  Interference alignment (IA) is a linear precoding strategy that can achieve
optimal capacity scaling at high SNR in interference networks. However, most
existing IA designs require full channel state information (CSI) at the
transmitters, which would lead to significant CSI signaling overhead. There are
two techniques, namely CSI quantization and CSI feedback filtering, to reduce
the CSI feedback overhead. In this paper, we consider IA processing with CSI
feedback filtering in MIMO cellular networks. We introduce a novel metric,
namely the feedback dimension, to quantify the first order CSI feedback cost
associated with the CSI feedback filtering. The CSI feedback filtering poses
several important challenges in IA processing. First, there is a hidden partial
CSI knowledge constraint in IA precoder design which cannot be handled using
conventional IA design methodology. Furthermore, existing results on the
feasibility conditions of IA cannot be applied due to the partial CSI
knowledge. Finally, it is very challenging to find out how much CSI feedback is
actually needed to support IA processing. We shall address the above challenges
and propose a new IA feasibility condition under partial CSIT knowledge in MIMO
cellular networks. Based on this, we consider the CSI feedback profile design
subject to the degrees of freedom requirements, and we derive closed-form
trade-off results between the CSI feedback cost and IA performance in MIMO
cellular networks.
