Planar Diffractive Neural Networks Empowered Communications: A Spatial Modulation Scheme

TL;DR

This paper introduces planar diffractive neural networks (PDNNs) for communication systems, enabling integrated, wave-based signal processing that simplifies hardware and enhances efficiency, with theoretical analysis and simulation validation.

Contribution

It pioneers the design of planar diffractive neural networks for communication, demonstrating their application in a space-shift-keying system with joint modulation, beamforming, and detection.

Findings

Theoretical analysis of detection probability and SER for PDNN-based systems.

Optimization of phase shift parameters using surrogate model training.

Simulations confirm the effectiveness and potential of PDNNs in RF communication.

Abstract

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.