Resource allocation for AI-native healthcare systems in 6G dense networks using deep reinforcement learning^☆

Abstract

Although 6G networks combined with artificial intelligence present revolutionary prospects for healthcare delivery, resource management in dense medical device networks stays a basic issue. Reliable communication directly affects patient outcomes in these settings; nonetheless, current resource allocation techniques struggle with complicated interference patterns and different service needs of AI-native healthcare systems. In dense installations where conventional approaches fail, this paper tackles the challenge of combining network efficiency with medical care priority. Thus, we offer a Dueling Deep Q-Network (DDQN) -based resource allocation approach for AI-native healthcare systems in 6G dense networks. First, we create a point-line graph coloring-based interference model to capture the unique characteristics of medical device communications. Building on this foundation, we suggest a DDQN approach to optimal resource allocation over multiple medical services by combining advantage estimate with healthcare-aware state evaluation. Unlike traditional graph-based models, this one correctly depicts the overlapping coverage areas common in hospital environments. Building on this basis, our DDQN design allows the system to prioritize medical needs while distributing resources by separating healthcare state assessment from advantage estimation. Experimental findings show that the suggested DDQN outperforms state-of-the-art techniques in dense healthcare installations by 14.6% greater network throughput and 13.7% better resource use. The solution shows particularly strong in maintaining service quality under vital conditions with 5.5% greater QoS satisfaction for emergency services and 8.2% quicker recovery from interruptions.

Keywords

Resource allocation / AI-native healthcare systems / 6G dense networks / Deep reinforcement learning

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