RF Anti-Jamming via Multi-Level Howells-Applebaum Null-Forming: 32-Channels, 5.8 GHz/ 100 MHz/ Beam on Xilinx Sx475T FPGA

Abstract

Real-time sensing and perception of the radio spectrum based on artificial intelligence (AI) is crucial for emerging intelligent wireless and electronic warfare systems. However, sensing can be greatly impacted by harmful radio frequency interference (RFI). Emerging drone warfare allows many RFI sources/jammers to be distributed across a wide field-of-view which necessitates real-time measurement, adaptation and aperture nulling to remove the RFI before AI-based sensing and perception of sources of interest can occur. This work explores algorithmic innovations that improve the computational complexity of classical Howells-Applebaum adaptive nulling algorithm to enable fast, real-time adaptive operation at significantly lower arithmetic complexity. Design examples for AI-enabled sensing and perception across a 32-element antenna receiver with 32 independent channels and a Xilinx Virtex-6 Sx475 FPGA backend are discussed. Examples show computer architecture for digital signal processing and AI algorithms operating on the FPGA, with real-time measurements for spectrum sensing and modulation recognition on the RadioML2018.a dataset with and without the proposed adaptive nullforming system. A general adversarial AI-based spectrum perception architecture that allows both jamming of opponents while simultaneously nulling out RFI and conducting AI-based radio intelligence applications is examined and demonstrated in the 5.7-5.8 GHz band using a 32 element real-time FPGA realization. Modulation recognition is demonstrated for 16/32-QAM signals under heavy RFI conditions with additional “in the wild” RFI sources present.