Digital Predistortion of Millimeter-Wave GaN Power Amplifiers for 6G Integrated Communication, Sensing, and Power Transfer Scenarios

Abstract

Aiming at supporting diverse applications, integrated communication, sensing, and power transfer (ICSPT) is anticipated to be a key feature of 6G networks. In ICSPT scenarios, power amplifiers (PAs) suffer from dynamic effects due to rapid switching between different operating modes, posing new challenges for PA linearization. In this article, a novel digital predistortion (DPD) method is proposed to effectively compensate for PA distortion in such scenarios. To accurately characterize these dynamic effects with low complexity, we introduce a method for calculating dynamic state variables that represent the dynamic behavior of PAs. For communication mode, these variables are incorporated into a neural network (NN) model, leveraging NN’s powerful fitting capability to track changes in PA’s nonlinear characteristics due to mode switching. For sensing and power transfer modes, the variables are used to fine-tune the input signal, compensating for fluctuations in PA’s gain. Experimental validations were conducted on a millimeter-wave (mmWave) gallium nitride (GaN) PA at a center frequency of 27 GHz. The PA was excited by 40/200-MHz NR signals for communication mode, 100/500-MHz frequency-modulated continuous-wave (FMCW) signals for sensing mode, and a CW signal for power transfer mode. The test demonstrates that the proposed method significantly improves transmission performance while maintaining similar complexity.