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：本文提出了一种新颖的范式——AirCNN，通过空口模拟计算实现卷积神经网络。通过利用多个可重构智能表面和收发器设计，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，我们考虑两种类型的CNN：经典二维卷积和轻量级卷积（即深度可分离卷积）。对于通过空口计算实现二维卷积，我们提出并分析了两种RIS辅助的传输架构：多输入多输出和多输入单输出，以权衡传输开销与模拟性能。我们在实际约束条件下（如发射功率预算和单位模值相移要求）联合优化所有参数，包括发射预编码器、接收合并器和RIS相移。我们进一步将该框架扩展至深度可分离卷积，其需要针对深度卷积和逐点卷积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，二维卷积的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，多输入单输出架构仅在信道条件较差时表现更优。此外，与使用单个RIS相比，采用多个RIS能显著提升性能，尤其在视距主导的无线环境中。