Multi-layer MIMO Relay as Deep Physical Neural Networks: Power Amplifiers as Activation Functions

• URL: http://arxiv.org/abs/2607.18354v1
• Date: Mon, 20 Jul 2026 09:45:07 GMT
• Title: Multi-layer MIMO Relay as Deep Physical Neural Networks: Power Amplifiers as Activation Functions
• Abstract summary: Wireless physical neural networks (WPNNs) embed neural computation directly into analog hardware.<n>We propose a deep WPNN in which nonlinear activations are realized by a multi-hop multiple-input multiple-output (MIMO) relay network.<n>We show that the proposed architecture enables accurate over-the-air inference for image classification.
• Score: 56.870614369768354
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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