FPGA-based network-resonance applebaum adaptive arrays for directional spectrum sensing

Cognitive radio (CR) depends on the accurate detection of frequency, modulation, and direction pertaining to radio sources, in turn, leading to spatio-temporal directional spectrum sensing. False detections due to high levels of noise and interference may adversely impacts the CR's performance. To address this problem, a novel system architecture that increases the accuracy of directional spectrum sensing in situations with low signal to noise ratio (SNR) is proposed. This work combines adaptive arrays, multidimensional filter theory and cyclostationary feature detection. A linear array Applebaum beamformer is employed in conjunction with a two-dimensional (2-D) planar-resonant beam filter to perform highly directional receive mode wideband beamforming with improved spatial selectivity. A Xilinx Virtex-6 based field programmable gate array (FPGA) prototype of the improved beamforming front-end verifies a clock frequency of 100.9 MHz. The proposed network-resonant Applebaum array provides 6 dB, 5.5 dB and 5 dB noise suppression capability reflected in the spectral correlation function for input SNRs of -20 dB, -25 dB, and -30 dB, respectively, for an RF beam direction 50° degrees from array broadside.