# Towards Optimal Power Control via Ensembling Deep Neural Networks

arXiv: https://arxiv.org/abs/1807.10025 · HTML: https://mlsanity.com/paper/1807.10025/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2018.

## Built on by (most cited first, 30 of 38)

- [Graph Neural Networks for Wireless Communications: From Theory to Practice](https://mlsanity.com/paper/2203.10800/) (2022)
- [Applications and Techniques for Fast Machine Learning in Science](https://mlsanity.com/paper/2110.13041/) (2021)
- [Scalable Power Control/Beamforming in Heterogeneous Wireless Networks with Graph Neural Networks](https://mlsanity.com/paper/2104.05463/) (2021)
- [Modular Meta-Learning for Power Control via Random Edge Graph Neural Networks](https://mlsanity.com/paper/2108.13178/) (2021)
- [Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspective](https://mlsanity.com/paper/2105.01696/) (2021)
- [Convolutional Learning on Multigraphs](https://mlsanity.com/paper/2209.11354/) (2022)
- [A Bipartite Graph Neural Network Approach for Scalable Beamforming Optimization](https://mlsanity.com/paper/2207.05364/) (2022)
- [Expert-Calibrated Learning for Online Optimization with Switching Costs](https://mlsanity.com/paper/2204.08572/) (2022)
- [Deep Graph Unfolding for Beamforming in MU-MIMO Interference Networks](https://mlsanity.com/paper/2304.00446/) (2023)
- [Generative Diffusion Models for Resource Allocation in Wireless Networks](https://mlsanity.com/paper/2504.20277/) (2025)
- [ENGNN: A General Edge-Update Empowered GNN Architecture for Radio Resource Management in Wireless Networks](https://mlsanity.com/paper/2301.00757/) (2022)
- [Graph Neural Networks for Power Allocation in Wireless Networks with Full Duplex Nodes](https://mlsanity.com/paper/2303.16113/) (2023)
- [Scalable Resource Management for Dynamic MEC: An Unsupervised Link-Output Graph Neural Network Approach](https://mlsanity.com/paper/2306.08938/) (2023)
- [Advancing RAN Slicing with Offline Reinforcement Learning](https://mlsanity.com/paper/2312.10547/) (2023)
- [Deep Learning-based Power Control for Cell-Free Massive MIMO Networks](https://mlsanity.com/paper/2102.10366/) (2021)
- [Deep Unsupervised Learning for Generalized Assignment Problems: A Case-Study of User-Association in Wireless Networks](https://mlsanity.com/paper/2103.14548/) (2021)
- [Learn to Communicate with Neural Calibration: Scalability and Generalization](https://mlsanity.com/paper/2110.00272/) (2021)
- [Learning for Robust Combinatorial Optimization: Algorithm and Application](https://mlsanity.com/paper/2112.10377/) (2021)
- [Flex-Net: A Graph Neural Network Approach to Resource Management in Flexible Duplex Networks](https://mlsanity.com/paper/2301.11166/) (2023)
- [GNN-Based Joint Channel and Power Allocation in Heterogeneous Wireless Networks](https://mlsanity.com/paper/2408.03957/) (2024)
- [Learning Autonomy in Management of Wireless Random Networks](https://mlsanity.com/paper/2106.07984/) (2021)
- [Machine Learning-Based User Scheduling in Integrated Satellite-HAPS-Ground Networks](https://mlsanity.com/paper/2205.13958/) (2022)
- [Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNet](https://mlsanity.com/paper/2212.07967/) (2022)
- [Power Control for 6G Industrial Wireless Subnetworks: A Graph Neural Network Approach](https://mlsanity.com/paper/2212.14051/) (2022)
- [Power Control with QoS Guarantees: A Differentiable Projection-based Unsupervised Learning Framework](https://mlsanity.com/paper/2306.01787/) (2023)
- [Accelerating Graph Neural Networks via Edge Pruning for Power Allocation in Wireless Networks](https://mlsanity.com/paper/2305.12639/) (2023)
- [Unsupervised Learning-Based Joint Resource Allocation and Beamforming Design for RIS-Assisted MISO-OFDMA Systems](https://mlsanity.com/paper/2506.22448/) (2025)
- [To Supervise or Not: How to Effectively Learn Wireless Interference Management Models?](https://mlsanity.com/paper/2112.14011/) (2021)
- [Informed Learning by Wide Neural Networks: Convergence, Generalization and Sampling Complexity](https://mlsanity.com/paper/2207.00751/) (2022)
- [A Deep Learning Based Resource Allocator for Communication Networks with Dynamic User Utility Demands](https://mlsanity.com/paper/2311.04600/) (2023)

## Abstract (arXiv)

A deep neural network (DNN) based power control method is proposed, which aims at solving the non-convex optimization problem of maximizing the sum rate of a multi-user interference channel. Towards this end, we first present PCNet, which is a multi-layer fully connected neural network that is specifically designed for the power control problem. PCNet takes the channel coefficients as input and outputs the transmit power of all users. A key challenge in training a DNN for the power control problem is the lack of ground truth, i.e., the optimal power allocation is unknown. To address this issue, PCNet leverages the unsupervised learning strategy and directly maximizes the sum rate in the training phase. Observing that a single PCNet does not globally outperform the existing solutions, we further propose ePCNet, a network ensemble with multiple PCNets trained independently. Simulation results show that for the standard symmetric multi-user Gaussian interference channel, ePCNet can outperform all state-of-the-art power control methods by 1.2%-4.6% under a variety of system configurations. Furthermore, the performance improvement of ePCNet comes with a reduced computational complexity.