. Computer simulation results show that compared to the algorithm in the literature[40], blind channel estimation algorithm based on the Optimal window function can improve channel estimation ONMSE in the same data length and SNR conditions, this improvement is relatively obvious in lower data length,2 dB can be achieved.In another word, blind channel estimation algorithm based on the Optimal window function can achieve better channel estimation results with less data sequence length. This mathod proved to be effective. In addition, some computer simulation are given to study the performance and the scope of application of this method. Simulation results show that, channel ONMSE decreases gradually with the increase in observational data length and SNR;with the same zero-point, although the smallest phase, the largest phase and non-minimum phase FIR system have same magnitude response. the method in this paper can identify them out because higher-order statistics contains the phase information of system.6.Based on former work ,proposed the direction of reserch in the further: Blind channel estimation algorithm based on Optimal window function and HOS is complexity higher and show poor performance when estimating phase, how to improve the drawbacks; The method in this paper is applicable to the situation that the receiver antenna number is larger than the n transmitter side(that is, over-determined).If it is under-determined, how the algorithm should be modified and expanded; the current study focused on the FIR system, but in reality, Modeling the communication system into a FIR one is not always justified, so channel estimation of IIR system can be studied in the following work.