Antenna selection for Interference Alignment based on subspace Canonical Correlation

The main objective of this contribution is to develop a novel antenna selection algorithm for Interference Alignment (IA) in multi-user communication systems. Successive IA requires high degree of independency among the channels, which could hardly exist in real-world environments. Therefore, the Bit Error Rate (BER) performance of the IA system suffers from a dramatic degradation, especially in indoor environments. Applying the developed antenna selection algorithm can effectively increase the channels diversity and improve the BER performance. This selection algorithm based on selecting the maximum Canonical Correlation (CC) between the desired signal subspace and the interference-free subspace in order to maximize the average received Signal-to-Noise Ratio (SNR) of the system. The influence of the CC on sum-rate would be presented mathematically. Simulation results show a significant improvement of the BER system performance based on the CC antenna selection algorithm compared with the maximum sum-rate selection algorithm.