ItemConference Paper

Deep Reinforcement Learning for Dynamic Resource Allocation in Intelligent Communication Networks

Title
Deep Reinforcement Learning for Dynamic Resource Allocation in Intelligent Communication Networks
Creator
Sati, Dayal Chandra; Logeshwaran, J.; Dhanasekaran, S.; Sama, Mukhtar; Kandi, Yash; Gupta, Rishi
Description
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.
Source
Proceedings - 2025 IEEE 1st International Conference on Smart Innovations in Systems, Infrastructure, Mechanical, Power, AI and Computing Technologies, SISIMPACT 2025;pp.833-838
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Adaptability; Dimensional; Dynamic; Heuristics; Infrastructure; Intelligent; Reinforcement; Sufficient
Coverage
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
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833155787-4;
Format
online
Language
English
Type
Conference paper

Citation

Sati, Dayal Chandra; Logeshwaran, J.; Dhanasekaran, S.; Sama, Mukhtar; Kandi, Yash; Gupta, Rishi, “Deep Reinforcement Learning for Dynamic Resource Allocation in Intelligent Communication Networks,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/26209.

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