Grouping-based Interference Alignment with IA-Cell Assignment in Multi-Cell MIMO MAC under Limited Feedback

Abstract: Interference alignment (IA) is a promising technique to efficiently mitigate interference and to enhance the capacity of a wireless communication network. This paper proposes a grouping-based interference alignment (GIA) with optimized IA-Cell assignment for the multiple cells interfering multiple-input and multiple-output (MIMO) multiple access channel (MAC) network under limited feedback. This work consists of three main parts: 1) a complete study (including some new improvements) of the GIA with respect to the degrees of freedom (DoF) and optimal linear transceiver design is performed, which allows for low-complexity and distributed implementation; 2) based on the GIA, the concept of IA-Cell assignment is introduced. Three IA-Cell assignment algorithms are proposed for the setup with different backhaul overhead and their DoF and rate performance is investigated; 3) the performance of the proposed GIA algorithms is studied under limited feedback of IA precoders. To enable efficient feedback, a dynamic feedback bit allocation (DBA) problem is formulated and solved in closed-form. The practical implementation, the required backhaul overhead, and the complexity of the proposed algorithms are analyzed. Numerical results show that our proposed algorithms greatly outperform the traditional GIA under both unlimited and limited feedback.