Revolutionizing Connectivity: Unveiling Next-Gen Efficiency with 6G's Ultra-Reliable Low Latency Communications Resource Allocation

In the imminent era of 6G, this comprehensive review paper navigates the landscape of Efficient Resource Allocation in Ultra-Reliable Low Latency Communications (URLLC) with a particular focus on limitations and advancements in deep learning methodologies, specifically Deep-Q-Learning (DQL). As the demand for unprecedented connectivity experiences intensifies, the study addresses the challenges and opportunities associated with 6G URLLC, emphasizing the critical role of DQL in optimizing resource allocation. The paper critically evaluates the current state of DQL applications in URLLC, providing a nuanced understanding of its limitations in diverse scenarios. It explores the interplay between DQL and other resource allocation techniques, shedding light on synergies and potential areas for improvement. The limitations discussed include issues related to scalability, convergence, and adaptability to dynamic network conditions. Amidst the review of existing methodologies, the paper proposes potential enhancements to DQL, such as hybrid approaches integrating traditional optimization techniques, to overcome its limitations effectively. The study also highlights the importance of benchmarking and standardization in evaluating the performance of DQL-based resource allocation schemes, ensuring a fair comparison across different scenarios.