📢 Latest Update: Is the UGC CARE List Still Valid in 2026? New Guidelines for Selecting Peer-Reviewed Journals View Now

Journal of Telecommunication, Switching Systems and Networks Cover

Journal of Telecommunication, Switching Systems and Networks

E-ISSN: 2454-6372 | Peer-Reviewed Journal (Refereed Journal) | Hybrid Open Access

About the Journal

Journal of Telecommunication, Switching Systems and Networks Journal of Telecommunication, Switching Systems, and Networks [2454-6372(e)] is a peer-reviewed hybrid open-access journal launched in 2014 focused on the rapid publication of fundamental research papers on all areas of Telecommunication, switching … systems, and Networks.

View full focus & scope More

Journal Information

Title: Journal of Telecommunication, Switching Systems and Networks
Abbreviation: jotssn
Issues Per Year: 3 Issues
E-ISSN: 2454-6372
Publisher: STM Journals, An imprint of Consortium e-Learning Network Pvt. Ltd.
DOI: 10.37591/JoTSSN
Starting Year: 2014
Subject: Telecommunication, Switching Systems and Networks
Publication Format: Hybrid Open Access
Language: English
Copyright Policy: CC BY-NC-ND
Type: Peer-reviewed Journal (Refereed Journal)

Address:

STM Journals, An imprint of Consortium e-Learning Network Pvt. Ltd. A-118, 1st Floor, Sector-63, Noida, U.P. India, Pin - 201301

Editorial Board

View Full Editorial Board

jotssn maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.

Editor in Chief

Editor

Dr. K Chandra Bhushana Rao, Professor

JNTUK-University College of Engineering, Andhra Pradesh, India,

Email :

Latest Articles

Ahead of Print

Artificial Intelligence-Based Optimization of Resource Allocation in 5G and 6G Wireless Networks

The rapid growth of connected devices, heterogeneous applications, immersive services, and intelligent edge systems has increased the complexity of resource management in fifth generation (5G) and emerging sixth generation (6G) wireless networks. Radio spectrum, transmit power, bandwidth, computing resources, network slices, and user association must be dynamically allocated while satisfying conflicting requirements for throughput, latency, reliability, fairness, and energy efficiency.

Artificial Intelligence; Machine Learning; Resource Allocation; Deep Reinforcement Learning; Spectrum Allocation.

AI-Powered Cloud Storage Security: Intelligent Threat Detection, Adaptive Access Control and Resilient Data Protection

Cloud storage has become a basic service for enterprises, educational institutions, digital platforms and electrical/electronic systems that continuously generate data. Its convenience, scalability and remote accessibility also increase exposure to credential theft, ransomware, accidental sharing, malicious insiders, misconfiguration and large-scale data leakage.

Artificial Intelligence, Machine Learning, Cloud Storage, Cloud Security, Zero Trust

AI Agent-Based Security Copilots for Security Operations Centers

Security Operations Centers (SOCs) must interpret large volumes of alerts, logs, endpoint events, identity signals, threat intelligence, and cloud activity while responding quickly to incidents.

AI Agents, Security Copilot, Security Operations Center, Large Language Model, Incident Response

IOT- Based Smart Transportation Management System

Rapid urbanisation has created a situation in which traffic congestion has become one of the major problems affecting many aspects such as, mobility, safety and environmental quality.

Smart Transportation, IoT, Wireless Sensor Networks, Intelligent Traffic Control, Real- Time Monitoring, Data Analytics, Urban Mobility, Sustainable Transportation

Intelligent Network Slicing and Adaptive Switching for Ultra-Low-Latency 6G Communications

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.

6G Networks; Network Slicing; Adaptive Switching; Deep Reinforcement Learning; Ultra-Low Latency; Resource Optimization

Machine Learning-Based Channel Estimation in 5G, Beyond-5G, and 6G Networks: Recent Advances and Future Directions

Accurate channel estimation is one of the most fundamental challenges in modern wireless communication systems.

Channel Estimation, Machine Learning, Deep Learning, 5G NR, OFDM, Convolutional Neural Network, TDL Channel, CDL Channel, Doppler Shift, BER, MMSE, SRCNN, DnCNN, 6G, Non-Terrestrial Networks, Satellite Communication
Support