AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
The proposed modified Deep Reinforcement Learning-based intelligent TDD configuration framework for adaptive radio resource allocation in 5G HetNets effectively enhances network reliability, resource utilization, and communication efficiency in dynamic 5G HetNet environments.
The simulation results show that the suggested Hyb-DRL-KkOA algorithm performs better than the conventional bandwidth allocation algorithms, and reduces the blocking probability, makespan, cost, and energy utilization while improving the throughput; therefore, providing enhanced quality of service (QoS).
The wildest boom of wireless devices and the shift to 5G/6G ecosystems contributed to the lack of the spectrum, making the old traditional methods of static allocation less and less efficient. cognitive radio networks provide an alternative with dynamic nature, the current solutions tend to fail because of the sophisti...
A Deep Reinforcement Learning (DRL)-based framework for dynamic spectrum access in 6G heterogeneous Cognitive Radio Networks (Het-CRNs), wherein secondary users learn optimal channel selection policies through direct interaction with the radio environment, without requiring explicit statistical channel models is propos...
A Dual-Stage Multi-Time-Scale Temporal Attention-Based LSTM network (D-MTSTA-LSTM) has been architected, which effectively learns short- and long-term relationships in network trends, thereby precisely predicting optimal communication routes and associated power and spectrum allocation.
A novel orchestration scheme for game-theoretic UARA in HetNets that closely approximates the optimal policy for the considered operational objectives, while delivering higher network throughput than conventional association methods.
A path selection model that combines bottleneck link usage and reinforcement learning that achieves superior state awareness and adaptive routing performance in multi-source heterogeneous networks and hence can be used effectively for intelligent routing in next-generation power communication networks.
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