# Energy-Efficient Subchannel and Power Allocation for HetNets Based on Convolutional Neural Network

arXiv: https://arxiv.org/abs/1903.00165 · HTML: https://mlsanity.com/paper/1903.00165/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2019.

## Built on by (most cited first, 5 of 5)

- [Link Scheduling using Graph Neural Networks](https://mlsanity.com/paper/2109.05536/) (2021)
- [Graph-based Algorithm Unfolding for Energy-aware Power Allocation in Wireless Networks](https://mlsanity.com/paper/2201.11799/) (2022)
- [Opportunistic Routing in Wireless Communications via Learnable State-Augmented Policies](https://mlsanity.com/paper/2503.03736/) (2025)
- [Learning State-Augmented Policies for Information Routing in Communication Networks](https://mlsanity.com/paper/2310.00248/) (2023)
- [Graph Neural Network based scheduling : Improved throughput under a generalized interference model](https://mlsanity.com/paper/2111.00459/) (2021)

## Abstract (arXiv)

Heterogeneous network (HetNet) has been proposed as a promising solution for handling the wireless traffic explosion in future fifth-generation (5G) system. In this paper, a joint subchannel and power allocation problem is formulated for HetNets to maximize the energy efficiency (EE). By decomposing the original problem into a classification subproblem and a regression subproblem, a convolutional neural network (CNN) based approach is developed to obtain the decisions on subchannel and power allocation with a much lower complexity than conventional iterative methods. Numerical results further demonstrate that the proposed CNN can achieve similar performance as the Exhaustive method, while needs only 6.76% of its CPU runtime.