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Bibhu Prasad Ganthia,
- Assistant Professor, Department of Electrical Engineering, Indira Gandhi Institute of Technology, Sarang, Dhenkanal, Odisha, India
Abstract
The rapid evolution of sixth-generation (6G) wireless communication networks demands intelligent and adaptive network management mechanisms capable of supporting ultra-low-latency, massive connectivity, high reliability, and heterogeneous service requirements. This study proposes an Intelligent Network Slicing and Adaptive Switching (INS-AS) framework for optimizing communication resources and minimizing end-to-end latency in 6G environments. The proposed framework dynamically creates and manages logical network slices according to application-specific requirements, including enhanced mobile broadband, ultra-reliable low-latency communication, massive Internet of Things, and mission-critical services. An artificial intelligence-based decision mechanism is incorporated to continuously monitor traffic load, channel conditions, bandwidth utilization, congestion level, and latency requirements. Based on these parameters, the framework performs adaptive switching among available network slices and communication paths to prevent congestion and maintain service quality. A deep reinforcement learning-based optimization strategy is considered to learn optimal slice-selection, routing, and switching decisions under dynamically changing network conditions. The proposed approach is evaluated through MATLAB/Python-based network simulations using latency, throughput, packet delivery ratio, switching delay, bandwidth utilization, and energy consumption as performance indicators. Comparative analysis with conventional static slicing and traditional switching methods is expected to demonstrate improved latency performance, resource utilization, reliability, and adaptability. The study provides an intelligent architecture for autonomous and scalable 6G networks capable of satisfying diverse real-time communication requirements.
Keywords: 6G Networks; Network Slicing; Adaptive Switching; Deep Reinforcement Learning; Ultra-Low Latency; Resource Optimization
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Journal of Telecommunication, Switching Systems and Networks
| Volume | 13 | |
| 02 | ||
| Received | 18/08/2026 | |
| Accepted | 26/08/2026 | |
| Published | 05/09/2026 | |
| Publication Time | 18 Days |