Data-Driven Soft Demapping for Residual Impairments Channels

Deep learning based solutions are being integrated into the physical and link layers of wireless networks. They often effect an improvement in transmission reliability and/or efficiency when there is a model or an algorithm deficit. In this letter, we propose a deep learning-aided soft demapper, consisting of a fully-connected deep neural network (DNN), to alleviate a channel model deficit. We apply it in microwave backhaul transmissions affected by impairments generated by the local oscillator and power amplifier. The proposed DNN soft demapper learns the best approximation for the log-likelihood ratios (LLRs). The learned LLRs show gains over model-based impairment-aware LLRs, as they capture the actual channel as observed through data. We implement weight pruning and periodical retraining to adapt to statistical changes and make our proposed approach fit for practical cost-aware applications.