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This technical report presents a novel framework of artificial intelligence (AI) and machine learning (ML) for wireless access, offering a flexible deep learning strategy to address decentralized optimization tasks in multi-tier networks with a hierarchical structure. Furthermore, it proposes a method to solve optimization problems, such as minimizing key performance indicators (KPIs) of the system, including energy consumption and latency, in a hierarchical network structure, specifically focusing on a mobile edge computing environment, through distributed learning structures. The proposed approach is capable of handling large-scale nodes through a single training process, making it suitable for next-generation communication environments that demand high efficiency and low-latency transmission. Moreover, this technical report describes the training methodology and performance evaluation results of the proposed technique, referring to the AI/ML technology specifications outlined in the 3GPP standards (TR 38.843).
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TTAR-06.0290.pdf
