Transformer-Aided CSI Prediction for Interference Alignment in MIMO Systems

Interference alignment (IA) is a degrees-of-freedom optimal interference management technique which approaches the capacity of an interference network at high signal-to-noise ratio (SNR) regime. It works as a cooperative precoding scheme in which different transmitters coordinate their transmissions so that all interference signals at an unintended receiver are confined to the same subspace. Each receiver can recover the desired signal by eliminating the aligned interferences using a suitable receiver design. However, IA requires full channel state information (CSI) of all involved links at all transmitters, which incurs a huge feedback overhead and is not feasible in practice. In this study, we propose a transformer-aided CSI prediction for signaling overhead reduction. Our proposed transformerbased CSI prediction demonstrates superior accuracy compared to the conventional methods. The predicted CSI is utilized to perform IA and compute the precoding matrices in a multiantenna system, allowing them to efficiently implement IA scheme. Numerical results show that the achievable rate with the proposed transformer-based method is within 98 % of the achievable rate with perfect CSI. In comparison, conventional deep learning-based CSI estimation approaches, namely long short-term memory networks and convolutional neural networks, is found to achieve only 90 % and 88 % of the ideal rate, respectively.