Artificial Intelligence in Modern Communication Systems: Enhancing Efficiency and Reliability

Paper ID: EIJTEM_2025_12_2_121-125

Author's Name: Rakhi Thakur, Ajay Sonkesriya

Volume: 12

Issue: 2

Year: 2025

Page No: 121-125

Abstract:

The rapid growth of modern communication systems, driven by technologies such as 5G, Internet of Things (IoT), and cloud computing, has introduced unprecedented challenges in network efficiency, reliability, and scalability. Traditional communication frameworks often struggle to manage dynamic traffic patterns, interference, and resource allocation effectively. Artificial Intelligence (AI) offers transformative solutions by enabling intelligent decision-making, adaptive optimization, and predictive analytics within communication networks. This paper explores the integration of AI techniques including machine learning, deep learning, and reinforcement learning into modern communication systems to enhance performance, reliability, and user experience. Key applications analyzed include network traffic prediction, dynamic resource allocation, signal optimization, and anomaly detection. Experimental results and simulations demonstrate that AI-driven communication systems achieve significant improvements in throughput, latency, and error reduction compared to conventional methods. The study also addresses challenges related to computational complexity, model interpretability, and real-time deployment. Overall, the research highlights the pivotal role of AI in driving efficient, reliable, and adaptive communication networks, paving the way for next-generation technologies and intelligent connectivity solutions.

Keywords: Artificial Intelligence, Communication Systems, Network Optimization, Machine Learning, Deep Learning, Reinforcement Learning, Resource Allocation, Traffic Prediction.

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