Enhancing Scalability in Deep-Q-Learning for Resource Allocation in 6G URLLC Networks: Hybrid Approaches and Optimization Techniques

6G networks provide ultra-reliable low-latency communication (URLLC) demands that have never been seen before, necessitating efficient and dynamic resource allocation to satisfy tough performance standards. Although Deep-Q-Learning (DQL) has demonstrated potential in improving URLLC’s resource allocation, it has significant impediments when it comes to scaling, especially when network complexity and size rise. For 6G URLLC networks to be more scalable, this study investigates new hybrid methods that mix DQL with conventional optimization strategies. We explore the possibility of combining DQL with techniques like convex optimization, game theory, and heuristic algorithms to address the computational load and slow convergence problems that are common in large-scale deployments. This research suggests a hybrid framework that takes advantage of DQL and classical approaches to create a system that can adapt to different network conditions while still being robust and efficient. Simulations across several 6G network settings show that the suggested method works, and it improves scalability, resource consumption, and system performance by a large margin. For the future of 6G communication, this study lays the groundwork for better, more flexible techniques for allocating resources.