Closed-Form Sum-Rate Analysis of Interference Alignment with Limited Feedback Based on Scalar Quantization and Random Vector Quantization

Interference alignment (IA) is a promising interference management technique to achieve the theoretical optimal degree of freedom (DoF) performance in multi-user cooperation scenarios. However, effective achievable sum-rate IA largely affected by feedback overhead and accuracy channel state information (CSI) decoding (DI). Therefore, it critical establish exact relationship between obtain performance. Most existing analysis approaches focus on vector quantization (VQ)-based strategy, but implementation complexity VQ will be excessive when more bits are required expected for larger-sized matrices or higher signal-to-noise ratio (SNR) regimes. Moreover, obtained formulas too complicated quick evaluation. In this paper, new method under different strategies proposed trade-off complexity, closed-form expressions derived. First, case with random (RVQ)-based CSI feedback, error RVQ transformed into equivalent Gaussian error, based which formula obtained. Second, scalar (SQ)-based SQ established. Third, SQ-based RVQ-based DI derived combining these two kinds errors. Finally, simulation results confirm that accurate enough, can help determine practical conditions. demonstrate may applicable scenarios fewer receiving antennas low SNR