@Article{cmc.2025.066428,
AUTHOR = {Venkata Satya Suresh kumar Kondeti, Raghavendra Kulkarni, Binu Sudhakaran Pillai, Surendran Rajendran},
TITLE = {Slice-Based 6G Network with Enhanced Manta Ray Deep Reinforcement Learning-Driven Proactive and Robust Resource Management},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {84},
YEAR = {2025},
NUMBER = {3},
PAGES = {4973--4995},
URL = {http://www.techscience.com/cmc/v84n3/63191},
ISSN = {1546-2226},
ABSTRACT = {Next-generation 6G networks seek to provide ultra-reliable and low-latency communications, necessitating network designs that are intelligent and adaptable. Network slicing has developed as an effective option for resource separation and service-level differentiation inside virtualized infrastructures. Nonetheless, sustaining elevated Quality of Service (QoS) in dynamic, resource-limited systems poses significant hurdles. This study introduces an innovative packet-based proactive end-to-end (ETE) resource management system that facilitates network slicing with improved resilience and proactivity. To get around the drawbacks of conventional reactive systems, we develop a cost-efficient slice provisioning architecture that takes into account limits on radio, processing, and transmission resources. The optimization issue is non-convex, NP-hard, and requires online resolution in a dynamic setting. We offer a hybrid solution that integrates an advanced Deep Reinforcement Learning (DRL) methodology with an Improved Manta-Ray Foraging Optimization (ImpMRFO) algorithm. The ImpMRFO utilizes Chebyshev chaotic mapping for the formation of a varied starting population and incorporates Lévy flight-based stochastic movement to avert premature convergence, hence facilitating improved exploration-exploitation trade-offs. The DRL model perpetually acquires optimum provisioning strategies via agent-environment interactions, whereas the ImpMRFO enhances policy performance for effective slice provisioning. The solution, developed in Python, is evaluated across several 6G slicing scenarios that include varied QoS profiles and traffic requirements. The DRL model perpetually acquires optimum provisioning methods via agent-environment interactions, while the ImpMRFO enhances policy performance for effective slice provisioning. The solution, developed in Python, is evaluated across several 6G slicing scenarios that include varied QoS profiles and traffic requirements. Experimental findings reveal that the proactive ETE system outperforms DRL models and non-resilient provisioning techniques. Our technique increases PSSRr, decreases average latency, and optimizes resource use. These results demonstrate that the hybrid architecture for robust, real-time, and scalable slice management in future 6G networks is feasible.},
DOI = {10.32604/cmc.2025.066428}
}