Transfer Learning-Enabled Distortion Compensation for Amplitude-Phase-Time Block Modulation-Based Nonlinear Single-Carrier Wireless Communications
This work addresses PA nonlinearity and memory effects in wireless transmitters, offering a practical solution for improving spectral compliance and energy efficiency in single-carrier systems, but the gains are specific to the APTBM scheme and may be incremental for the broader field.
The paper proposes a transfer-learning-enabled digital transceiver-cooperative method for nonlinear single-carrier wireless communications using amplitude-phase-time block modulation (APTBM), combining iterative clipping and filtering, static digital pre-distortion, and a lightweight digital post-distortion network with few-shot adaptation. Results show reliable transmission at ~2 dB input back-off under a 30-dBc ACLR constraint, with over 2 dB performance gain and reduced training time/computational overhead compared to conventional learning-based DPoD.
Power amplifier (PA) nonlinearity and memory effects significantly limit the spectral compliance, reliability, and energy efficiency of communication systems. To address this, we propose a transfer-learning-enabled, fully digital transceiver-cooperative method for amplitude-phase-time block modulation (APTBM)-based nonlinear single-carrier transmission under adjacent channel leakage ratio (ACLR) constraints. At the transmitter, iterative clipping and filtering (ICAF) and static digital pre-distortion (SDPD) act jointly to reduce signal peaks and suppress spectral regrowth without requiring wideband feedback. At the receiver, the inherent amplitude-phase constraints of APTBM provide weakly supervised prior knowledge for offline inverse-model pretraining, which is followed by the online few-shot adaptation of a lightweight digital post-distortion (DPoD) network. Subsequently, a cascaded DPoD and clipping-noise cancellation scheme systematically compensates for residual distortions induced by both the PA and ICAF. Simulation and measurement results demonstrate reliable transmission at an input back-off of approximately 2 dB under a 30-dBc ACLR constraint. Furthermore, the proposed DPoD approach significantly reduces online training time and computational overhead, delivering a performance gain of over 2 dB compared to conventional learning-based DPoD schemes.