基于幅度-相位-时间块调制的非线性单载波无线通信中，启用迁移学习的失真补偿

中文摘要 AI 辅助

功率放大器（PA）的非线性和记忆效应严重限制了通信系统的频谱合规性、可靠性和能效。为解决该问题，我们提出一种启用迁移学习的全数字收发信机协同方法，用于相邻信道泄漏比（ACLR）约束下基于幅度-相位-时间块调制（APTBM）的非线性单载波传输。在发射端，迭代限幅滤波（ICAF）和静态数字预失真（SDPD）协同作用，以降低信号峰均比并抑制频谱再生，且无需宽带反馈。在接收端，APTBM固有的幅度-相位约束为离线逆模型预训练提供弱监督先验知识，随后对轻量级数字后失真（DPoD）网络进行在线少样本自适应。接着，级联的DPoD和限幅噪声抵消方案系统补偿由PA和ICAF共同引起的残余失真。仿真与实测结果表明，在30-dBc的ACLR约束下，输入回退约2 dB时可实现可靠传输；此外，所提DPoD方法显著减少了在线训练时间和计算开销，相比传统基于学习的DPoD方案，性能增益超过2 dB。

英文摘要

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.

发表机构

• Southeast University（东南大学）
• Purple Mountain Laboratories（紫金山实验室）
• Nanjing University of Posts and Telecommunications（南京邮电大学）

机构由 AI 辅助整理，请以论文原文为准。

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13 pages, 14 figures, 1 table