This paper presents a tunable active bandpass filter with adjustable notch close to the passband for blocker rejection. The proposed filter is based on two-path signal cancellation and consists of a top-C coupled tunable bandpass filter in parallel with an inductor. The proposed filter is implemented with high-Q N-path filter/resonator blocks in 65-nm CMOS process. The passband of the filter is tunable from 0.4 GHz to 2.2 GHz with a 3-dB bandwidth of 14.4-15.6 MHz, an insertion loss of 3.6-5.8 dB, a noise figure of 3.8-6 dB, and a total power consumption of 12.4-42 mW. Transmission zero is created close to the passband with a minimal offset of 40 MHz and are tunable across a 25 MHz range with up to 54 dB rejection. The blocker 1-dB compression point is 8.5 dBm and the out-of-band IIP3 is 23.5 dBm. The proposed filter provides a promising solution to multi-standard, multi-frequency software-defined radio applications.

RF fingerprinting is an emerging technology for identifying hardware-specific features of wireless transmitters and may find important applications in wireless security. In this study, the authors present a new RF fingerprinting scheme using deep neural networks. In particular, a long short-term memory based recurrent neural network is proposed and used for automatically identifying hardware-specific features and classifying transmitters. Experimental studies using identical RF transmitters showed very high detection accuracy in the presence of strong noise (signal-to-noise ratio as low as −12 dB) and demonstrated the effectiveness of the proposed scheme.