Skip to main content

Research Repository

Advanced Search

DTWN: Q-learning-based Transmit Power Control for Digital Twin WiFi Networks

Çakır, Lal Verda; Huseynov, Khayal; Ak, Elif; Canberk, Berk


Lal Verda Çakır

Khayal Huseynov

Elif Ak


Interference has always been the main threat to the performance of traditional WiFi networks and next-generation moving forward. The problem can be solved with transmit power control(TPC). However, to accomplish this, an information-gathering process is required. But this brings overhead concerns that decrease the throughput. Moreover, mitigation of interference relies on the selection of transmit powers. In other words, the control scheme should select the optimum configuration relative to other possibilities based on the total interference, and this requires an extensive search. Furthermore, bidirectional communication in real-time needs to exist to control the transmit powers based on the current situation. Based on these challenges, we propose a complete solution with Digital Twin WiFi Networks (DTWN). Contrarily to other studies, with the agent programs installed on the APs in the physical layer of this architecture, we enable information-gathering without causing overhead to the wireless medium. Additionally, we employ Q-learning-based TPC in the Brain Layer to find the best configuration given the current situation. Consequently, we accomplish real-time monitoring and management thanks to the digital twin. Then, we evaluate the performance of the proposed approach through total interference and throughput metrics over the increasing number of users. Furthermore, we show that the proposed DTWN model outperforms existing schemes.

Journal Article Type Article
Online Publication Date Jun 8, 2022
Publication Date 2022
Deposit Date Nov 1, 2022
Publicly Available Date Nov 2, 2022
Journal EAI Endorsed Transactions on Industrial Networks and Intelligent Systems
Print ISSN 2410-0218
Publisher EAI: European Alliance for Innovation
Peer Reviewed Peer Reviewed
Volume 9
Issue 31
Article Number e5
Keywords Digital Twin, Reinforcement Learning, Transmit Power Control, WiFi, Interference
Public URL


You might also like

Downloadable Citations