多层MIMO中继作为深度物理神经网络：以功率放大器作为激活函数
Multi-layer MIMO Relay as Deep Physical Neural Networks: Power Amplifiers as Activation Functions
- Department of Electrical and Electronic Engineering, Imperial College London（伦敦帝国理工学院电气与电子工程系）
- Faculty of Engineering, Bar-Ilan University（巴伊兰大学工程学院）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本文提出用多跳MIMO中继网络构建深度无线物理神经网络，以功率放大器非线性为激活函数，实现可端到端训练的空中全连接网络，针对不同CSI场景开发两种收发器设计，仿真显示该架构能准确进行图像分类，凸显利用硬件非线性增强推理能力的优势。
中文摘要 AI 辅助
无线物理神经网络（WPNNs）将神经计算直接嵌入模拟硬件，比传统数字实现具有更低的能耗和延迟。本文提出一种深度WPNN，其中非线性激活由多跳多输入多输出（MIMO）中继网络实现，每个中继实现可训练的复线性增益和偏置，随后功率放大器的固有非线性充当激活函数。多个中继的级联实现了一个可端到端训练参数的空中全连接网络。针对不同信道状态信息（CSI）可用性场景开发了两种收发器设计：一种基于最小二乘法（LS）的方案，仅需接收端CSI；另一种基于奇异值分解（SVD）的方案，需发送端和接收端CSI。仿真结果表明，所提出的架构能够实现用于图像分类的准确空中推理。特别是，结果突出了利用硬件非线性增强推理能力的优势。
英文摘要
Wireless physical neural networks (WPNNs) embed neural computation directly into analog hardware, offering lower energy consumption and latency than conventional digital implementations. In this paper, we propose a deep WPNN in which nonlinear activations are realized by a multi-hop multiple-input multiple-output (MIMO) relay network, in which each relay implements a trainable complex linear gain and bias, followed by the power amplifier's intrinsic nonlinearity acting as an activation function. The cascade of multiple relays therefore realizes an over-the-air fully connected network whose parameters can be trained end-to-end. We develop two transceiver designs for different channel state information (CSI) availability scenarios: a least squares (LS)-based scheme requiring only receiver-side CSI, and a singular-value-decomposition (SVD)-based scheme requiring both transmitter-side and receiver-side CSI. Simulation results show that the proposed architecture enables accurate over-the-air inference for image classification. In particular, the results highlight the advantage of exploiting hardware nonlinearity for enhanced inference capability.
