Deep Learning-Driven Channel Assignment and Load Balancing in MANETs: A Review of Optimization Strategies and Performance Enhancements

Abstract:-Mobile Ad Hoc Networks (MANETs) are dynamic, self-configuring wireless networks that lack a centralized infrastructure, making efficient channel assignment and load balancing critical for optimal performance. Traditional methods struggle to adapt to the dynamic topology and interference issues inherent in MANETs. This paper explores the integration of deep learning techniques to enhance channel allocation and load balancing, improving network efficiency, reducing latency, and optimizing resource utilization. Various deep learning models, including reinforcement learning and convolutional neural networks, have been applied to predict network conditions and dynamically allocate channels based on traffic patterns and node mobility. The review highlights key advancements, compares different methodologies, and discusses the challenges and future directions in leveraging deep learning for MANET optimization. The findings suggest that deep learning-based approaches significantly enhance network adaptability, reduce congestion, and improve overall throughput, making them a promising solution for next-generation wireless networks.

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