# Deep Learning for Distributed Optimization: Applications to Wireless Resource Management

arXiv: https://arxiv.org/abs/1905.13378 · HTML: https://mlsanity.com/paper/1905.13378/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2019.

## Built on by (most cited first, 14 of 14)

- [Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications](https://mlsanity.com/paper/2111.12444/) (2021)
- [A Bipartite Graph Neural Network Approach for Scalable Beamforming Optimization](https://mlsanity.com/paper/2207.05364/) (2022)
- [Learning Robust Beamforming for MISO Downlink Systems](https://mlsanity.com/paper/2103.01602/) (2021)
- [Two-Stage Stochastic Optimization via Primal-Dual Decomposition and Deep Unrolling](https://mlsanity.com/paper/2105.01853/) (2021)
- [Learning Optimal Fronthauling and Decentralized Edge Computation in Fog Radio Access Networks](https://mlsanity.com/paper/2103.11284/) (2021)
- [Learning Autonomy in Management of Wireless Random Networks](https://mlsanity.com/paper/2106.07984/) (2021)
- [Deep Learning Based Resource Assignment for Wireless Networks](https://mlsanity.com/paper/2109.12970/) (2021)
- [Limited Communications Distributed Optimization via Deep Unfolded Distributed ADMM](https://mlsanity.com/paper/2309.14353/) (2023)
- [Task-Oriented Edge Networks: Decentralized Learning Over Wireless Fronthaul](https://mlsanity.com/paper/2312.01288/) (2023)
- [Logarithmically Quantized Distributed Optimization over Dynamic Multi-Agent Networks](https://mlsanity.com/paper/2410.20345/) (2024)
- [Team Deep Mixture of Experts for Distributed Power Control](https://mlsanity.com/paper/2007.14147/) (2020)
- [Model-Free Learning of Optimal Deterministic Resource Allocations in Wireless Systems via Action-Space Exploration](https://mlsanity.com/paper/2108.10352/) (2021)
- [Large Language Models for Knowledge-Free Network Management: Feasibility Study and Opportunities](https://mlsanity.com/paper/2410.17259/) (2024)
- [Heterogeneous Resource Allocation with Multi-task Learning for Wireless Networks](https://mlsanity.com/paper/2502.10027/) (2025)

## Abstract (arXiv)

This paper studies a deep learning (DL) framework to solve distributed non-convex constrained optimizations in wireless networks where multiple computing nodes, interconnected via backhaul links, desire to determine an efficient assignment of their states based on local observations. Two different configurations are considered: First, an infinite-capacity backhaul enables nodes to communicate in a lossless way, thereby obtaining the solution by centralized computations. Second, a practical finite-capacity backhaul leads to the deployment of distributed solvers equipped along with quantizers for communication through capacity-limited backhaul. The distributed nature and the nonconvexity of the optimizations render the identification of the solution unwieldy. To handle them, deep neural networks (DNNs) are introduced to approximate an unknown computation for the solution accurately. In consequence, the original problems are transformed to training tasks of the DNNs subject to non-convex constraints where existing DL libraries fail to extend straightforwardly. A constrained training strategy is developed based on the primal-dual method. For distributed implementation, a novel binarization technique at the output layer is developed for quantization at each node. Our proposed distributed DL framework is examined in various network configurations of wireless resource management. Numerical results verify the effectiveness of our proposed approach over existing optimization techniques.