DYNAMIC SPECTRUM INTELLIGENCE USING REINFORCEMENT LEARNING FOR 6G COGNITIVE NETWORK

Abstract

The evolution toward 6G networks brings the challenge of efficiently utilizing the 
increasingly scarce radio spectrum. Dynamic Spectrum Intelligence (DSI), powered by 
Reinforcement Learning (RL), emerges as a promising solution to achieve adaptive, autonomous, 
and intelligent spectrum management. This research explores the integration of RL algorithms 
within cognitive 6G environments for dynamic spectrum sensing, allocation, and optimization. The 
proposed framework enables network agents to learn optimal policies through continuous 
interaction with the wireless environment, maximizing spectrum efficiency while minimizing 
interference. By leveraging deep reinforcement learning techniques such as Deep Q-Networks and 
Policy Gradient Methods, the system dynamically adjusts to fluctuating traffic demands and 
heterogeneous network conditions. The study also emphasizes the incorporation of context-aware 
intelligence, enabling predictive spectrum access based on user behavior and spatiotemporal 
patterns. Simulation results demonstrate significant improvements in throughput, latency reduction, 
and spectral efficiency when compared to traditional heuristic and static allocation methods. The 
proposed DSI model represents a key step toward achieving fully autonomous, energy-efficient, and 
intelligent spectrum orchestration for next-generation 6G cognitive networks, facilitating seamless 
connectivity, ultra-reliable low-latency communication, and optimized use of radio resources. 

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