The explosion of the number of low-power devices in the next decades calls for a re-thinking of wireless network design, namely, unifying wireless transmission of information and power so as to make the best use of the RF spectrum, radiation, and infrastructure for the dual purpose of communicating and energizing. This article provides a novel learning-based approach towards such wireless network design. To that end, a parametric model of a practical energy harvester, accounting for various sources of nonlinearities, is proposed using a nonlinear regression algorithm applied over collected real data. Relying on the proposed model, the learning problem of modulation design for Simultaneous Wireless Information-Power Transmission (SWIPT) over a point-to-point link is studied. Joint optimization of the transmitter and the receiver is implemented using Neural Network (NN)-based autoencoders. The results reveal that by increasing the receiver power demand, the baseband transmit modulation constellation converges to an On-Off keying signalling. Utilizing the observations obtained via learning, an algorithmic SWIPT modulation design is proposed. It is observed via numerical results that the performance loss of the proposed modulations are negligible compared to the ones obtained from learning. Extension of the studied problem to learning modulation design for multi-user SWIPT scenarios and coded modulation design for point-to-point SWIPT are considered. The major conclusion of this work is to utilize learning-based results to design non learning-based algorithms, which perform as well. In particular, inspired by the results obtained via learning, an algorithmic approach for coded modulation design is proposed, which performs very close to its learning counterparts, and is significantly superior due to its high real-time adaptability to new system design parameters.

中文翻译：

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学习沟通和激励：无线信息电力传输的调制、编码和多址设计

未来几十年低功耗设备数量的爆炸式增长需要重新思考无线网络设计，即统一信息和功率的无线传输，以充分利用射频频谱、辐射和基础设施具有沟通和激励的双重目的。本文为此类无线网络设计提供了一种新颖的基于学习的方法。为此，使用应用于收集的真实数据的非线性回归算法，提出了考虑各种非线性源的实用能量收集器的参数模型。基于所提出的模型，研究了点对点链路上同时无线信息功率传输（SWIPT）调制设计的学习问题。发射器和接收器的联合优化是使用基于神经网络 (NN) 的自动编码器实现的。结果表明，通过增加接收器功率需求，基带发射调制星座会收敛到开关键控信令。利用通过学习获得的观察结果，提出了一种算法 SWIPT 调制设计。通过数值结果观察到，与从学习中获得的那些相比，所提出的调制的性能损失可以忽略不计。考虑将所研究的问题扩展到学习多用户 SWIPT 场景的调制设计和点对点 SWIPT 的编码调制设计。这项工作的主要结论是利用基于学习的结果来设计非基于学习的算法，这些算法也表现良好。特别是，