AI-DRIVEN COGNITIVE COMMUNICATION SYSTEMS FOR 6G: A PYTHON-BASED IMPLEMENTATION AND PERFORMANCE ANALYSIS

Author(s)	Dr. MADHUMITA K, Mr. PRAKHAR SINGH, Mr. JELEN ALBERT J, Mr. LINGESH C A
Country	India
Abstract	The sixth generation (6G) of wireless networks envisions intelligent, ultra-reliable, and self-optimizing systems capable of delivering terabit-per-second throughput, sub-millisecond latency, and pervasive machine intelligence. Integrating Artificial Intelligence (AI) into Cognitive Radio (CR) architectures is a fundamental step toward this goal. This paper presents a Python-based implementation and performance analysis of AI-driven cognitive communication systems for 6G. The proposed model employs supervised learning for spectrum sensing and reinforcement learning for dynamic channel allocation. Analytical and simulation results demonstrate a 95 % detection accuracy at 5 dB SNR, a 35 % reduction in latency, and a 22 % improvement in throughput compared to conventional CR. Additional investigations explore scalability, energy efficiency, and potential deployment scenarios across smart infrastructures. The results establish AI as a core catalyst for the 6G evolution.
Keywords	6G, Artificial Intelligence, Cognitive Radio, Reinforcement Learning, Spectrum Sensing, Python Simulation, Low Latency Networks, Edge Intelligence
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 16, Issue 4, October-December 2025
Published On	2025-11-19
DOI	https://doi.org/10.71097/IJSAT.v16.i4.9470
Short DOI	https://doi.org/hbb8gw

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