A joint real Grassmannian quantization strategy for SISO IA with limited feedback

Interference alignment (IA) is a scheme to achieve degrees of freedom (DOF) of interference network at high signal-to-noise ratio (SNR). In order to implement IA scheme in frequency-division duplexing (FDD) system, receivers feedback channel state information to transmitters. The key problem is to acquire accurate transmitter channel state information (CSIT) in the presence of the quantization error. In this paper, a joint real Grassmannian quantization strategy is proposed to reduce codebook size in single-input single-output (SISO) frequency-selective channel with K user. More concretely, this strategy quantizes the real part and imaginary part of channel vector respectively to reduce the chordal distance. Meanwhile, a noise-limited criterion is assumed that interference leakage is smaller than thermal noise. Under this criterion, the codebook size using the proposed strategy is much smaller than the codebook size using conventional complex Grassmannian quantization strategy. With the same codebook size, simulations show a significant sum rate gain at high SNR compared with the conventional strategy.