A Reinforcement-Learning-Assisted Power Amplifier for RF Fingerprint Generation in 65 nm CMOS

A reconfigurable power amplifier (PA) is implemented in CMOS 65nm to enable radio identification for secure wireless communication by injecting tunable radio frequency fingerprints (RFFs) into the physical layer. The large ensemble of RFFs is achieved by offsetting the distributions of process variations affecting the PA’s hardware features. The resulting large RFF capacity is exploited to increase resilience to noise and temperature changes by selecting distinct RFFs from the ensemble and reconfiguring the PA to restore nominal RFFs following temperature shifts. The secure PA achieves over 14000 time-varying RFFs while consuming only 22 mW and occupying a core area of < 0.0951 mm2. A reinforcement learning (RL)-based control has been implemented on FPGA for closed-loop reconfiguration of the transmitter to achieve robust and low-overhead security measures that overcome noise and temperature influences in dynamic environments.