An Initial Survey on Quantum Enhanced RF Signal Extraction in Cluttered Environments

RF (radio frequency) signals are commonly used in modern communication systems, e.g., radio broadcasting, mobile electronic devices, smart meters, satellite transmission, etc., but often face significant challenges such as signal interference and/or background noise. To gain a deeper understanding of signal extraction methods, in this paper, we perform an initial survey on the existing approaches and general trends in extracting signals of interest from the mixed signals received in cluttered environments, including advanced sensing, filtering, data augmentation, machine learning, and emerging quantum-enhanced signal processing algorithms. We then study a specific machine learning model that employs a Bayesian neural network (BNN) as the encoder, combined with a decoder based on a deep neural network (DNN), a long short-term memory (LSTM) network, or another BNN. Case studies are performed to compare different autoencoder neural network models, and initial testing results show that the BNN's probabilistic parameters can handle input signal uncertainty well and may significantly enhance feature extraction and signal clarity. Planned future research will explore quantum computing methods to enhance RF signal extraction in cluttered environments, and some potential quantum algorithms are also identified in the paper.