Research On Precoding And Feedback For Mimo Interference Alignment

Interference alignment is an effective interference management strategy. With properly designed transmit precoder such that multiple interference from different transmitters overlapped at each receiver. Interference alignment can realizes the maximum degree of freedom (DoF) in interference system and significantly improve system capacity. Multiple input multiple output (MIMO) technology is the key technology to improve the spectrum efficiency. MIMO system can provide a large number of spatial degrees of freedom, which is very suitable for the implementation of interference alignment. Combining MIMO technology and the interference alignment will greatly improve the interference network capacity, therefore becomes an important research area in today’s wireless communication field.Research on interference alignment is still in its infancy and there are many problems unsolved. For example, explicit precoder of interference alignment is difficult to obtain in most cases, and only suboptimal numerical solution can be achieved by numerical methods. Interference alignment requires global channel state information at transmitter. On the one hand, acquisition of the global channel state information will bring a lot of overhead. On the other hand, imperfect channel state information bring about precoding error and lead to performance degradation. Further, the origin interference alignment algorithms are proposed based on symatric interference channel, and modifications are needed before the implementation of interference alignment in to the current networks such as cellular network.Focus on MIMO interference alignment, the research works in this thesis include precoder design, channel state information feedback and transceiver design for cellular networks, which are detailed as follows:Firstly, degree of freedom and precoding algorithms for interference alignment are analyzed. Since degrees of freedom should be known before designing the precoder in MIMO interference alignment, degree of freedom for several MIMO interference networks are analyzed and the methods of realize degree of freedom are evaluated. After that, precoder design algorithms are analyzed, which include closed form algorithms and numerical algorithms. This thesis gives an interference alignment algorithm based on quadratic programming which jointly design the transmit precoder and receive decoder to optimize the system capacity. In each iteration, precoders and decoders are updated simultaneously. Simulation results show that the proposed algorithm outperforms traditional interference alignment algorithm in the expanse of higher complexity. However, this algorithm can be used to evaluate the performance of interference alignment. Secondly, MMSE interference alignment strategy under imperfect channel state information is studied. Interference aligned requires global channel state information at transmitters (CSIT). Channel state information obtained at transmitter usually have some errors. Gaussian statistic channel error model and norm bounded channel model are analyzed. Based on the Gaussian statistic channel error model, this thesis gives interference alignment strategy which minimize the mean square error with per transmitter power constraint. Simulation results show that compared with traditional MMSE algorithm，the proposed algorithm achieves better performance. Based on norm bounded channel model, two interference alignment algorithms which minimize the worst case sum MSE and minimize worst case user MSE are proposed. These two algorithms are robust transceiver algorithms mini-max total MSE algorithm can achieve better total MSE performance, from the viewpoint of system and the mini-max users MSE algorithm can achieve better user MSE performance, which is a fair precoding algorithm.Thirdly, bit allocation algorithm in the process of channel state information feedback for MIMO interference alignment is studied. Since channel gains of interference channels are differenc, allocation feedback bits equally on different channels is sub-optimal. In order to solve this problem, relationship between quantization distortion and quantization bits is studied, and lower bound of average rate for interference alignment with limited feedback is derived. Based on the relationship between lower bound of average rate and the quantization bits and channel gains, distributed and centralized feedback bit allocation algorithms are proposed. Distributed feedback bit allocation algorithm maximize the lower bound of the average rate of each user with per user feedback bits constraint, while centralized feedback bit allocation algorithm maximize the average sum rate lower bound with the total number of feedback bits constraint. Simulation results show that the proposed two feedback bit allocation algorithms achieve higher sum rate compared with the equal bit allocation algorithm. Performance of centralized feedback bit allocation algorithm is slightly better than distributed algorithm, but centralized feedback bit allocation should be processed at central processing unit, so more control and feedback resources are needed. Also, the centralized feedback bit allocation algorithm is more computational complex than distributed algorithm.Lastly, interference alignment based on cascade precoding for cellular networks is studied. In order to solve the rate degradation for cell edge user due to inter cell interference, two modified interference alignment precoding algorithms are proposed. Cascade precoding algorithm based on interference space expansion and interference minimization are proposed. Proposed algorithms can achieve better performance when there are multiple interference base stations. Since desired signal power cannot be controlled in interference alignment, which could lead to signal power degradation and therefore the degradation of throughput. Considering the characteristics of the cascade precoding algorithm, multi-user selection algorithm based on the Frobenius norm of equivalent channel matrices are proposed. Simulation results show that the proposed multi-user selection algorithm can greatly improve system performance.