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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63616
Volume:
10 |
5 |
May 2026
Pages:
1--
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Master of computer applications
Bapuji institute of engineering and technology,Davanagere
Master of computer applications
Bapuji institute of engineering and technology,Davanagere
Pervasive Evolution of Mobile Architectures: The trajectory of cellular technology remains in a state of perpetual advancement, currently pivoting toward the deployment of 6G Infrastructures. Hyper-Latency and Terahertz Throughput: This forthcoming standard is engineered for Sub-Millisecond Latency and unprecedented data transfer speeds, virtually eliminating traditional network bottlenecks. Cognitive Network Autonomy: Leveraging embedded AI, these networks will transition into Predictive Ecosystems, autonomously anticipating user requirements and optimizing resource allocation through proactive, context-aware. how does it work? The system grabs data from every part of the network, nonstop, and chews through it as it comes in. The integration of advanced machine learning facilitates a transition from reactive maintenance to Autonomous Network Resilience. By scrutinizing high-velocity telemetry for anomalies— ranging from arterial congestion to hardware degradation—the system identifies latent failure modes before they escalate into systemic disruptions Proactive Remediation and Operational Continuity: Real-time diagnostic triggers provide technical teams with Pre- emptive Intelligence, allowing for targeted interventions that preserve service-level integrity. This "Self-Healing" capability ensures that the network remains optimized, significantly reducing mean-time- to-repair (MTTR) and operational overhead. Recursive Intelligence and Predictive Refinement: The primary objective is the development of a Heuristic Management Framework that utilizes deep learning to analyze historical datasets. This allows the system to engage in continuous self-calibration, progressively increasing the accuracy of its predictive models Optimization of the 6G Ecosystem: By embedding intelligence directly into the network’s core, the 6G infrastructure achieves unprecedented levels of Computational Efficiency.
KEYWORDS
This system is more than a small upgrade The GAI- WMRS represents a fundamental evolution in telecommunications, moving beyond passive observation into the domain of Autonomous Prescriptive Orchestration. By integrating high-velocity data synthesis with real-time corrective logic, the architecture transitions from a reactive maintenance model to a state of Proactive Systemic Resilience. I. Autonomous Fault Mitigation and Operational FluidityThe core value of this framework lies in its ability to neutralize anomalies within the Pre-Incident Window. By scrutinizing multi-source telemetry in real time, the system identifies and remediates latent disruptions— such as spectral congestion or hardware degradation— well before they manifest as service outages. This effectively eliminates the "Latency of Intervention" associated with manual oversight, ensuring that the 6G ecosystem remains in a perpetual state of optimization.
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Publication Date
5/20/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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