Interference alignment (IA) is a linear precoding strategy that can achieve
optimal capacity scaling at high SNR in interference networks. Most of the
existing IA designs require full channel state information (CSI) at the
transmitters, which induces a huge CSI signaling cost. Hence it is desirable to
improve the feedback efficiency for IA and in this paper, we propose a novel IA
scheme with a significantly reduced CSI feedback. To quantify the CSI feedback
cost, we introduce a novel metric, namely the feedback dimension. This metric
serves as a first-order measurement of CSI feedback overhead. Due to the
partial CSI feedback constraint, conventional IA schemes can not be applied and
hence, we develop a novel IA precoder / decorrelator design and establish new
IA feasibility conditions. Via dynamic feedback profile design, the proposed IA
scheme can also achieve a flexible tradeoff between the degree of freedom (DoF)
requirements for data streams, the antenna resources and the CSI feedback cost.
We show by analysis and simulations that the proposed scheme achieves
substantial reductions of CSI feedback overhead under the same DoF requirement
in MIMO interference networks.Comment: 30 pages, 7 figures, accepted for publication by IEEE transactions on
signal processing in June, 201
