Abstract
Reliable WiFi coverage hinges on the strategic placement and accurate configuration of access points (APs). Traditional, manual AP placements are time consuming, costly, and suboptimal. This disclosure describes techniques that leverage Q-learning to determine optimal access point (AP) placements and configurations in an indoor environment. A reinforcement learning (RL) agent is trained using a digital representation of the indoor environment and a reward function that maximizes network coverage while minimizing the number of APs. The digital representation of the indoor environment includes floorplans and coverage maps. The trained RL agent can be deployed to optimize the placement and configuration of APs in the real-world indoor environment. The techniques can provide increased network capacity and reduced interference, enable an improved user experience, and provide AP placement/ configurations that evolve with the network.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
n/a, "WiFi Access Point Placement and Configuration Using Reinforcement Learning Agents", Technical Disclosure Commons, (May 09, 2025)
https://www.tdcommons.org/dpubs_series/8106