Design of a Pulse-Operated, Nonlinear Distortion Statistics CMOS Module for Millimeter-Wave Power Amplifiers’ Adaptive Linearization

A low-power, pulse-operation-based nonlinear distortion statistics module is proposed, comprising a nonlinear distortion error conversion unit and a nonlinear distortion error extraction unit. This module enables high-accuracy characterization of Ka-band power amplifier (PA) nonlinearity, providing critical guidance for advanced self-adaptive linearization. A novel nonlinear distortion statistical characteristics theory is introduced, leveraging the cumulative probability distribution (CPD) of PA input and output envelope voltages. Based on this theory, a search method for maximizing the accuracy of nonlinearity characterization is developed, mainly focusing on the dynamic range. The proposed statistics module is implemented in a 65-nm CMOS process. The nonlinear distortion error conversion unit first characterizes nonlinear distortion through a dual-path peak detector and comparators. Subsequently, the nonlinear distortion error extraction unit extracts the nonlinear distortion error signal using only logic gates and an error integrator, which performs an <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">xor</small> operation followed by a summation operation to derive the error. This design significantly reduces hardware complexity while maintaining high accuracy. Prototype verification is conducted through two scenarios. Characterizing accuracy, mainly focusing on the dynamic range, is validated by cascading the statistics module with an analog linearizer. A CMOS PA integrated with an analog linearizer is utilized as an example to demonstrate the potential for self-adaptive PA linearization. The characterizing accuracy measurement results demonstrate that the proposed module achieves a normalized dynamic range better than 0.57. In the self-adaptive PA linearization measurement, the statistic module’s iterative guidance for linearization achieves the adjacent channel power ratio (ACPR) of −35 dBc and an improvement of 2.2% in error vector magnitude (EVM). With a low power consumption of only 30 mW, the proposed statistics module achieves high accuracy in nonlinear characterization and provides a self-adaptive linearization solution for millimeter wave (mmWave) PAs, demonstrating the significant potential for advanced self-adaptive linearization applications.