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@article{209593,
        author = {Amerenrda Tiwari and Adarsh Mishra and Mrs. Devika Singh},
        title = {6G Network and AI: Integration Enabling Intelligent Adaptive, and Beyond 5g Wireless Systems},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {5},
        pages = {2713-2723},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=209593},
        abstract = {Beyond-5G and 6G Wireless Systems are required to enable services and technologies with extremely high data rates, ultra-low latency, enormous numbers of connections, and significantly less energy than current systems.
Additionally, the broad array of operational environments associated with 6G will pose significant challenges for traditional fixed resource management policies. This paper outlines a Research Plan based upon a doctoral thesis outline that enables us to incorporate Artificial Intelligence (AI) and Machine Learning (ML) into Adaptive Resource Allocation and Network Orchestration. The Research Plan is designed to utilize AI/ML to manage Ultra Dense Heterogeneous Networks (UDHN) employing Terahertz (THz) Communication, Ultra-Massive Multiple Input Multiple Output (UM-MIMO), and Reconfigurable Intelligent Surfaces (RIS). Our Research Plan defines Resource Management as an Optimization Problem involving the simultaneous optimization of multiple objectives including Subcarrier Assignment and Transmission Power, while satisfying constraints related to Delay Limitation, Power Limitation, and Interference Limitation. To resolve this Non-Convex Optimization Problem and the time varying nature of Channel Conditions, we propose a Multi-Agent Deep Reinforcement Learning (MADRL) Architecture consisting of Custom Deep Q-Network (DQN) Modules and Proximal Policy Optimization (PPO) Modules, enabling Flexible Decision Making.
Additionally, we plan to utilize Explainable Artificial Intelligence (XAI) to explain the Logic behind our Resource Management Decisions. We also plan to evaluate the validity of our Methodology through Simulation Studies utilizing MATLAB for Physical Layer Modeling and Channel Modeling, Python based TensorFlow/PyTorch Platforms for AI Model Training, and NS-3 for Network Level Modeling. We also plan to Generate Artificial CSI under Mobile and Blocked Conditions and utilize these Artificial CSI Data Sets to Compare our Approach to Proportional Fair Scheduling, Waterfalling Power Allocation, and several other Deep Learning 5G Baselines. Since this outline describes the Experimental Design but does not report on any Actual Experimental Results, No Performance Metrics have been included in this paper. Instead, this paper outlines the Experimental Designs, Evaluation Metrics, Predicted Results, Limitations, and Criteria for Demonstrating the Feasibility of our Proposed Contributions.},
        keywords = {6G, beyond 5G, Artificial Intelligence, Machine Learning, Multi-Agent Deep Reinforcement Learning, Federated, Learning, Explainable AI, Terahertz Communication, Resource Allocation, Interference Mitigation, RIS, UM-MIMO},
        month = {October},
        }