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

arXiv: https://arxiv.org/abs/2608.08554 · HTML: https://mlsanity.com/paper/2608.08554/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 37 references that are in the map.
- First posted on arXiv 9 Aug 2026.

## Builds on (most cited first, 4 of 4)

- [First 20 Years of Green Radios](https://mlsanity.com/paper/1908.07696/) (2019)
- [3D Polarized Modulation: System Analysis and Performance](https://mlsanity.com/paper/2007.07675/) (2020)
- [An Efficient QAM Detector via Nonlinear Post-distortion based on FDE Bank under PA Impairments](https://mlsanity.com/paper/2010.15940/) (2020)
- [Two-Stage Signal Reconstruction for Amplitude-Phase-Time Block Modulation-based Communications](https://mlsanity.com/paper/2512.18326/) (2025)

## Abstract (arXiv)

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.