Network-Controlled Repeaters and Deep Q-Learning for Energy-Efficient 6G Routing: A Lifelike Simulation Framework Using GNS3

In the forthcoming 6G era, network infrastructure must achieve unprecedented energy efficiency, adaptability, and ultra-low latency. This paper proposes a novel Deep Q-Learning (DQN)-based routing framework that leverages Network-Controlled Repeaters (NCRs) to dynamically optimize routing paths. Unlike prior studies that remain largely simulation-only, we develop a realistic emulation environment in GNS3 integrated with a custom reinforcement learning testbed compatible with OpenAI Gym. The DQN agent learns to activate optimal NCR configurations based on traffic and channel conditions, thereby minimizing unnecessary energy expenditure. Experimental results show that the proposed approach achieves up to 34% energy savings, 21% faster convergence, and a 7–10% improvement in packet delivery ratio compared to baseline and classical energy-aware protocols. Scalability experiments confirm consistent performance in networks of up to 50 nodes, while preliminary vehicular mobility tests using SUMO demonstrate robustness under dynamic conditions. By bridging reinforcement learning with emulation-driven evaluation, this work offers a scalable, efficient, and deployment-oriented framework for designing future 6G networks