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This paper presents a new low-area and low-power Field Programmable Gate Array (FPGA) implementation of a Radio Frequency (RF) modulation classifier based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, known as DBCLASS. The proposed architecture demonstrates a novel approach for the efficient hardware realisation of the DBSCAN algorithm by utilising parallelism, a bespoke sorting algorithm, and eliminating memory access. The design achieves 100&#x0025; classification accuracy with labcaptured RF data above 8 dB signal-to-noise ratio(SNR) whilst exhibiting an improvement of latency in comparison to the next quickest design by a factor of 7.5, a reduction in terms of total FPGA resources used in comparison to the next smallest complete system by a factor of 3.65, and a reduction in power consumption over the next most efficient by a factor of 4.75. The proposed design is well suited for resource-constrained applications, such as mobile cognitive radios and spectrum monitoring systems.

Low Area and Low Power FPGA Implementation of a DBSCAN-Based RF Modulation Classifier