Instruct: Given the title of a paper, retrieve paper titles that are cited by the given paper.
Query: A Low-Power Self-Forward-Body-Bias CMOS LNA for 3–6.5-GHz UWB Receivers
An ultrawideband CMOS low-noise amplifier for 3.1-10.6-GHz wireless receivers
Millimeter-Wave Low-Noise Amplifier Design in 28-nm Low-Power Digital CMOS
Highly Linear mm-Wave CMOS Power Amplifier
A 2-nW 1.1-V self-biased current reference in CMOS technology
A single-chip linear CMOS power amplifier for 2.4 GHz WLAN
A 52 $\mu$ W Wake-Up Receiver With $-$ 72 dBm Sensitivity Using an Uncertain-IF Architecture
A 7-µW 2.4-GHz wake-up receiver with -80 dBm sensitivity and high co-channel interferer tolerance
Design and Optimization of Area-Constrained Wirelessly Powered CMOS UWB SoC for Localization Applications
A 3dB NF 0.1–6.6GHz inductorless wideband low-noise amplifier in 0.13µm CMOS
A 90nm-CMOS, 500Mbps, fully-integrated IR-UWB transceiver using pulse injection-locking for receiver phase synchronization
An Ultra-Low-Voltage CMOS Process-Insensitive Self-Biased OTA With Rail-to-Rail Input Range
Low-power receive electronics for a miniature real-time 3D ultrasound probe
Design, Implementation, and Performance Evaluation of a Flexible Low-Latency Nanowatt Wake-Up Radio Receiver
A 3 . 5 GHz Low Noise , High Gain Narrow Band Differential Low Noise Amplifier Design for WiMAX Applications
A Silicon-Based Low-Power Broadband Transimpedance Amplifier
A Compact UWB Antenna for On-Body Applications
3–5 GHz UWB Impulse Radio Transmitter and Receiver MMIC Optimized for Long Range Precision Wireless Sensor Networks
Low Voltage CMOS LC VCO with Switched Self-Biasing
A 60 GHz Broadband Low-Noise Amplifier With Variable-Gain Control in 65 nm CMOS
Dumbbell DGS based broadband RF choke for UWB LNA
Interactive knowledge-based kernel PCA
Modeling of SiC MOSFET with temperature dependent parameters and its applications
Noise modeling for RF CMOS circuit simulation
Challenges and advances in computational docking: 2009 in review.
Context Adaptive Neural Network for Rapid Adaptation of Deep CNN Based Acoustic Models