A joint real grassmannian quantization strategy for MIMO interference alignment with limited feedback

Interference alignment (IA) is a scheme to approach the capacity at high signal-to-noise ratio (SNR) in multiuser multiple-input multiple-output (MIMO) interference networks. To implement the IA scheme in a frequency-division duplexing (FDD) system, transmitter channel state information (CSIT) is fed back from the receiver with finite bits. However, such CSIT is subject to quantization errors and delays of feedback channels. In this paper, we verify that interference leakage is bounded by chordal distance in the MIMO channel. Besides, a joint real Grassmannian quantization strategy is proposed to reduce chordal distance to improve CSIT quality. Meanwhile, under the noise-limited criterion, the lower bound of the codebook size of our proposed strategy is much smaller than that of the conventional complex Grassmannian quantization strategy. Simulations demonstrate that our proposed strategy provides substantial performance gains compared with the conventional strategy.