Distributed Coordination in Multiantenna Cellular Networks

Wireless communications are important in our highly connected world. The amount of data being transferred in cellular networks is steadily growing, and consequently more capacity is needed. This thesis considers the problem of downlink capacity improvement from the perspective of multicell coordination. By employing multiple antennas at the transmitters and receivers of a multicell network, the inherent spatial selectivity of the users can be exploited in order to increase the capacity through linear precoding and receive filtering. For the coordination between cells, distributed algorithms are often sought due to their low implementation complexity and robustness. In this context, the thesis considers two problem domains: base station clustering and coordinated precoding. Base station clustering corresponds to grouping the cell base stations into disjoint clusters in order to reduce the coordination overhead. This is needed in intermediate-sized to large networks, where the overhead otherwise would be overwhelmingly high. Two solution methods for the clustering problem are proposed: an optimal centralized method, as well as a heuristic distributed method. The optimal method applies to a family of throughput models and exploits the structure of the model to find bounds that can be used to focus the search for the optimal clustering into promising territories. The distributed method instead uses notions from coalitional game theory, where the base stations are modelled as rational and intelligent players in a game. By letting the players make individual deviations that benefit them in the game, i.e. switching clusters, a distributed coalition formation algorithm is obtained. Coordinated precoding is the act of finding the linear precoders and receive filters that maximize the network performance, given a base station clustering. Four specific challenges are studied in this problem domain. First, coordinated precoding under intercluster interference is considered. The channels of the intercluster links are not explicitly estimated due to overhead reasons, and these links thus lead to intercluster interference. By exploiting the known statistics of the intercluster channels, a robust and distributed coordinated precoding algorithm is developed. Second, coordinated precoding under imperfect channel state information is considered. Relying on the channel reciprocity under time-division duplex operation, a distributed estimation framework is proposed. Given the estimated channels, a robust and distributed coordinated precoding algorithm is then derived. Third, coordinated precoding under imperfect radio hardware is considered. By modelling the radio frequency distortion noises, a distributed coordinated precoding method that accounts for the imperfections is proposed. Fourth, joint coordinated precoding and discrete rate selection is considered. By bounding and linearizing an originally intractable optimization problem, a heuristic algorithm is derived which selects the transmit rate from a finite set and simultaneously forms the linear precoders and receive filters.