Learning Multitone Modulation for Nonlinear Dual-Unified SWIPT Receivers

Abstract

Unified receivers (URs) enable simultaneous wireless information and power transfer (SWIPT) by reusing rectified signals for both information decoding and energy harvesting (EH). Dual UR-SWIPT architectures extend this concept by producing two rectified outputs of opposite polarity. However, existing dual UR modulation schemes rely on heuristic scalar decision rules, which can be suboptimal. This letter studies the design of multitone-based modulation schemes for nonlinear dual UR-SWIPT, with the objective of minimizing the symbol error rate (SER) under average transmit power and EH constraints. Since the resulting problem is analytically intractable, we propose an autoencoder-based design that embeds the nonlinear dual UR-SWIPT channel as a fixed differentiable layer. Our framework enables the joint learning of multitone parameters and a decoding rule that operates directly on the noisy two-dimensional output, while also supporting EH-aware training to satisfy an EH requirement. Numerical results show that the learned modulations achieve 3.5 dB gain at a given SER over the baseline schemes, while the EH-aware design ensures an improved rate-energy tradeoff. The learned waveforms reveal a clear structure and exploit the output geometry for improved symbol separation.