# Deep Reinforcement Learning for 5G Networks: Joint Beamforming, Power Control, and Interference Coordination

arXiv: https://arxiv.org/abs/1907.00123 · HTML: https://mlsanity.com/paper/1907.00123/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2019.

## Built on by (most cited first, 18 of 18)

- [UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach](https://mlsanity.com/paper/2106.15734/) (2021)
- [Adversarial Machine Learning for 5G Communications Security](https://mlsanity.com/paper/2101.02656/) (2021)
- [Multi-hop RIS-Empowered Terahertz Communications: A DRL-based Hybrid Beamforming Design](https://mlsanity.com/paper/2101.09137/) (2021)
- [Deep Learning Assisted Calibrated Beam Training for Millimeter-Wave Communication Systems](https://mlsanity.com/paper/2101.05206/) (2021)
- [Transmit Power Control for Indoor Small Cells: A Method Based on Federated Reinforcement Learning](https://mlsanity.com/paper/2209.13536/) (2022)
- [Real-Time Black-Box Optimization for Dynamic Discrete Environments Using Embedded Ising Machines](https://mlsanity.com/paper/2506.16924/) (2025)
- [A Machine Learning Based Algorithm for Joint Improvement of Power Control, link adaptation, and Capacity in Beyond 5G Communication systems](https://mlsanity.com/paper/2201.07090/) (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)
- [Deep Reinforcement Learning for Power Control in Next-Generation WiFi Network Systems](https://mlsanity.com/paper/2211.01107/) (2022)
- [Wireless Dataset Similarity: Measuring Distances in Supervised and Unsupervised Machine Learning](https://mlsanity.com/paper/2601.01023/) (2026)
- [A Glimpse of Physical Layer Decision Mechanisms: Facts, Challenges, and Remedies](https://mlsanity.com/paper/2102.07258/) (2021)
- [Zero-Shot Adaptation for mmWave Beam-Tracking on Overhead Messenger Wires through Robust Adversarial Reinforcement Learning](https://mlsanity.com/paper/2102.08055/) (2021)
- [Joint Cluster Head Selection and Trajectory Planning in UAV-Aided IoT Networks by Reinforcement Learning with Sequential Model](https://mlsanity.com/paper/2112.00333/) (2021)
- [Learning RL-Policies for Joint Beamforming Without Exploration: A Batch Constrained Off-Policy Approach](https://mlsanity.com/paper/2310.08660/) (2023)
- [Selective Experience Sharing in Reinforcement Learning Enhances Interference Management](https://mlsanity.com/paper/2501.15735/) (2025)
- [Deep Reinforcement Learning Approach to QoSAware Load Balancing in 5G Cellular Networks under User Mobility and Observation Uncertainty](https://mlsanity.com/paper/2510.24869/) (2025)
- [Plan2Cleanse: Test-Time Backdoor Defense via Monte-Carlo Planning in Deep Reinforcement Learning](https://mlsanity.com/paper/2605.09638/) (2026)
- [Spatio-Temporal Scheduling Prediction Under Backhaul Delay for Resilient Coordinated Beamforming](https://mlsanity.com/paper/2607.08454/) (2026)

## Abstract (arXiv)

The fifth generation of wireless communications (5G) promises massive increases in traffic volume and data rates, as well as improved reliability in voice calls. Jointly optimizing beamforming, power control, and interference coordination in a 5G wireless network to enhance the communication performance to end users poses a significant challenge. In this paper, we formulate the joint design of beamforming, power control, and interference coordination as a non-convex optimization problem to maximize the signal to interference plus noise ratio (SINR) and solve this problem using deep reinforcement learning. By using the greedy nature of deep Q-learning to estimate future rewards of actions and using the reported coordinates of the users served by the network, we propose an algorithm for voice bearers and data bearers in sub-6 GHz and millimeter wave (mmWave) frequency bands, respectively. The algorithm improves the performance measured by SINR and sum-rate capacity. In realistic cellular environments, the simulation results show that our algorithm outperforms the link adaptation industry standards for sub-6 GHz voice bearers. For data bearers in the mmWave frequency band, our algorithm approaches the maximum sum-rate capacity, but with less than 4% of the required run time.