Transfer Learning-Enabled Distortion Compensation for Amplitude-Phase-Time Block Modulation-Based Nonlinear Single-Carrier Wireless Communications

2026-08-09

AI Summary

This work addresses the challenges posed by nonlinearities and memory effects in power amplifiers, which cause spectral regrowth, reduced energy efficiency, and degraded reliability—particularly under stringent adjacent channel leakage ratio (ACLR) constraints where efficiency and performance are difficult to balance. The authors propose a fully digital, transceiver-cooperative distortion compensation framework based on transfer learning: the transmitter integrates iterative clipping and filtering (ICAF) with static digital pre-distortion (SDPD), while the receiver employs a lightweight digital post-distortion (DPoD) network. Leveraging prior knowledge from an APTBM model, the DPoD is weakly supervised via modulation-structure-induced constraints for pre-training and further adapted online with few-shot learning to efficiently compensate residual distortions, aided by clipping noise cancellation. Evaluated under a 30-dBc ACLR constraint, the approach achieves reliable transmission with approximately 2 dB of input back-off, outperforming existing learning-based DPoD schemes by over 2 dB while substantially reducing online training overhead and computational complexity.

Abstract

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.

Problem

• power amplifier nonlinearity
• memory effects
• spectral regrowth
• adjacent channel leakage ratio
• distortion compensation

Innovation

• Transfer Learning
• Digital Post-Distortion (DPoD)
• Amplitude-Phase-Time Block Modulation (APTBM)
• Power Amplifier Nonlinearity Compensation
• Transceiver-Cooperative Distortion Mitigation