Real-Time 5.7–5.8 GHz 32-Beam Approximate Discrete Fourier Transform Spectrum Sensor for RF Perception on Xilinx Sx475T

Abstract

The radio spectrum in the sub-6 GHz (FR1) band is crowded and contested, and is sought after by commercial, scientific and defense users. Situational awareness through spectrum sensing, and AI/ML-enabled perception that recognizes behaviors, patterns, modulations, devices and waveforms is a crucial need for emerging autonomous/cognitive radio systems. This work describes measurable progress in the use of extremely low complexity approximate DFT algorithms as multi-beam beamformers in the digital domain for multibeam spatial RF beamforming. The paper begins with a longterm vision for intelligent spectrum awareness across wide bands and multi-directions with multi-chiplet system in package hardware acceleration of both beamforming, Fourier and AI/ML algorithms, followed by a focus account of specific progress with digital architectures and real-time prototype implementations across the 5.7–5.8 GHz band for 32 RF beams. A real-time temporal frequency resolution of 100 kHz across 100 MHz of baseband bandwidth is achieved, across 32 simultaneous fully-digital RF-beams, using a Xilinx Sx475 FPGA implementation. Details of multiplierless approximate DFT beamformers, automated modulation recognition algorithms using AI/ML, analog channelization, spectrum sensing and perception architectures are also discussed. Over-the-air experiments using the RadioML.2018.a dataset confirmed both single source accuracy (better than 97%) and impact of multi-beams on AI/ML performance for multiple strong RFI sources.