Demonstration of FPGA-Accelerated ML-based Wideband Spectrum Awareness and Signal Characterization in Real-Time

This paper proposes a sophisticated spectrum awareness tool that introduces a novel approach at the intersection of spectrum sensing and signal characterization, leveraging an optimized 1-dimensional (1D) faster region-proposal convolutional neural network (FR-CNN). Our research addresses the evolving challenges in cluttered radio frequency environments, where multiple transmissions occur simultaneously. The FR-CNN model is tailored for 1D signal processing, incorporating machine learning for precise angle of arrival (AoA) estimation. Notably, our framework outperforms conventional methods in terms of efficiency and accuracy, extending to signal characterization through over-the-air testing with software-defined radios (SDR). Additionally, our approach demonstrates adaptability to wideband signals, enabling effective analysis and classification across a broad range of frequencies and waveforms. In a complementary study, we evaluate machine learning methods for AoA estimation in wireless communication systems. Despite a slightly higher error rate, the machine learning algorithm demonstrates robustness and stability in synthetic and over-the-air (OTA) test scenarios. The sensing results will be displayed in real-time in the frequency domain, indicating the signal’s angle of arrival, center frequency, bandwidth, and modulation type.