Resource Allocation in Hybrid Radio-Optical IoT Networks using GNN with Multi-task Learning

• URL: http://arxiv.org/abs/2511.07428v1
• Date: Wed, 29 Oct 2025 15:02:28 GMT
• Title: Resource Allocation in Hybrid Radio-Optical IoT Networks using GNN with Multi-task Learning
• Authors: Aymen Hamrouni, Sofie Pollin, Hazem Sallouha,
• Abstract summary: This paper addresses the problem of dual-technology scheduling in hybrid Internet of Things (IoT) networks that integrate Optical NeuralOWC and Radio Frequency (RF)<n>We propose a supervised multi-task learning architecture combining a two-stage Graph Embedding with Transformer (DGET) framework.<n>The proposed framework achieves near-optimal scheduling with over 90% classification accuracy, reduces computational complexity, and demonstrates higher robustness under partial channel observability.
• Score: 11.833896722352568
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This paper addresses the problem of dual-technology scheduling in hybrid Internet of Things (IoT) networks that integrate Optical Wireless Communication (OWC) alongside Radio Frequency (RF). We begin by formulating a Mixed-Integer Nonlinear Programming (MINLP) model that jointly considers throughput maximization and delay minimization between access points and IoT nodes under energy and link availability constraints. However, given the intractability of solving such NP-hard problems at scale and the impractical assumption of full channel observability, we propose the Dual-Graph Embedding with Transformer (DGET) framework, a supervised multi-task learning architecture combining a two-stage Graph Neural Networks (GNNs) with a Transformer-based encoder. The first stage employs a transductive GNN that encodes the known graph topology and initial node and link states. The second stage introduces an inductive GNN for temporal refinement, which learns to generalize these embeddings to the evolved states of the same network, capturing changes in energy and queue dynamics over time, by aligning them with ground-truth scheduling decisions through a consistency loss. These enriched embeddings are then processed by a classifier for the communication links with a Transformer encoder that captures cross-link dependencies through multi-head self-attention via classification loss. Simulation results show that hybrid RF-OWC networks outperform standalone RF systems by handling higher traffic loads more efficiently and reducing the Age of Information (AoI) by up to 20%, all while maintaining comparable energy consumption. The proposed DGET framework, compared to traditional optimization-based methods, achieves near-optimal scheduling with over 90% classification accuracy, reduces computational complexity, and demonstrates higher robustness under partial channel observability.

Related papers

• Unrolled Neural Networks for Constrained Optimization [83.29547301151177]
  Our framework comprises two coupled neural networks that jointly approximate the saddle point of the Lagrangian.<n>We numerically evaluate the framework on mixed-integer quadratic programs and power allocation in wireless networks.
  arXiv  Detail & Related papers  (2026-01-24T03:12:41Z)
• Distributed Link Sparsification for Scalable Scheduling Using Graph Neural Networks (Journal Version) [50.894272363373126]
  In wireless networks characterized by dense connectivity, the significant signaling overhead generated by distributed link scheduling algorithms can exacerbate issues like congestion, energy consumption, and radio footprint expansion.<n>We propose a distributed link sparsification scheme employing graph neural networks (GNNs) to reduce scheduling overhead for delay-tolerant traffic while maintaining network capacity.<n>A GNN module is trained to adjust contention thresholds for individual links based on traffic statistics and network topology, enabling links to withdraw from scheduling contention when they are unlikely to succeed.
  arXiv  Detail & Related papers  (2025-09-05T18:59:14Z)
• BHViT: Binarized Hybrid Vision Transformer [53.38894971164072]
  Model binarization has made significant progress in enabling real-time and energy-efficient computation for convolutional neural networks (CNN)<n>We propose BHViT, a binarization-friendly hybrid ViT architecture and its full binarization model with the guidance of three important observations.<n>Our proposed algorithm achieves SOTA performance among binary ViT methods.
  arXiv  Detail & Related papers  (2025-03-04T08:35:01Z)
• Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks [55.467288506826755]
  Federated learning (FL) has been recognized as a viable solution for local-privacy-aware collaborative model training in wireless edge networks.<n>Most existing communication-efficient FL algorithms fail to reduce the significant inter-device variance.<n>We propose a novel communication-efficient FL algorithm, named FedQVR, which relies on a sophisticated variance-reduced scheme.
  arXiv  Detail & Related papers  (2025-01-20T04:26:21Z)
• FusionLLM: A Decentralized LLM Training System on Geo-distributed GPUs with Adaptive Compression [55.992528247880685]
  Decentralized training faces significant challenges regarding system design and efficiency. We present FusionLLM, a decentralized training system designed and implemented for training large deep neural networks (DNNs) We show that our system and method can achieve 1.45 - 9.39x speedup compared to baseline methods while ensuring convergence.
  arXiv  Detail & Related papers  (2024-10-16T16:13:19Z)
• Multi-Flow Transmission in Wireless Interference Networks: A Convergent Graph Learning Approach [9.852567834643292]
  We introduce a novel algorithm called Dual-stage Interference-Aware Multi-flow Optimization of Network Data-signals (DIAMOND) A centralized stage computes the multi-flow transmission strategy using a novel design of graph neural network (GNN) reinforcement learning (RL) routing agent. Then, a distributed stage improves the performance based on a novel design of distributed learning updates.
  arXiv  Detail & Related papers  (2023-03-27T18:49:47Z)
• Graph Neural Networks for Power Allocation in Wireless Networks with Full Duplex Nodes [10.150768420975155]
  Due to mutual interference between users, power allocation problems in wireless networks are often non-trivial. Graph Graph neural networks (GNNs) have recently emerged as a promising approach tackling these problems and an approach exploits underlying topology of wireless networks.
  arXiv  Detail & Related papers  (2023-03-27T10:59:09Z)
• ENGNN: A General Edge-Update Empowered GNN Architecture for Radio Resource Management in Wireless Networks [29.23937571816269]
  A key task is to efficiently manage the radio resource by judicious beamforming and power allocation. We propose an edge-update mechanism, which enables GNNs to handle both node and edge variables. The proposed method achieves higher sum rate but with much shorter time than state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-12-14T14:04:25Z)
• Non-Coherent Over-the-Air Decentralized Gradient Descent [0.0]
  Implementing Decentralized Gradient Descent in wireless systems is challenging due to noise, fading, and limited bandwidth. This paper introduces a scalable DGD algorithm that eliminates the need for scheduling, topology information, or CSI.
  arXiv  Detail & Related papers  (2022-11-19T19:15:34Z)
• Federated Learning for Energy-limited Wireless Networks: A Partial Model Aggregation Approach [79.59560136273917]
  limited communication resources, bandwidth and energy, and data heterogeneity across devices are main bottlenecks for federated learning (FL) We first devise a novel FL framework with partial model aggregation (PMA) The proposed PMA-FL improves 2.72% and 11.6% accuracy on two typical heterogeneous datasets.
  arXiv  Detail & Related papers  (2022-04-20T19:09:52Z)
• Scalable Power Control/Beamforming in Heterogeneous Wireless Networks with Graph Neural Networks [6.631773993784724]
  We propose a novel unsupervised learning-based framework named heterogeneous interference graph neural network (HIGNN) to handle these challenges. HIGNN is scalable to wireless networks of growing sizes with robust performance after trained on small-sized networks.
  arXiv  Detail & Related papers  (2021-04-12T13:36:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.