# Deep Reinforcement Learning for Power Control in Next-Generation WiFi Network Systems

arXiv: https://arxiv.org/abs/2211.01107 · HTML: https://mlsanity.com/paper/2211.01107/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 2 Nov 2022.

## Builds on (most cited first, 13 of 13)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks](https://mlsanity.com/paper/1808.00490/) (2018)
- [Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial](https://mlsanity.com/paper/2011.03615/) (2020)
- [Deep Learning for Wireless Communications](https://mlsanity.com/paper/2005.06068/) (2020)
- [Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks](https://mlsanity.com/paper/1712.00162/) (2017)
- [Wi-Fi Meets ML: A Survey on Improving IEEE 802.11 Performance with Machine Learning](https://mlsanity.com/paper/2109.04786/) (2021)
- [Deep Reinforcement Learning for 5G Networks: Joint Beamforming, Power Control, and Interference Coordination](https://mlsanity.com/paper/1907.00123/) (2019)
- [DeepWiFi: Cognitive WiFi with Deep Learning](https://mlsanity.com/paper/1910.13315/) (2019)
- [How to Attack and Defend NextG Radio Access Network Slicing with Reinforcement Learning](https://mlsanity.com/paper/2101.05768/) (2021)
- [QoS and Jamming-Aware Wireless Networking Using Deep Reinforcement Learning](https://mlsanity.com/paper/1910.05766/) (2019)
- [Reinforcement Learning for Dynamic Resource Optimization in 5G Radio Access Network Slicing](https://mlsanity.com/paper/2009.06579/) (2020)
- [Carrier-Sense Multiple Access for Heterogeneous Wireless Networks Using Deep Reinforcement Learning](https://mlsanity.com/paper/1810.06830/) (2018)
- [Distributed Power Control for Large Energy Harvesting Networks: A Multi-Agent Deep Reinforcement Learning Approach](https://mlsanity.com/paper/1904.00601/) (2019)

## Built on by (most cited first, 1 of 1)

- [Fairness-Utilization Trade-off in Wireless Networks with Explainable Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2411.01924/) (2024)

## Abstract (arXiv)

This paper presents a deep reinforcement learning (DRL) solution for power control in wireless communications, describes its embedded implementation with WiFi transceivers for a WiFi network system, and evaluates the performance with high-fidelity emulation tests. In a multi-hop wireless network, each mobile node measures its link quality and signal strength, and controls its transmit power. As a model-free solution, reinforcement learning allows nodes to adapt their actions by observing the states and maximize their cumulative rewards over time. For each node, the state consists of transmit power, link quality and signal strength; the action adjusts the transmit power; and the reward combines energy efficiency (throughput normalized by energy consumption) and penalty of changing the transmit power. As the state space is large, Q-learning is hard to implement on embedded platforms with limited memory and processing power. By approximating the Q-values with a DQN, DRL is implemented for the embedded platform of each node combining an ARM processor and a WiFi transceiver for 802.11n. Controllable and repeatable emulation tests are performed by inducing realistic channel effects on RF signals. Performance comparison with benchmark schemes of fixed and myopic power allocations shows that power control with DRL provides major improvements to energy efficiency and throughput in WiFi network systems.