Adversarial Training for Channel State Information Estimation in LTE Multi-antenna Systems

Deep neural networks can be utilised for channel state information (CSI) estimation in wireless communications. We aim to decrease the bit error rate of such networks without increasing their complexity, since the wireless environment requires solutions with high performance while constraining implementation cost. For this reason, we investigate the use of adversarial training, which has been successfully applied to image super-resolution tasks that share similarities with CSI estimation tasks. CSI estimators are usually trained in a Single-In Single-Out (SISO) configuration to estimate the channel between two specific antennas and then applied to multi-antenna configurations. We show that the performance of neural networks in the SISO training environment is not necessarily indicative of their performance in multi-antenna systems. The analysis shows that adversarial training does not provide advantages in the SISO environment, however, adversarially trained models can outperform non-adversarially trained models when applying antenna diversity to Long-Term Evolution systems. The use of a feature extractor network is also investigated in this study and is found to have the potential to enhance the performance of Multiple-In Multiple-Out antenna configurations at higher SNRs. This study emphasises the importance of testing neural networks in the context of use while also showing possible advantages of adversarial training in multi-antenna systems without necessarily increasing network complexity.