# Spatial Deep Learning for Wireless Scheduling

arXiv: https://arxiv.org/abs/1808.01486 · HTML: https://mlsanity.com/paper/1808.01486/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2018.

## Built on by (most cited first, 30 of 32)

- [Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications](https://mlsanity.com/paper/2111.12444/) (2021)
- [Link Scheduling using Graph Neural Networks](https://mlsanity.com/paper/2109.05536/) (2021)
- [Concave Utility Reinforcement Learning with Zero-Constraint Violations](https://mlsanity.com/paper/2109.05439/) (2021)
- [Decentralized Inference with Graph Neural Networks in Wireless Communication Systems](https://mlsanity.com/paper/2104.09027/) (2021)
- [Scalable Power Control/Beamforming in Heterogeneous Wireless Networks with Graph Neural Networks](https://mlsanity.com/paper/2104.05463/) (2021)
- [Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges](https://mlsanity.com/paper/2309.07438/) (2023)
- [Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspective](https://mlsanity.com/paper/2105.01696/) (2021)
- [Expert-Calibrated Learning for Online Optimization with Switching Costs](https://mlsanity.com/paper/2204.08572/) (2022)
- [Sobolev Spaces, Kernels and Discrepancies over Hyperspheres](https://mlsanity.com/paper/2211.09196/) (2022)
- [Privacy-Preserving Decentralized Inference with Graph Neural Networks in Wireless Networks](https://mlsanity.com/paper/2208.06963/) (2022)
- [ENGNN: A General Edge-Update Empowered GNN Architecture for Radio Resource Management in Wireless Networks](https://mlsanity.com/paper/2301.00757/) (2022)
- [Uncertainty Injection: A Deep Learning Method for Robust Optimization](https://mlsanity.com/paper/2302.12304/) (2023)
- [Two-Stage Stochastic Optimization via Primal-Dual Decomposition and Deep Unrolling](https://mlsanity.com/paper/2105.01853/) (2021)
- [Opportunistic Routing in Wireless Communications via Learnable State-Augmented Policies](https://mlsanity.com/paper/2503.03736/) (2025)
- [Learning to Optimize Under Constraints with Unsupervised Deep Neural Networks](https://mlsanity.com/paper/2101.00744/) (2021)
- [Learning Autonomy in Management of Wireless Random Networks](https://mlsanity.com/paper/2106.07984/) (2021)
- [Machine Learning Methods for Management UAV Flocks -- a Survey](https://mlsanity.com/paper/2108.13448/) (2021)
- [Machine Learning-Based User Scheduling in Integrated Satellite-HAPS-Ground Networks](https://mlsanity.com/paper/2205.13958/) (2022)
- [Power Control for 6G Industrial Wireless Subnetworks: A Graph Neural Network Approach](https://mlsanity.com/paper/2212.14051/) (2022)
- [Wireless Power Control Based on Large Language Models](https://mlsanity.com/paper/2603.00474/) (2026)
- [A Graph Foundation Model for Wireless Resource Allocation](https://mlsanity.com/paper/2604.07390/) (2026)
- [Learning State-Augmented Policies for Information Routing in Communication Networks](https://mlsanity.com/paper/2310.00248/) (2023)
- [Spatial Deep Learning for Site-Specific Movement Optimization of Aerial Base Stations](https://mlsanity.com/paper/2312.10490/) (2023)
- [Self-Improving Interference Management Based on Deep Learning With Uncertainty Quantification](https://mlsanity.com/paper/2401.13206/) (2024)
- [Graph Neural Network based scheduling : Improved throughput under a generalized interference model](https://mlsanity.com/paper/2111.00459/) (2021)
- [Informed Learning by Wide Neural Networks: Convergence, Generalization and Sampling Complexity](https://mlsanity.com/paper/2207.00751/) (2022)
- [Learning Cellular Coverage from Real Network Configurations using GNNs](https://mlsanity.com/paper/2304.10328/) (2023)
- [A Deep Learning Based Resource Allocator for Communication Networks with Dynamic User Utility Demands](https://mlsanity.com/paper/2311.04600/) (2023)
- [Transfer Learning with Reconstruction Loss](https://mlsanity.com/paper/2404.00505/) (2024)
- [GRLinQ: An Intelligent Spectrum Sharing Mechanism for Device-to-Device Communications with Graph Reinforcement Learning](https://mlsanity.com/paper/2408.09394/) (2024)

## Abstract (arXiv)

The optimal scheduling of interfering links in a dense wireless network with full frequency reuse is a challenging task. The traditional method involves first estimating all the interfering channel strengths then optimizing the scheduling based on the model. This model-based method is however resource intensive and computationally hard because channel estimation is expensive in dense networks; furthermore, finding even a locally optimal solution of the resulting optimization problem may be computationally complex. This paper shows that by using a deep learning approach, it is possible to bypass the channel estimation and to schedule links efficiently based solely on the geographic locations of the transmitters and the receivers, due to the fact that in many propagation environments, the wireless channel strength is largely a function of the distance dependent path-loss. This is accomplished by unsupervised training over randomly deployed networks, and by using a novel neural network architecture that computes the geographic spatial convolutions of the interfering or interfered neighboring nodes along with subsequent multiple feedback stages to learn the optimum solution. The resulting neural network gives near-optimal performance for sum-rate maximization and is capable of generalizing to larger deployment areas and to deployments of different link densities. Moreover, to provide fairness, this paper proposes a novel scheduling approach that utilizes the sum-rate optimal scheduling algorithm over judiciously chosen subsets of links for maximizing a proportional fairness objective over the network. The proposed approach shows highly competitive and generalizable network utility maximization results.