Blind Channel Estimation And Equalization Based On ICA In MIMO-OFDM System

In the future, the target of broad band wireless communication is to transmit data fast and reliably, while there are two main tough problems in front of us, they are how to eliminate multipath fading and increase bandwidth efficiency. MIMO technology can take advantage of the spatial diversity obtained by spatially separated antennas, so it can improve channel capacity gain without increasing additional spectrum resource and antenna transmission power. OFDM is an especial case of Multi-carrier transmission, where a single datastream is transmitted over a number of lower rate subcarrier. It is worth mention here that OFDM can be seen as either a modulation technique or a Multiplexing technique. One of the main reasons to use OFDM is to increase the robustness against frequency selective fading and bandwidth efficiency. The combination of the two powerful techniques, MIMO and OFDM, has received considerable attention over the last decade for its promising capability to combat the multipath fading and boost the system capacity, it also appears as a promising candidate for the coming fourth generation (4G) wireless communications.In mobile radio communications,the transmitted signal undergoes two main effects. One is the arrival of multiple replicas of the signal interfering with the desired signal,which is known as multipath propagation. The other is the shift in frequency of the signal due to the relative motion between the transmitter and receiver,which is described by Doppler effect. These two effects will introduce inter-symbol interfenrence (ISI) to the received signal, generating great error in symbol detection. For OFDM system, Guard period is introduced to each symbol to eliminate ISI, but there will be another problem which is inter-carrier interference (ICI) also caused by multipath. To achieve high-speed reliable communication, channel identification and time equalization are necessary to overcome the effects of ICI, which is also the basis for coherent detection and demodulation. In MIMO system, multiple access interference (MAI) is added to the received signal, which can be tackled by space equalization. Hence, space-time equalization is necessary based on channel estimation for MIMO-OFDM system.The channel estimation methods can be divided into three categories, they are non-blind channel estimation, blind channel estimation and semi-blind channel estimation. The non-blind channel estimation based on pilot symbols or trained sequence has good capability and is easy to realize, but it decreases bandwidth and transmission efficiency. The blind method is based on the limited characters of the transmitted information symbols and their statistical trait. So this kind of algorithm has the most frequency efficiency but slow converge, complicated computation, and can not effectively track the channel. Semi-blind channel estimation solves the problems of spectrum waste from non-blind channel estimation and high complication of blind channel estimation methods. Hence the semi-blind channel estimation algorithm for MMO-OFDM system is regarded to be a promising way for channel estimation.In this paper, subspace channel estimation algorithms based on second-order statistics (SOS) in several different systems are presented in detail. Then we extend the methods in MIMO system to MIMO-OFDM system, and complete space-time equalization. If each channel in MIMO-OFDM system can be modeled as a FIR filter, the input-output relationship may be rewritten in the form of a low rank model when sufficient number of received data samples is available for both zero-padding and cyclic prefix based OFDM system. The fact that the column space of the received data matrix and column space of the channel matrix span the same subspace may then be exploited for determining the parameters of FIR-MIMO channel. This leads to a cost function made up of the noise space of the autocorrelation matrix of the received signal vector. Hence, estimates of the channel parameter can be obtained by minimizing the function subject to a properly chosen constraint. In blind identification for MIMO system some ambiguities always exist, which may be described in a form of constant full rank Q×Q ambiguity matrix, where Q is the number of transmitted sources. Time equalization can be achieved through MMSE equalizer by using the estimated channel information to eliminate ICI, and a low-dimension equalizer is proposed to decrease computation. The remaining ambiguity matrix can be considered to perform instantaneous linear mixing of the time equalized signals. In addition, source sequences may easily be assumed to be statistically independent and non-Gaussian in communications, so the spatial mixture of the users'signal can be tackled by means of ICA to a accomplish spatial equalization.A Monte Carlo simulation is conducted to simulate the algorithms in this paper using MATLAB on computer. The correlation matrix of the received vector is obtained by standard time-domain sample averaging and white Gaussian noise is added to the output. The normalized root-mean-square error (NRMSE) of channel estimation result and bit-error ratio (BER) of the system are used to evaluate the performance. The analysis of the performance of subspace algorithm, traditional MMSE time equalizer vs. proposed low-dimension equalizer, and ICA method for space equalization in different condition of sample number and signal-nosie ratio (SNR) are given.