# Deep Reinforcement Learning for Joint Spectrum and Power Allocation in Cellular Networks

arXiv: https://arxiv.org/abs/2012.10682 · HTML: https://mlsanity.com/paper/2012.10682/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2020.

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

- [Average Reward Reinforcement Learning for Wireless Radio Resource Management](https://mlsanity.com/paper/2501.06700/) (2025)
- [Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNet](https://mlsanity.com/paper/2212.07967/) (2022)
- [Hierarchical Multi-Agent DRL-Based Framework for Joint Multi-RAT Assignment and Dynamic Resource Allocation in Next-Generation HetNets](https://mlsanity.com/paper/2202.13652/) (2022)
- [Generalization of Deep Reinforcement Learning for Jammer-Resilient Frequency and Power Allocation](https://mlsanity.com/paper/2302.02250/) (2023)
- [Cell-Free Data Power Control Via Scalable Multi-Objective Bayesian Optimisation](https://mlsanity.com/paper/2212.10299/) (2022)

## Abstract (arXiv)

A wireless network operator typically divides the radio spectrum it possesses into a number of subbands. In a cellular network those subbands are then reused in many cells. To mitigate co-channel interference, a joint spectrum and power allocation problem is often formulated to maximize a sum-rate objective. The best known algorithms for solving such problems generally require instantaneous global channel state information and a centralized optimizer. In fact those algorithms have not been implemented in practice in large networks with time-varying subbands. Deep reinforcement learning algorithms are promising tools for solving complex resource management problems. A major challenge here is that spectrum allocation involves discrete subband selection, whereas power allocation involves continuous variables. In this paper, a learning framework is proposed to optimize both discrete and continuous decision variables. Specifically, two separate deep reinforcement learning algorithms are designed to be executed and trained simultaneously to maximize a joint objective. Simulation results show that the proposed scheme outperforms both the state-of-the-art fractional programming algorithm and a previous solution based on deep reinforcement learning.