Interference Management Algorithms for Multicell MIMO Systems

Abstract

Intercell interference is one of the most challenging issues limiting the performance of cellular systems, especially when the spectrum is highly reused across cells.In multiple-input multiple-output (MIMO) systems, in particular, it has been reported that, in a multicell environment, the performance of spatial multiplexing is significantly degraded due to intercell interference. In this dissertation, we develop interference management algorithms for multicell MIMO systems. In the first part of this dissertation, an efficient user selection scheme for the downlink of multiuser MIMO systems is proposed in a multicell environment. In a multicell environment, the intercell interference is one of the most influential factors limiting the performance. Thus, a user selection scheme that considers intercell interference is essential to increase the sum rate. The proposed scheme is based on an interference aware precoding. It sequentially selects users such that the sum rate is maximized. In particular, we develop a simple incremental metric for the sum rate. The use of the derived metric enables a significant reduction in the computational complexity of the user selection process, as compared to the optimal exhaustive search. Numerical results show that the proposed scheme provides near-optimal performance with substantially reduced complexity. In the second part of this dissertation, we propose a one-shot (non-iterative) cooperative beamforming scheme for downlink multicell systems. Unlike previous noniterative beamforming schemes, the proposed cooperative beamforming strives to balance maximizing the desired signal power while minimizing the generated interference power to neighbors by maximizing the network-wide average sum rate. Based on the average sum rate analysis, we derive what we term a global selfishness that steers the egoistic-altruistic balance of the network to maximize average sum rate. The global selfishness enables an autonomous decision on the cooperative beamforming vector in each cell. The main advantage of our approach is that cooperative beamforming solutions are analytically derived not only for an ideal two-cell network scenario but also for a practical three-sectored cellular network scenario. The simulation results verify that the proposed one-shot cooperative beamforming outperforms other conventional non-iterative schemes especially in interference-limited regions, which implies that it is very effective for performance improvement of edge users. In the third part of this dissertation, we propose a distributed cell clustering algorithm for coordinated-multi-point (CoMP) system based on message passing algorithm. In 5G networks system, it is expected that a large number of base stations (BSs) serve simultaneously and BSs are deployed in a very high density. Because of this high density systems, the centralized coordination approaches typically lead to high computational burden for practical implementations. Moreover, the sum rate metric are all coupled and it is difficult to determine clusters of BSs. This motivates us to propose a distributed cell clustering scheme based on message passing algorithm. The simulation results verify that the proposed clustering algorithm outperform the conventional distributed algorithm and reduces computational burden compared to centralized clustering algorithms.