@inproceedings{83ffb44e30e349f0ae5710d4669316fe,
title = "Power Control in massive MIMO Networks Using Transfer Learning with Deep Neural Networks",
abstract = "Power control (PC) plays a crucial role in massive multiple-input-multiple-output (mMIMO) networks. There are several heuristic algorithms, like the weighted mean square error (WMMSE) algorithm, used to optimise the PC. In order these algorithms to perform the power allocation they require high computational power. In this paper, we address this problem through the application of machine learning (ML)-based algorithms as they can produce close to optimal solutions with a very low computational complexity. We propose the use of transfer learning with deep neural networks (TLDNN) under the objective of maximising the sum spectral efficiency (SE). The evaluation results demonstrate that the TLDNN approach outperforms the deep neural network (DNN) based PC and is twice faster than the WMMSE based PC.",
keywords = "deep neural network, machine learning, massive MIMO, power control, transfer learning",
author = "Neda Ahmadi and Iosif Mporas and Anastasios Papazafeiropoulos and Pandelis Kourtessis and John Senior",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 27th IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2022 ; Conference date: 02-11-2022 Through 03-11-2022",
year = "2022",
doi = "10.1109/CAMAD55695.2022.9966903",
language = "English",
series = "IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
pages = "89--93",
booktitle = "2022 IEEE 27th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2022",
address = "United States",
}