An Intelligent Bio-AI for Optimized Resource Allocation in 5G Networks

The rapid expansion of 5G networks necessitates sophisticated resource allocation algorithms to tackle the problems posed by fluctuating traffic conditions, varied device requirements, and rigorous Quality of Service (QoS) standards. This work presents the Bio-AI Allocator, a hybrid model that combines Deep Reinforcement Learning (DRL) with Ant Colony Optimization (ACO) for effective and adaptable resource management. The DRL model is trained via the publicly accessible 5G Quality of Service Dataset from Kaggle, which includes essential parameters like as signal strength, bandwidth utilization, latency, and user mobility patterns. The training utilizes Q-learning with episodic incentives to formulate optimal resource allocation strategies. Experimental findings indicate that the Bio-AI Allocator realizes a 20% decrease in latency, a 25% increase in throughput, and a 15% gain in energy economy relative to traditional approaches such as Round-Robin and Max-Min Fairness scheduling. The comparative analysis demonstrates the superiority of the proposed hybrid model compared to standalone AI and bio-inspired methods, proving the Bio-AI Allocator is a scalable and intelligent solution for next-generation 5G networks.