A neural network-optimized broadband RF rectifier with wide dynamic range

To achieve both wide bandwidth and wide dynamic range, a rectifier with adaptive power allocation method is designed, and a broadband matching network using neural network optimization is proposed. The rectifier consists of two broadband rectifier branches operating in different input power ranges and utilizes the different input impedances of the branches at different input power levels to achieve automatic power allocation. The optimization of the matching network for each branch using neural networks reduces the computational cost and simplifies the design process. The proposed rectifier is validated by simulation analysis and practical tests. Measurements show that the rectifier is more than 50% efficient over the frequency range of 0.31 to 1.38 GHz, with a peak efficiency of 82% at 25 dBm input power. At 0.6 GHz, the rectifier is more than 50% efficient over the input power range of 7.5 to 33.5 dBm, with a dynamic range of 26 dB.