Research On Robust Interference Alignment Algorithms With Imperfect Channel State Informations

The channel capacity of the users will decrease due to the serious interference when the receivers decode the desired signal in multi-users communication systems. In order to solve this situation, interference alignment is the most ideal solution. Generally speaking, the prerequisite of realizing the interference alignment is that the sender can obtain perfect global channel state information(CSI). However, in the actual wireless communication environment, the sender will receive a non-ideal CSI due to channel estimation error, quantization error, transmission delay and so on, which makes the interference signal cannot be completely aligned and degrades the performance of system. Therefore, it is very necessary to design robust interference alignment algorithms. The main work of this thesis includes:A limited feedback-based interference alignment algorithm to maximize the rate lower bound in interfering MIMO-MAC is proposed in this paper,which is based on analyzing the disadvantages of traditional limited feedback interference alignment schemes in two-cell interfering MIMO-MAC. We select optimized code words from the perspective of maximizing the rate lower bound, and combine bit allocation algorithm and MAX-SINR algorithm to reduce the sum rate loss. Further, we generate the sets of code words which are close to the perfect precoders, and in these sets we only need low complexity searching to gain the optimized code words, which realizes the suboptimal global searching. The simulation results verify that the proposed algorithm effectively improves the system rate and the rate lower bound of system users.We research the case that the error of CSI degrades the performance of interference alignment algorithm in K-user interference network, and propose a robust interference alignment algorithm based on QR decomposition. The QR composition is used to preprocess the jointly received signal which includes error. Then we minimizes the interference leakage power to design the equivalent precoding matrix, and utilizes minimum mean square error(MMSE) criterion to design the equivalent interference suppression matrix. Finally, the simulation results verify that the proposed algorithm effectively improves the performance of system under the error of CSI.We research the case that the CSI exist the delay and error which degrades the performance of system, and propose a robust joint interference and phase alignment algorithm based on Bayes estimation and power allocation among signal flows. Firstly, the best prediction of the current CSI is obtained through the Bayes estimation. And then, interference suppression matrix is designed through maximizing signal to inter-cell interference plus noise ratio in the forward link, and the precoding matrix is designed through maximizing SINR in the reverse link. Finally, we combine power allocation and phase alignment to design the robust interference alignment algorithm. The simulation results verify that the proposed algorithm effectively improves the performance of system when CSI exits the delay and error.