HORCRUX: Accurate Cross Band Channel Prediction

Recent advancement in Frequency Domain Duplexing (FDD) enables wireless systems to use different frequency bands for uplink and downlink communication without explicit channel feedback information. The current state-of-the-art approaches either estimate the underlying variables in the uplink channel or use an artificial neural network architecture to estimate the downlink channel from the uplink channel. However, such techniques fail to perform accurately in multipath-rich environments and environments unseen during training. This paper presents HORCRUX, a physics-based machine learning system that can be generalized and scaled to any environment while predicting downlink channels with high accuracy and applies to single-antenna and MIMO systems. Our approach uses multiple neural networks, trained on the standard wireless channel model, firstly to divide the uplink channel into smaller sub-channels and secondly to generate coarse estimates for the variables for each of the underlying sub-channels. Finally, we use an efficient and fast optimization framework to get fine-tuned variable estimates to predict the downlink channel. We implement our system using software-defined radios. Our evaluations show that HORCRUX performs 8 dB better than state of the art in downlink channel prediction accuracy in diverse wireless environments. 1