Diffractive neural networks, where signal processing is embedded into wave propagation, promise light-speed and energy-efficient computation. However, existing three-dimensional structures, such as stacked intelligent metasurfaces (SIMs), face critical challenges in implementation and integration. In contrast, this work pioneers planar diffractive neural networks (PDNNs) empowered communications, a novel architecture that performs signal processing as signals propagate through artificially designed planar circuits. To demonstrate the capability of PDNN, we propose a PDNN-based space-shift-keying (PDNN-SSK) communication system with a single radio-frequency (RF) chain and a maximum power detector. In this system, PDNNs are deployed at both the transmitter and receiver to jointly execute modulation, beamforming, and detection. We conduct theoretical analyses to provide the maximization condition of correct detection probability and derive the closed-form expression of the symbol error rate (SER) for the proposed system. To approach these theoretical benchmarks, the phase shift parameters of PDNNs are optimized using a surrogate model-based training approach, which effectively navigates the high-dimensional, non-convex optimization landscape. Extensive simulations verify the theoretical analysis framework and uncover fundamental design principles for the PDNN architecture, highlighting its potential to revolutionize RF front-ends by replacing conventional digital baseband modules with this integrable RF computing platform.

翻译：衍射神经网络将信号处理嵌入波传播过程，有望实现光速且高能效的计算。然而，现有的三维结构（如堆叠智能超表面）在实现与集成方面面临严峻挑战。相比之下，本研究开创性地提出平面衍射神经网络（PDNN）赋能通信架构，该新颖结构通过人工设计的平面电路在信号传播过程中执行信号处理。为展示PDNN的能力，我们提出一种基于PDNN的空间移位键控（PDNN-SSK）通信系统，该系统采用单射频链与最大功率检测器。在该系统中，发射端与接收端均部署PDNN，以协同执行调制、波束成形与检测。我们通过理论分析提供了正确检测概率的最大化条件，并推导出所提系统符号错误率的闭式表达式。为逼近这些理论界限，采用基于代理模型的训练方法优化PDNN的相移参数，该方法能有效应对高维非凸优化空间。大量仿真验证了理论分析框架，揭示了PDNN架构的基础设计原则，凸显其通过可集成的射频计算平台替代传统数字基带模块、从而革新射频前端的潜力。