Artificial Intelligence for Congestion Detection and Control in Cognitive Radio Networks
Main Article Content
Abstract
Cognitive Radio Networks (CRNs) have emerged as a promising solution to address spectrum scarcity by enabling dynamic spectrum access and intelligent spectrum management. However, the increasing number of secondary users and dynamic spectrum allocation often lead to network congestion, interference, packet loss, and degraded Quality of Service (QoS). Traditional congestion control mechanisms in CRNs are mostly reactive and fail to adapt efficiently to rapidly changing network conditions. To overcome these challenges, this paper proposes an Artificial Intelligence (AI)-based framework for congestion detection and control in Cognitive Radio Networks.
The approach utilizes machine learning techniques to monitor network parameters such as spectrum utilization, channel occupancy, queue length, packet arrival rate, and transmission delay. By analyzing these parameters, the AI model accurately detects congestion levels and predicts potential network overload situations. Based on the predicted congestion state, adaptive spectrum allocation and intelligent routing strategies are implemented to minimize interference and balance traffic load among available channels.
Results demonstrate that the AI-based congestion control framework significantly improves Packet Delivery Ratio (PDR), reduces end-to-end delay, lowers packet loss, and enhances overall spectrum efficiency compared to traditional CRN congestion control techniques. The integration of AI enables proactive decision-making and dynamic resource optimization, making the proposed system highly suitable for next-generation wireless communication environments.
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