|Wangkit Wong||The Hong Kong University of Science and Technology, Hong Kong|
|S H Gary Chan||The Hong Kong University of Science and Technology, P.R. China|
Interference Alignment (IA) has emerged as a promising interference coordination approach for cooperative MIMO systems. Due to heavy CSI feedback overhead, APs (Access Points) need to be partitioned into cooperation groups no larger than a certain size where only APs in the same group are able to cooperate with IA. We consider a general MIMO network using a hybrid interference coordination approach, i.e. intra-group interference is managed with IA, while inter-group interference is overcome with traditional orthogonal multiple access techniques. Users are usually non-uniformly distributed. Their throughput can be improved by association optimization. We study the novel problem of minimizing AP load by joint AP grouping and user association. The problem is shown to be NP-hard. Based on alternating direction optimization, we propose DAGA (Distributed Joint AP Grouping and User Association) to tackle the problem. DAGA is distributed and uses only long-term CSI. Based on current AP grouping, it produces an approximated user association solution which is at most e log m (m is the number of APs) times of the optimum. Based on current user association, it adjusts AP grouping with local search. Extensive simulation results show that it substantially outperforms other comparison schemes.