This paper introduces AirCNN, a novel paradigm for implementing convolutional neural networks (CNNs) via over-the-air (OTA) analog computation. By leveraging multiple reconfigurable intelligent surfaces (RISs) and transceiver designs, we engineer the ambient wireless propagation environment to emulate the operations of a CNN layer. To comprehensively evaluate AirCNN, we consider two types of CNNs, namely classic two-dimensional (2D) convolution (Conv2d) and light-weight convolution, i.e., depthwise separable convolution (ConvSD). For Conv2d realization via OTA computation, we propose and analyze two RIS-aided transmission architectures: multiple-input multiple-output (MIMO) and multiple-input single-output (MISO), balancing transmission overhead and emulation performance. We jointly optimize all parameters, including the transmitter precoder, receiver combiner, and RIS phase shifts, under practical constraints such as transmit power budget and unit-modulus phase shift requirements. We further extend the framework to ConvSD, which requires distinct transmission strategies for depthwise and pointwise convolutions. Simulation results demonstrate that the proposed AirCNN architectures can achieve satisfactory classification performance. Notably, Conv2d MISO consistently outperforms Conv2d MIMO across various settings, while for ConvSD, MISO is superior only under poor channel conditions. Moreover, employing multiple RISs significantly enhances performance compared to a single RIS, especially in line-of-sight (LoS)-dominated wireless environments.

翻译：本文提出AirCNN，一种通过空中模拟计算实现卷积神经网络的新型范式。通过利用多个可重构智能表面与收发器设计，我们对环境无线传播信道进行调控以模拟CNN层的运算。为全面评估AirCNN，我们考虑两种CNN类型：经典二维卷积与轻量化卷积（即深度可分离卷积）。针对空中计算实现的二维卷积，我们提出并分析了两种RIS辅助传输架构：多输入多输出与多输入单输出，以权衡传输开销与模拟性能。我们在发射功率预算与单位模值相位约束等实际条件下，联合优化发射预编码器、接收合并器及RIS相移等所有参数。进一步将该框架扩展至深度可分离卷积，其深度卷积与逐点卷积需要采用不同的传输策略。仿真结果表明，所提出的AirCNN架构能够取得令人满意的分类性能。值得注意的是，在不同设置下二维卷积多输入单输出架构始终优于多输入多输出架构，而对于深度可分离卷积，多输入单输出仅在恶劣信道条件下表现更优。此外，与单RIS相比，采用多RIS能显著提升性能，尤其在视距主导的无线环境中。