Energy Efficient Power Allocation Based on Machine Learning Generated Clusters for Distributed Antenna Systems

In this paper, we consider the combination of machine learning (ML) and wireless communication. We design a machine learning generated clusters model in a distributed antenna system (DAS), which is constructed by two different ML clustering algorithms, i.e., k-means algorithm and Gaussian mixture model-based (GMM) algorithm. Under the communication scenario of DAS with ML generated clusters model, we investigate two different power allocation optimization problems with the interference of maximizing spectral efficiency (SE) and energy efficiency (EE) in DAS, respectively. We compare the SE and EE of DAS with ML generated clusters model and the conventional model. The simulation results verify the effectiveness of DAS with ML generated clusters model, which can obtain the much better performance of SE and EE compared with the conventional communication model in DAS.

Abstract licence: CC BY-NC-ND

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