Developing self-healing and self-optimizing networks using Deep Reinforcement Learning.

Ganesh Narsinhrao Dhase; Jaideep V Deshmukh; G D Swami; Gajanan K Damkondwar

Developing self-healing and self-optimizing networks using Deep Reinforcement Learning.

Author(s)	Prof. Ganesh Narsinhrao Dhase, Prof. Jaideep V Deshmukh, Prof. G D Swami, Mr. Gajanan K Damkondwar
Country	India
Abstract	Next-generation networks (such as 5G/6G and edge computing) require unprecedented levels of reliability and efficiency, rendering manual or heuristic network management obsolete. To achieve true autonomy, networks must possess self-healing and self-optimizing capabilities. Deep Reinforcement Learning (DRL) offers a powerful framework for this, but standard DRL methods often suffer from slow convergence, scalability issues, and policy instability when handling dual-objective tasks in large-scale topologies. In this paper, we propose [Insert Name of Your Framework], an intelligent, self-configuring networking framework driven by an advanced DRL architecture. Our approach models localized network telemetry as a Markov Decision Process (MDP) to dynamically detect structural disruptions and optimize resource allocation simultaneously. By incorporating [insert key algorithmic novelty, e.g., a dual-attention mechanism / localized reward structuring], the proposed framework accelerates agent convergence and avoids catastrophic policy shifts during sudden link failures. We evaluate our method across various standard network topologies under volatile traffic demands. Simulation results demonstrate that our framework achieves a [X%] reduction in fault recovery latency and a [Y%] improvement in overall throughput compared to state-of-the-art baselines, paving the way for resilient, zero-touch network automation
Keywords	Deep Reinforcement Learning (DRL), self-healing networks, self-optimizing networks, zero-touch network management, Markov Decision Process (MDP), 5G/6G communication networks, network resilience, quality of service (QoS), autonomous traffic engineering.
Field	Engineering
Published In	Volume 8, Issue 4, July-August 2026
Published On	2026-07-04

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CrossRef DOI prefix of IJFMR is 10.36948/ijfmr

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Developing self-healing and self-optimizing networks using Deep Reinforcement Learning.

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