Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Sati, Dayal Chandra; Logeshwaran, J.; Dhanasekaran, S.; Sama, Mukhtar; Kandi, Yash; Gupta, Rishi Title A name given to the resource Deep Reinforcement Learning for Dynamic Resource Allocation in Intelligent Communication Networks Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Proceedings - 2025 IEEE 1st International Conference on Smart Innovations in Systems, Infrastructure, Mechanical, Power, AI and Computing Technologies, SISIMPACT 2025;pp.833-838 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/SISIMPACT67725.2025.11439416" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/SISIMPACT67725.2025.11439416</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105037462684?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105037462684?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sati D.C., Apex Institute of Technology, Chandigarh University, Gharun, India; Logeshwaran J., Christ University, Department of Computer Science, Karnataka, Bengaluru, India; Dhanasekaran S., Sri Eshwar College of Engineering, Department of Electronics and Communication Engineering, Tamilnadu, Coimbatore, India; Sama M., Marwadi University, Department of Mechanical Engineering, Gujarat, Rajkot, India; Kandi Y., Manipal University Jaipur, Department of Computer and Communication Engineering, Rajasthan, Jaipur, India; Gupta R., Manipal University Jaipur, Department of Computer Science and Engineering, Rajasthan, Jaipur, India Description An account of the resource As intelligent communication implementations like 5G and IoT-enabled infrastructure meet technological advancement, provisioned network resource allocation dynamically and efficiently will be deemed crucial to accommodate diverse service demands and guarantee an optimized part of the network on demand. Resource management strategies based on traditional approaches often lack a sufficient response to the dynamic posture of network states and complex, heterogeneous environments. To address these challenges, deep reinforcement learning (DRL) has emerged as a powerful methodology wherein deep neural networks are employed to enable intelligent and adaptive decision-making based on dynamic network conditions. In this paper, we study the potential of DRL for dynamic resource management in intelligent communication networks. We build a DRL-driven agent that enables optimal allocation policy learning by interacting with high-dimensional, stochastic network environments with variable traffic loads, user mobility, and heterogeneous quality-of-service (QoS) requirements. Realistic simulation scenarios show that the proposed DRL framework outperforms conventional allocation heuristics in terms of throughput, latency, and fairness among users. We elaborate on the ramifications of explorationexploitation tradeoffs, convergence stability, and compute efficiency in the context of scale deployments. This way, our results prove that DRL is a potential candidate for dynamic resource allocation in future intelligent communication networks due to its better adaptability and performance. 2025 IEEE. Subject The topic of the resource Adaptability; Dimensional; Dynamic; Heuristics; Infrastructure; Intelligent; Reinforcement; Sufficient Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155787-4; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online