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Ridho Hendra Yoga Perdana(리드호 헨드라 요가 페르다나),Beongku An(안병구) 한국통신학회 2022 한국통신학회 학술대회논문집 Vol.2022 No.2
This paper studies the deep learning-based joint power allocation and phase shift in multiuser multi-intelligent reflecting surface (IRS)-aided massive MIMO systems. The signal-to-noise-plus noise ratio is formulated to determine the spectral efficiency problem. Particularly, we design a deep neural network (DNN) to learn the relation between the position of every user within cell with the optimal power allocation and phase shift policies. The simulation results show that the suggested idea achieves good performance in predict the power allocation and phase shift with accuracy 97% compared to the conventional method while it reduces the computation complexity.
Ridho Hendra Yoga Perdana,Toan-Van Nguyen,Beongku An 한국통신학회 2023 ICT Express Vol.9 No.2
In this paper, we propose a deep learning approach for solving power allocation problems in massive MIMO networks. We use signal-to-interference-plus-noise-ratio (SINR) and signal-to-leak-plus-noise ratio (SLNR) criteria for linear precoder design to define the max–min and max-prod power allocation challenges. The power allocation process to each user equipment in the base station coverage takes a long time and is inefficient, hence numerous base stations are deployed to serve multiple user equipments. As a result, we develop a deep neural network (DNN) framework in which the user’s equipment position is utilized to train the deep model, which is then used to forecast the ideal power distribution depending on the user’s location. Compared to the traditional optimization approach, the DNN design helps to obtain the optimal solution of the power allocation problem within a short time via a quick-inference process. Simulation results show that the SINR criterion outperforms the SLNR one. Meanwhile, deep learning achieves excellent results in forecasting power allocation with an accuracy of 85% for the max–min strategy and 99% for the max-product approach.