| Recently, as a novel method to handle interference, interference alignment(IA) attains more and more attentions and becomes a popular research topic. The fundamental concept of interference alignment is to compress signal dimensions of the received interference as far as possible so that the interference-free signal dimensions can be provided for transmission of the desired signal. The emergence of interference alignment makes the capacity of interfering network have a leap growth, and contributes to high speed and reliable transmission for next generation wireless communication systems. Therefore, to study the IA algorithm design and its application in actual network has a vary important significance. Recent studies in both information theoretical analysis and application design for different types of interfering networks have made outstanding progress. However, due to the complicated interference structure and limited spatial signal dimensions in multi-cell multi-user MIMO systems, the researches about IA algorithm design and application for this interfering network are extremely limited.This dissertation focuses on the IA and its application in multi-cell multi-user MIMO downlink scenario, which is the typical MIMO interfering broadcast channels(IBC). The goal is to propose basic IA algorithms and IA scheme design for application in practical network. So the researches are divided into two parts: one is IA algorithm design, the other is application scheme design over limited signal dimensions. Specifically, based on the summarization of conventional IA algorithms, several IA algorithms with different optimization criteria are presented for MIMO IBC. Meanwhile, by introducing the user selection mechanism and the partially connectivity of wireless communication link, two types of IA solutions satisfied with feasibility conditions of IA are designed and can be applied efficiently in a multi-cell multi-user network. Specially, the IA scheme based on the partially connected network can even break through the constraint of limited signal dimensions to be applied in a large-scale multi-cell network while achieved capacity increasing linear with the cell number’ growth. In addition, for the cognitive radio network(CRN), a feasible cognitive IA scheme with user selection is also proposed. The main content of this dissertation could be summarized as the following several aspects:In Chapter 2, the algorithm design of IA for multi-cell multi-user MIMO downlink scenario, namely for MIMO IBC, is briefly studied. Firstly, the corresponding feasibility conditions of IA for MIMO IBC are analyzed. And then two existing iterative IA algorithms for MIMO IBC, namely minimum interference leakage(Min-IL) criterion based multi-cell Min-IL algorithm and maximum signal-to-interference-plus-noise ratio(Max-SINR) criterion based Max-SINR algorithm are discussed. The multi-cell Max-SINR algorithm generally has better sum rate capacity than the multi-cell Min-IL algorithm. However, because the multi-cell Max-SINR algorithm uses the optimization for data stream and not considers the requirement of system for the orthogonal precoding matrix and receive filter matrix in practical application of IA, and its performance is not good in multiple streams mode, an improved algorithm for multi-cell Max-SINR algorithm, which is based on generalized eigenvalue decomposition(GED) is proposed. While the maximum signal-to-leakage-plus-noise ratio(Max-SLNR) criterion based Max-SLNR multi-cell Max-SLNR iterative optimal algorithm is presented too.Chapter 3 investigates the application problems of IA in multi-cell multi-user MIMO systems under limited signal dimensions(the number of antennas per transmitter and receiver pair are limited). Based on the summarization for the conventional schemes, several IA schemes combined with user selection are presented and improves the system capacity of conventional schemes further with optimization for their algorithms or iterative structure and selected user subset. By introducing the mechanism of user selection, the IA schemes can not only receive the gain of multi-user diversity, but also be applied in a multi-cell multi-user network with arbitrary size, nevertheless the total DoF that the whole system can achieve is still limited by the signal dimensions.In Chapter 4, aiming for further solving the application problems of IA in multi-cell multi-user MIMO systems under limited signal dimensions, by introducing the partially connectivity of wireless communication link, a noval serial iterative IA scheme is presented firstly for partially connected MIMO IC. Then focusing on multi-cell MIMO systems, two feasible iterative IA schemes base on the partially connected MIMO IBC are proposed. Through partially connectivity, the IA scheme can be applied in a multi-cell network of arbitrary cell size, while achieve the optimal total degrees of freedom increasing linear with the cell number’ growth. That means, in a partially connected MIMO IBC, IA can break through the constraint of finite signal dimensions and be achievable among an arbitrary number of cells. The IA scheme based on partially connected network has an important significance for application of IA in a practical large-scale multi-cell network.In Chapter 5, the application of IA in cognitive radio network(CRN) is investigated. Due to the interference structure of cognitive network brought is more complicated than before, the chapter firstly presents the iterative cognitive IA(CIA) scheme based on user selection for the scenario which the secondary network of cognitive radio network is MIMO interference channel. Sequentially, the CIA scheme is extended to the cognitive radio network where the secondary network is the MIMO IBC. The feasible CIA scheme with user selection is proposed, which can eliminate the influence of various types of inference efficiently at same time. The researches of this chapter further broaden the application of IA in more complex network scenarios.
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