Planar Diffractive Neural Networks Empowered Communications: A Spatial Modulation Scheme

2025-11-30

AI Summary

To address the challenges of complex integration and high cost associated with three-dimensional smart intelligent metasurfaces (SIMs), this paper pioneers the integration of planar diffractive neural networks (PDNNs) into wireless communication systems, proposing a PDNN-assisted spatial shift keying (SSK) modulation architecture. It employs passively engineered planar circuits at the RF front-end to perform signal modulation, beamforming, and detection directly in the analog domain—enabling light-speed, zero-power baseband computation. The work innovatively combines SSK with surrogate-model-driven phase optimization and establishes a rigorous theoretical framework. We derive necessary and sufficient conditions for maximizing correct detection probability and obtain a closed-form expression for the symbol error rate (SER). Simulation results validate the architecture’s efficiency under single-RF-chain operation and uncover fundamental design principles for deploying PDNNs in wireless communications.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

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.

Problem

• Proposes a planar diffractive neural network for RF signal processing
• Develops a spatial modulation system using PDNNs for joint modulation and detection
• Optimizes PDNN parameters to approach theoretical performance benchmarks

Innovation

• Planar diffractive neural networks for signal processing
• Single RF chain with maximum power detection
• Surrogate model-based training for phase optimization