Blind Beamforming via Deep Learning-Based Signal Classification and Transfer Learning

We propose a new cognitive technique for blind adaptive beamforming which uses a pre-trained deep learningbased signal classifier to protect a signal of interest (SOI) from interference. The method is an application of transfer learning since the features learned from noisy single-antenna captures are transferred to the task of beamforming using an antenna array. The proposed technique, called the classification-based transfer learning (CBTL) beamformer, forms a beam towards the SOI and nulls towards interference by maximizing a measure of classification confidence for the SOI. It does not require synchronization to the SOI or knowledge of the array manifold. We describe how the beamformer is optimized and the desired characteristics of the classifier. Extensive simulations show that the CBTL beamformer can improve the array signal-to-interference-plus-noise ratio by 8 dB or more compared to state-of-the-art techniques when using 64 samples from a 4-element array, which can be useful for dealing with dynamic environments. We validate this result using real-world data from an open-access radio testbed. The novelty of the proposed approach is in its ability to repurpose a classifier for a task that it was not trained for, eliminate the need for training data from an antenna array, and automate the development of beamformers without relying on expert-designed features.